diff --git a/examples/B1-Fisher-1993.ipynb b/examples/B1-Fisher-1993.ipynb
index aac8dbe..2df476c 100644
--- a/examples/B1-Fisher-1993.ipynb
+++ b/examples/B1-Fisher-1993.ipynb
@@ -7,6 +7,7 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
+    "import polars as pl\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "from pycircstat2 import Circular, load_data"
@@ -36,7 +37,7 @@
    "source": [
     "from pycircstat2.utils import time2float\n",
     "\n",
-    "d_fisher_b1 = np.asarray(load_data(\"B1\", source=\"fisher\")[\"time\"].values)\n",
+    "d_fisher_b1 = np.asarray(load_data(\"B1\", source=\"fisher\")[\"time\"].to_numpy())\n",
     "c_fisher_b1 = Circular(time2float(d_fisher_b1), unit=\"hour\")"
    ]
   },
@@ -93,7 +94,7 @@
     }
    ],
    "source": [
-    "d_fisher_b2 = np.asarray(load_data(\"B2\", source=\"fisher\")[\"θ\"].values)\n",
+    "d_fisher_b2 = np.asarray(load_data(\"B2\", source=\"fisher\")[\"θ\"].to_numpy())\n",
     "d_fisher_b2_double = np.hstack([d_fisher_b2, d_fisher_b2 + 180])\n",
     "# This is an example of an axial data set, which only contains one direction;\n",
     "# To replicate Fig 2.2, you need to double the data;\n",
@@ -135,7 +136,7 @@
     }
    ],
    "source": [
-    "d_fisher_b2 = np.asarray(load_data(\"B2\", source=\"fisher\")[\"θ\"].values)\n",
+    "d_fisher_b2 = np.asarray(load_data(\"B2\", source=\"fisher\")[\"θ\"].to_numpy())\n",
     "d_fisher_b2_double = np.hstack([d_fisher_b2, d_fisher_b2 + 180])\n",
     "# This is an example of an axial data set, which only contains one direction;\n",
     "# To replicate Fig 2.2, you need to double the data;\n",
@@ -166,7 +167,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "d_fisher_b1 = np.asarray(load_data(\"B1\", source=\"fisher\")[\"time\"].values)\n",
+    "d_fisher_b1 = np.asarray(load_data(\"B1\", source=\"fisher\")[\"time\"].to_numpy())\n",
     "c_fisher_b1 = Circular(time2float(d_fisher_b1), unit=\"hour\")"
    ]
   },
@@ -318,7 +319,7 @@
     }
    ],
    "source": [
-    "d_fisher_b5 = np.asarray(load_data(\"B5\", source=\"fisher\")[\"θ\"].values)\n",
+    "d_fisher_b5 = np.asarray(load_data(\"B5\", source=\"fisher\")[\"θ\"].to_numpy())\n",
     "d_fisher_b5_double = np.hstack([d_fisher_b5, d_fisher_b5 + 180])\n",
     "# This is an example of an axial data set, which only contains one direction;\n",
     "# To replicate Fig 2.2, you need to double the data;\n",
@@ -421,7 +422,7 @@
    "outputs": [],
    "source": [
     "d_fisher_b6 = load_data(\"B6\", source=\"fisher\")\n",
-    "d_fisher_b6_s2 = np.asarray(d_fisher_b6[d_fisher_b6[\"set\"] == 2][\"θ\"].values)\n",
+    "d_fisher_b6_s2 = np.asarray(d_fisher_b6.filter(pl.col(\"set\") == 2)[\"θ\"].to_numpy())\n",
     "c_fisher_b6 = Circular(d_fisher_b6_s2, unit=\"degree\")"
    ]
   },
@@ -500,7 +501,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "d_fisher_b5 = np.asarray(load_data(\"B5\", source=\"fisher\")[\"θ\"].values)\n",
+    "d_fisher_b5 = np.asarray(load_data(\"B5\", source=\"fisher\")[\"θ\"].to_numpy())\n",
     "c_fisher_b5 = Circular(d_fisher_b5, unit=\"degree\")"
    ]
   },
@@ -567,6 +568,13 @@
     "### Figure 4.12 and Table 4.1"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 16,
@@ -575,15 +583,15 @@
    "source": [
     "d_fisher_b6 = load_data(\"B6\", \"fisher\")\n",
     "\n",
-    "c_fisher_b6_s0 = Circular(np.sort(d_fisher_b6[d_fisher_b6[\"set\"] == 2][\"θ\"].values[:10]))\n",
+    "c_fisher_b6_s0 = Circular(np.sort(d_fisher_b6.filter(pl.col(\"set\") == 2)[\"θ\"].to_numpy()[:10]))\n",
     "c_fisher_b6_s1 = Circular(\n",
-    "    np.sort(d_fisher_b6[d_fisher_b6.set == 2][\"θ\"].values[:20]),\n",
+    "    np.sort(d_fisher_b6.filter(pl.col(\"set\") == 2)[\"θ\"].to_numpy()[:20]),\n",
     "    kwargs_median={\n",
     "        \"method\": \"deviation\",\n",
     "        \"average_method\": \"unique\",\n",
     "    },\n",
     ")\n",
-    "c_fisher_b6_s2 = Circular(np.sort(d_fisher_b6[d_fisher_b6.set == 2][\"θ\"].values[:]))"
+    "c_fisher_b6_s2 = Circular(np.sort(d_fisher_b6.filter(pl.col(\"set\") == 2)[\"θ\"].to_numpy()[:]))"
    ]
   },
   {
@@ -593,7 +601,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1800x600 with 3 Axes>"
       ]
@@ -648,7 +656,7 @@
       "median                    279.0                     247.5                     245.0               \n",
       "mean                      280.8                     248.7                     247.6               \n",
       "95% median CI             [245. 315.]               [229. 277.]               [229. 267.]         \n",
-      "95% bootstrap mean CI     [254.1 303.6]             [226.  272.3]             [232.6 262.6]       \n",
+      "95% bootstrap mean CI     [253.4 303.1]             [225.1 271.3]             [231.6 262.9]       \n",
       "95% large-sample mean CI  -                         -                         [232.7 262.5]       \n",
       "\n",
       "* The bootstrap CI is a 95% Highest Density Interval (HDI) based on the bootstrap distribution.\n"
@@ -714,7 +722,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "d_fisher_b7 = load_data(\"B7\", \"fisher\")[\"θ\"].values[:]\n",
+    "d_fisher_b7 = load_data(\"B7\", \"fisher\")[\"θ\"].to_numpy()[:]\n",
     "c_fisher_b7 = Circular(d_fisher_b7)"
    ]
   },
@@ -786,7 +794,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: 2026-06-08 17:33:19 CEST\n",
+      "Last updated: 2026-06-09 13:12:57 CEST\n",
       "\n",
       "Python implementation: CPython\n",
       "Python version       : 3.13.11\n",
@@ -794,6 +802,7 @@
       "\n",
       "matplotlib : 3.10.9\n",
       "numpy      : 2.4.4\n",
+      "polars     : 1.40.1\n",
       "pycircstat2: 0.1.16\n",
       "scipy      : 1.17.1\n",
       "\n",
diff --git a/examples/B2-Zar-2010.ipynb b/examples/B2-Zar-2010.ipynb
index 72c14a5..9d10d65 100644
--- a/examples/B2-Zar-2010.ipynb
+++ b/examples/B2-Zar-2010.ipynb
@@ -133,7 +133,7 @@
     }
    ],
    "source": [
-    "d1 = load_data(\"D1\", source=\"zar\")[\"θ\"].values[:]\n",
+    "d1 = load_data(\"D1\", source=\"zar\")[\"θ\"].to_numpy()[:]\n",
     "c1 = Circular(data=d1)\n",
     "\n",
     "# Figure 26.2\n",
@@ -176,7 +176,7 @@
    ],
    "source": [
     "d2 = load_data(\"D2\", source=\"zar\")\n",
-    "c2 = Circular(data=d2[\"θ\"].values[:], w=d2[\"w\"].values[:])\n",
+    "c2 = Circular(data=d2[\"θ\"].to_numpy()[:], w=d2[\"w\"].to_numpy()[:])\n",
     "\n",
     "# figure 26.4\n",
     "c2.plot(\n",
@@ -238,7 +238,7 @@
     "\n",
     "from pycircstat2.utils import data2rad\n",
     "\n",
-    "d1 = load_data(\"D1\", source=\"zar\")[\"θ\"].values[:]\n",
+    "d1 = load_data(\"D1\", source=\"zar\")[\"θ\"].to_numpy()[:]\n",
     "n = len(d1)\n",
     "alpha = data2rad(d1, k=360)\n",
     "\n",
@@ -335,8 +335,8 @@
    ],
    "source": [
     "d2 = load_data(\"D2\", source=\"zar\")\n",
-    "alpha = np.asarray(data2rad(d2[\"θ\"].values[:], k=360))\n",
-    "w = d2[\"w\"].values[:]\n",
+    "alpha = np.asarray(data2rad(d2[\"θ\"].to_numpy()[:], k=360))\n",
+    "w = d2[\"w\"].to_numpy()[:]\n",
     "select = w != 0\n",
     "alpha = alpha[select]\n",
     "w = w[select]\n",
@@ -350,7 +350,7 @@
     "\n",
     "frame = pl.DataFrame(\n",
     "    {\n",
-    "        \"α\": d2[\"θ\"].values[select],\n",
+    "        \"α\": d2[\"θ\"].to_numpy()[select],\n",
     "        \"f\": w,\n",
     "        \"sin(α)\": sina,\n",
     "        \"f * sin(α)\": fsina,\n",
@@ -404,7 +404,7 @@
    "source": [
     "from scipy.stats import chi2\n",
     "\n",
-    "d1 = load_data(\"D1\", source=\"zar\")[\"θ\"].values[:]\n",
+    "d1 = load_data(\"D1\", source=\"zar\")[\"θ\"].to_numpy()[:]\n",
     "n = len(d1)\n",
     "alpha = np.asarray(data2rad(d1, k=360))\n",
     "\n",
@@ -491,7 +491,7 @@
     }
    ],
    "source": [
-    "d3 = load_data(\"D3\", source=\"zar\")[\"θ\"].values[:]\n",
+    "d3 = load_data(\"D3\", source=\"zar\")[\"θ\"].to_numpy()[:]\n",
     "alpha = np.asarray(data2rad(d3, k=360))\n",
     "alpha2 = 2 * alpha % (2 * np.pi)\n",
     "sin2a = np.sin(alpha2)\n",
@@ -569,13 +569,13 @@
       "│ ---    ┆ --- ┆ ---    ┆ ---       ┆ ---       │\n",
       "│ i64    ┆ i64 ┆ f64    ┆ f64       ┆ f64       │\n",
       "╞════════╪═════╪════════╪═══════════╪═══════════╡\n",
-      "│ 1      ┆ 160 ┆ 0.8954 ┆ -0.841401 ┆ 0.306245  │\n",
-      "│ 2      ┆ 169 ┆ 0.7747 ┆ -0.760467 ┆ 0.14782   │\n",
-      "│ 3      ┆ 117 ┆ 0.4696 ┆ -0.213194 ┆ 0.418417  │\n",
-      "│ 4      ┆ 140 ┆ 0.8794 ┆ -0.673659 ┆ 0.565267  │\n",
-      "│ 5      ┆ 186 ┆ 0.3922 ┆ -0.390051 ┆ -0.040996 │\n",
-      "│ 6      ┆ 134 ┆ 0.6952 ┆ -0.482926 ┆ 0.500085  │\n",
-      "│ 7      ┆ 171 ┆ 0.3338 ┆ -0.32969  ┆ 0.052218  │\n",
+      "│ 0      ┆ 160 ┆ 0.8954 ┆ -0.841401 ┆ 0.306245  │\n",
+      "│ 1      ┆ 169 ┆ 0.7747 ┆ -0.760467 ┆ 0.14782   │\n",
+      "│ 2      ┆ 117 ┆ 0.4696 ┆ -0.213194 ┆ 0.418417  │\n",
+      "│ 3      ┆ 140 ┆ 0.8794 ┆ -0.673659 ┆ 0.565267  │\n",
+      "│ 4      ┆ 186 ┆ 0.3922 ┆ -0.390051 ┆ -0.040996 │\n",
+      "│ 5      ┆ 134 ┆ 0.6952 ┆ -0.482926 ┆ 0.500085  │\n",
+      "│ 6      ┆ 171 ┆ 0.3338 ┆ -0.32969  ┆ 0.052218  │\n",
       "└────────┴─────┴────────┴───────────┴───────────┘\n",
       "k = 7; n = 10\n",
       "∑X = -3.69139; ∑Y = 1.94906\n",
@@ -598,16 +598,16 @@
    ],
    "source": [
     "d4 = load_data(\"D4\", source=\"zar\")\n",
-    "ms = np.asarray(data2rad(d4[\"mean\"].values[:], k=360))\n",
-    "rs = d4[\"r\"].values[:]\n",
+    "ms = np.asarray(data2rad(d4[\"mean\"].to_numpy()[:], k=360))\n",
+    "rs = d4[\"r\"].to_numpy()[:]\n",
     "X = rs * np.cos(ms)\n",
     "Y = rs * np.sin(ms)\n",
-    "i = d4.index.values[:]\n",
+    "i = np.arange(len(d4))\n",
     "\n",
     "frame = pl.DataFrame(\n",
     "    {\n",
     "        \"Sample\": i,\n",
-    "        \"μ\": d4[\"mean\"].values,\n",
+    "        \"μ\": d4[\"mean\"].to_numpy(),\n",
     "        \"r\": rs,\n",
     "        \"X\": X,\n",
     "        \"Y\": Y,\n",
@@ -676,7 +676,7 @@
     }
    ],
    "source": [
-    "d1 = load_data(\"D1\", source=\"zar\")[\"θ\"].values[:]\n",
+    "d1 = load_data(\"D1\", source=\"zar\")[\"θ\"].to_numpy()[:]\n",
     "c1 = Circular(data=d1)\n",
     "\n",
     "n = c1.n\n",
@@ -735,7 +735,7 @@
     }
    ],
    "source": [
-    "d7 = load_data(\"D7\", source=\"zar\")[\"θ\"].values[:]\n",
+    "d7 = load_data(\"D7\", source=\"zar\")[\"θ\"].to_numpy()[:]\n",
     "c7 = Circular(data=d7)\n",
     "\n",
     "n = c7.n\n",
@@ -790,7 +790,7 @@
     }
    ],
    "source": [
-    "d7 = load_data(\"D7\", source=\"zar\")[\"θ\"].values[:]\n",
+    "d7 = load_data(\"D7\", source=\"zar\")[\"θ\"].to_numpy()[:]\n",
     "c7 = Circular(data=d7)\n",
     "\n",
     "if c7.mean_lb < np.deg2rad(90) < c7.mean_ub:\n",
@@ -837,7 +837,7 @@
     }
    ],
    "source": [
-    "d8 = load_data(\"D8\", source=\"zar\")[\"θ\"].values[:]\n",
+    "d8 = load_data(\"D8\", source=\"zar\")[\"θ\"].to_numpy()[:]\n",
     "c8 = Circular(data=d8)\n",
     "alpha = c8.alpha\n",
     "n = len(alpha)\n",
@@ -918,7 +918,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x11d56ee90>]"
+       "[<matplotlib.lines.Line2D at 0x119d45450>]"
       ]
      },
      "execution_count": 19,
@@ -937,7 +937,7 @@
     }
    ],
    "source": [
-    "d8 = load_data(\"D8\", source=\"zar\")[\"θ\"].values[:]\n",
+    "d8 = load_data(\"D8\", source=\"zar\")[\"θ\"].to_numpy()[:]\n",
     "c8 = Circular(data=d8)\n",
     "alpha = c8.alpha\n",
     "n = len(alpha)\n",
@@ -987,7 +987,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "d9 = load_data(\"D9\", source=\"zar\")[\"θ\"].values[:]\n",
+    "d9 = load_data(\"D9\", source=\"zar\")[\"θ\"].to_numpy()[:]\n",
     "c9 = Circular(data=d9)\n",
     "\n",
     "med = c9.median\n",
@@ -1033,8 +1033,8 @@
    ],
    "source": [
     "d10 = load_data(\"D10\", source=\"zar\")\n",
-    "s1 = d10[d10[\"sample\"] == 1][\"θ\"].values[:]\n",
-    "s2 = d10[d10[\"sample\"] == 2][\"θ\"].values[:]\n",
+    "s1 = d10.filter(pl.col(\"sample\") == 1)[\"θ\"].to_numpy()[:]\n",
+    "s2 = d10.filter(pl.col(\"sample\") == 2)[\"θ\"].to_numpy()[:]\n",
     "\n",
     "c10_s1 = Circular(s1)\n",
     "c10_s2 = Circular(s2)\n",
@@ -1079,9 +1079,9 @@
    ],
    "source": [
     "d11 = load_data(\"D11\", source=\"zar\")\n",
-    "s1 = d11[d11[\"sample\"] == 1][\"θ\"].values[:]\n",
-    "s2 = d11[d11[\"sample\"] == 2][\"θ\"].values[:]\n",
-    "s3 = d11[d11[\"sample\"] == 3][\"θ\"].values[:]\n",
+    "s1 = d11.filter(pl.col(\"sample\") == 1)[\"θ\"].to_numpy()[:]\n",
+    "s2 = d11.filter(pl.col(\"sample\") == 2)[\"θ\"].to_numpy()[:]\n",
+    "s3 = d11.filter(pl.col(\"sample\") == 3)[\"θ\"].to_numpy()[:]\n",
     "\n",
     "c11_s1 = Circular(s1)\n",
     "c11_s2 = Circular(s2)\n",
@@ -1130,8 +1130,8 @@
     "\n",
     "\n",
     "d12 = load_data(\"D12\", source=\"zar\")\n",
-    "s1 = d12[d12[\"sample\"] == 1][\"θ\"].values[:]\n",
-    "s2 = d12[d12[\"sample\"] == 2][\"θ\"].values[:]\n",
+    "s1 = d12.filter(pl.col(\"sample\") == 1)[\"θ\"].to_numpy()[:]\n",
+    "s2 = d12.filter(pl.col(\"sample\") == 2)[\"θ\"].to_numpy()[:]\n",
     "\n",
     "c12_s1 = Circular(s1)\n",
     "c12_s2 = Circular(s2)\n",
@@ -1163,10 +1163,10 @@
     "\n",
     "\n",
     "d13 = load_data(\"D13\", source=\"zar\")\n",
-    "s1 = d13[d13[\"sample\"] == 1][\"θ\"].values[:]\n",
-    "w1 = d13[d13[\"sample\"] == 1][\"w\"].values[:]\n",
-    "s2 = d13[d13[\"sample\"] == 2][\"θ\"].values[:]\n",
-    "w2 = d13[d13[\"sample\"] == 2][\"w\"].values[:]\n",
+    "s1 = d13.filter(pl.col(\"sample\") == 1)[\"θ\"].to_numpy()[:]\n",
+    "w1 = d13.filter(pl.col(\"sample\") == 1)[\"w\"].to_numpy()[:]\n",
+    "s2 = d13.filter(pl.col(\"sample\") == 2)[\"θ\"].to_numpy()[:]\n",
+    "w2 = d13.filter(pl.col(\"sample\") == 2)[\"w\"].to_numpy()[:]\n",
     "\n",
     "c13_s1 = Circular(data=s1, w=w1)\n",
     "c13_s2 = Circular(data=s2, w=w2)\n",
@@ -1208,8 +1208,8 @@
    ],
    "source": [
     "d12 = load_data(\"D12\", source=\"zar\")\n",
-    "c12_s1 = Circular(data=d12[d12[\"sample\"] == 1][\"θ\"].values[:])\n",
-    "c12_s2 = Circular(data=d12[d12[\"sample\"] == 2][\"θ\"].values[:])\n",
+    "c12_s1 = Circular(data=d12.filter(pl.col(\"sample\") == 1)[\"θ\"].to_numpy()[:])\n",
+    "c12_s2 = Circular(data=d12.filter(pl.col(\"sample\") == 2)[\"θ\"].to_numpy()[:])\n",
     "\n",
     "from pycircstat2.hypothesis import wheeler_watson_test\n",
     "\n",
@@ -1244,8 +1244,8 @@
    ],
    "source": [
     "d14 = load_data(\"D14\", source=\"zar\")\n",
-    "c14_s1 = Circular(data=d14[d14[\"sex\"] == \"male\"][\"θ\"].values[:])\n",
-    "c14_s2 = Circular(data=d14[d14[\"sex\"] == \"female\"][\"θ\"].values[:])\n",
+    "c14_s1 = Circular(data=d14.filter(pl.col(\"sex\") == \"male\")[\"θ\"].to_numpy()[:])\n",
+    "c14_s2 = Circular(data=d14.filter(pl.col(\"sex\") == \"female\")[\"θ\"].to_numpy()[:])\n",
     "\n",
     "from pycircstat2.hypothesis import wallraff_test\n",
     "\n",
@@ -1281,8 +1281,8 @@
    ],
    "source": [
     "d15 = load_data(\"D15\", source=\"zar\")\n",
-    "s1 = time2float(d15[d15[\"sex\"] == \"male\"][\"time\"].values[:])\n",
-    "s2 = time2float(d15[d15[\"sex\"] == \"female\"][\"time\"].values[:])\n",
+    "s1 = time2float(d15.filter(pl.col(\"sex\") == \"male\")[\"time\"].to_numpy()[:])\n",
+    "s2 = time2float(d15.filter(pl.col(\"sex\") == \"female\")[\"time\"].to_numpy()[:])\n",
     "\n",
     "c15_s1 = Circular(data=s1)\n",
     "c15_s2 = Circular(data=s2)\n",
@@ -1323,8 +1323,8 @@
     "\n",
     "d20 = load_data(\"D20\", source=\"zar\")\n",
     "\n",
-    "a = Circular(data=d20[\"Insect\"].values[:])\n",
-    "b = Circular(data=d20[\"Light\"].values[:])\n",
+    "a = Circular(data=d20[\"Insect\"].to_numpy()[:])\n",
+    "b = Circular(data=d20[\"Light\"].to_numpy()[:])\n",
     "\n",
     "from pycircstat2.correlation import circ_corrcc\n",
     "\n",
@@ -1358,8 +1358,8 @@
    "source": [
     "d21 = load_data(\"D21\", source=\"zar\")\n",
     "\n",
-    "a = Circular(data=d21[\"θ\"].values[:]).alpha\n",
-    "x = d21[\"X\"].values[:]\n",
+    "a = Circular(data=d21[\"θ\"].to_numpy()[:]).alpha\n",
+    "x = d21[\"X\"].to_numpy()[:]\n",
     "\n",
     "from pycircstat2.correlation import circ_corrcl\n",
     "\n",
@@ -1396,8 +1396,8 @@
    ],
    "source": [
     "d22 = load_data(\"D22\", source=\"zar\")\n",
-    "a = Circular(data=d22[\"evening\"].values[:])\n",
-    "b = Circular(data=d22[\"morning\"].values[:])\n",
+    "a = Circular(data=d22[\"evening\"].to_numpy()[:])\n",
+    "b = Circular(data=d22[\"morning\"].to_numpy()[:])\n",
     "res = circ_corrcc(a, b, test=True, method=\"nonparametric\")\n",
     "\n",
     "print(f\"r = {res.r:.5f}, reject? = {res.reject_null}\")"
@@ -1430,7 +1430,7 @@
    ],
    "source": [
     "d2 = load_data(\"D2\", source=\"zar\")\n",
-    "c2 = Circular(data=d2[\"θ\"].values[:], w=d2[\"w\"].values[:])\n",
+    "c2 = Circular(data=d2[\"θ\"].to_numpy()[:], w=d2[\"w\"].to_numpy()[:])\n",
     "\n",
     "from pycircstat2.hypothesis import chisquare_test\n",
     "\n",
@@ -1462,7 +1462,7 @@
     }
    ],
    "source": [
-    "d1 = load_data(\"D1\", source=\"zar\")[\"θ\"].values[:]\n",
+    "d1 = load_data(\"D1\", source=\"zar\")[\"θ\"].to_numpy()[:]\n",
     "c1 = Circular(data=d1)\n",
     "\n",
     "from pycircstat2.hypothesis import watson_test\n",
@@ -1480,7 +1480,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: 2026-06-08 17:36:42 CEST\n",
+      "Last updated: 2026-06-09 13:17:14 CEST\n",
       "\n",
       "Python implementation: CPython\n",
       "Python version       : 3.13.11\n",
diff --git a/examples/B3-Pewsey-2014.ipynb b/examples/B3-Pewsey-2014.ipynb
index 4602209..f15788a 100644
--- a/examples/B3-Pewsey-2014.ipynb
+++ b/examples/B3-Pewsey-2014.ipynb
@@ -2,11 +2,12 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
+    "import polars as pl\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "from pycircstat2 import Circular, load_data"
@@ -53,7 +54,7 @@
     }
    ],
    "source": [
-    "d21 = load_data('wind', source='pewsey')['x'].values[:]\n",
+    "d21 = load_data('wind', source='pewsey')['x'].to_numpy()[:]\n",
     "c21 = Circular(data=d21, unit='radian')\n",
     "\n",
     "fig, ax = plt.subplot_mosaic(mosaic=\"ab\", figsize=(12, 6), per_subplot_kw={'b': {'projection': 'polar'}})\n",
@@ -109,9 +110,9 @@
    "source": [
     "d22 = load_data('B10', source='fisher')\n",
     "\n",
-    "c22s1 = Circular(data=d22[d22['set'] == 1]['θ'], unit='degree', )\n",
-    "c22s2 = Circular(data=d22[d22['set'] == 2]['θ'], unit='degree')\n",
-    "c22s3 = Circular(data=d22[d22['set'] == 3]['θ'], unit='degree')\n",
+    "c22s1 = Circular(data=d22.filter(pl.col(\"set\") == 1)['θ'], unit='degree', )\n",
+    "c22s2 = Circular(data=d22.filter(pl.col(\"set\") == 2)['θ'], unit='degree')\n",
+    "c22s3 = Circular(data=d22.filter(pl.col(\"set\") == 3)['θ'], unit='degree')\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(5, 5), subplot_kw={'projection': 'polar'})\n",
     "\n",
@@ -232,7 +233,7 @@
     }
    ],
    "source": [
-    "d21 = load_data('wind', source='pewsey')['x'].values[:]\n",
+    "d21 = load_data('wind', source='pewsey')['x'].to_numpy()[:]\n",
     "c21 = Circular(data=d21, unit='radian')\n",
     "\n",
     "fig, ax = plt.subplot_mosaic(mosaic=\"ab\", figsize=(12, 6), subplot_kw={'projection': 'polar'})\n",
@@ -315,7 +316,7 @@
     }
    ],
    "source": [
-    "b11 = load_data('B11', source='fisher')[\"θ\"].values[:]\n",
+    "b11 = load_data('B11', source='fisher')[\"θ\"].to_numpy()[:]\n",
     "c11_1 = Circular(data=b11, unit='degree', kwargs_mean_ci={\"method\": \"approximate\"})\n",
     "c11_2 = Circular(data=b11*2, unit='degree', kwargs_mean_ci={\"method\": \"approximate\"})\n",
     "c11_3 = Circular(data=b11*3, unit='degree', kwargs_mean_ci={\"method\": \"approximate\"})\n",
@@ -791,7 +792,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x11998a490>"
+       "<matplotlib.legend.Legend at 0x1186c4cd0>"
       ]
      },
      "execution_count": 18,
@@ -1125,7 +1126,7 @@
    ],
    "source": [
     "from pycircstat2.utils import time2float\n",
-    "d1 = time2float(load_data(\"B1\", 'fisher')['time'].values[:])\n",
+    "d1 = time2float(load_data(\"B1\", 'fisher')['time'].to_numpy()[:])\n",
     "c1 = Circular(data=d1, unit='hour')\n",
     "c1.plot(\n",
     "    config={\n",
@@ -1210,7 +1211,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: 2026-06-08 17:37:11 CEST\n",
+      "Last updated: 2026-06-09 13:18:33 CEST\n",
       "\n",
       "Python implementation: CPython\n",
       "Python version       : 3.13.11\n",
diff --git a/examples/T0-utils.ipynb b/examples/T0-utils.ipynb
index 6b7dc40..1f43214 100644
--- a/examples/T0-utils.ipynb
+++ b/examples/T0-utils.ipynb
@@ -73,49 +73,26 @@
     {
      "data": {
       "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
        "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>11:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>17:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>23:15</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+       "<small>shape: (3, 1)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>time</th></tr><tr><td>str</td></tr></thead><tbody><tr><td>&quot;11:00&quot;</td></tr><tr><td>&quot;17:00&quot;</td></tr><tr><td>&quot;23:15&quot;</td></tr></tbody></table></div>"
       ],
       "text/plain": [
-       "    time\n",
-       "1  11:00\n",
-       "2  17:00\n",
-       "3  23:15"
+       "shape: (3, 1)\n",
+       "┌───────┐\n",
+       "│ time  │\n",
+       "│ ---   │\n",
+       "│ str   │\n",
+       "╞═══════╡\n",
+       "│ 11:00 │\n",
+       "│ 17:00 │\n",
+       "│ 23:15 │\n",
+       "└───────┘"
       ]
      },
      "execution_count": 3,
@@ -155,7 +132,7 @@
    ],
    "source": [
     "from pycircstat2.utils import time2float\n",
-    "data_float = time2float(data['time'].values)\n",
+    "data_float = time2float(data['time'].to_numpy())\n",
     "data_float[:5]"
    ]
   },
@@ -280,7 +257,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: 2026-06-08 17:37:24 CEST\n",
+      "Last updated: 2026-06-09 13:19:00 CEST\n",
       "\n",
       "Python implementation: CPython\n",
       "Python version       : 3.13.11\n",
diff --git a/examples/T1-descriptive-statistics.ipynb b/examples/T1-descriptive-statistics.ipynb
index 4631e2c..b1c3147 100644
--- a/examples/T1-descriptive-statistics.ipynb
+++ b/examples/T1-descriptive-statistics.ipynb
@@ -7,6 +7,7 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
+    "import polars as pl\n",
     "\n",
     "from pycircstat2 import Circular, load_data"
    ]
@@ -62,7 +63,7 @@
    "source": [
     "from pycircstat2.descriptive import circ_r, circ_mean, circ_mean_and_r\n",
     "\n",
-    "b11 = load_data(\"B11\", source=\"fisher\")[\"θ\"].values[:]\n",
+    "b11 = load_data(\"B11\", source=\"fisher\")[\"θ\"].to_numpy()[:]\n",
     "c11 = Circular(data=b11)\n",
     "\n",
     "u, r = circ_mean_and_r(alpha=c11.alpha)\n",
@@ -306,14 +307,14 @@
    "source": [
     "from pycircstat2.descriptive import angular_std, circ_std\n",
     "\n",
-    "d1 = load_data('D1', source='zar')['θ'].values[:]\n",
+    "d1 = load_data('D1', source='zar')['θ'].to_numpy()[:]\n",
     "c1 = Circular(data=d1)\n",
     "s = angular_std(alpha=c1.alpha)\n",
     "s0 = circ_std(alpha=c1.alpha)\n",
     "print(f\"s={np.round(np.rad2deg(s))}, s0={np.round(np.rad2deg(s0))}\")\n",
     "\n",
     "d2 = load_data('D2', source='zar')\n",
-    "c2 = Circular(data=d2['θ'].values[:], w=d2['w'].values[:])\n",
+    "c2 = Circular(data=d2['θ'].to_numpy()[:], w=d2['w'].to_numpy()[:])\n",
     "s = angular_std(alpha=c2.alpha, w=c2.w)\n",
     "s0 = circ_std(alpha=c2.alpha, w=c2.w)\n",
     "print(f\"s={np.round(np.rad2deg(s))}, s0={np.round(np.rad2deg(s0))}\")"
@@ -393,7 +394,7 @@
     "from pycircstat2.descriptive import circ_mean_ci\n",
     "\n",
     "data_zar_ex4_ch26 = load_data(\"D1\", source=\"zar\")\n",
-    "circ_zar_ex4_ch26 = Circular(data=data_zar_ex4_ch26[\"θ\"].values[:])\n",
+    "circ_zar_ex4_ch26 = Circular(data=data_zar_ex4_ch26[\"θ\"].to_numpy()[:])\n",
     "\n",
     "## Approximate: Example 26.6, Zar, 2010\n",
     "lb, ub = circ_mean_ci(\n",
@@ -420,7 +421,7 @@
       "median                    279.0                     247.5                     245.0               \n",
       "mean                      280.8                     248.7                     247.6               \n",
       "95% median CI             [245. 315.]               [229. 277.]               [229. 267.]         \n",
-      "95% bootstrap mean CI     [254.6 301.8]             [225.8 272. ]             [233.9 262.2]       \n",
+      "95% bootstrap mean CI     [249.6 303. ]             [226.8 273.3]             [232.8 264.1]       \n",
       "95% large-sample mean CI  -                         -                         [232.7 262.5]       \n"
      ]
     }
@@ -429,9 +430,9 @@
     "from pycircstat2.descriptive import circ_median_ci\n",
     "\n",
     "d_ex3 = load_data('B6','fisher')\n",
-    "d_ex3_10 = np.sort(d_ex3[d_ex3.set==2]['θ'].values[:10])\n",
-    "d_ex3_20 = np.sort(d_ex3[d_ex3.set==2]['θ'].values[:20])\n",
-    "d_ex3_30 = np.sort(d_ex3[d_ex3.set==2]['θ'].values[:])\n",
+    "d_ex3_10 = np.sort(d_ex3.filter(pl.col(\"set\") == 2)['θ'].to_numpy()[:10])\n",
+    "d_ex3_20 = np.sort(d_ex3.filter(pl.col(\"set\") == 2)['θ'].to_numpy()[:20])\n",
+    "d_ex3_30 = np.sort(d_ex3.filter(pl.col(\"set\") == 2)['θ'].to_numpy()[:])\n",
     "\n",
     "table = {'median': [], 'mean': [], '95% median CI': [], '95% bootstrap mean CI': [], '95% large-sample mean CI': []}\n",
     "for i, d in enumerate([d_ex3_10, d_ex3_20, d_ex3_30]):\n",
@@ -481,13 +482,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: 2026-06-08 17:37:34 CEST\n",
+      "Last updated: 2026-06-09 13:19:52 CEST\n",
       "\n",
       "Python implementation: CPython\n",
       "Python version       : 3.13.11\n",
       "IPython version      : 9.13.0\n",
       "\n",
       "numpy      : 2.4.4\n",
+      "polars     : 1.40.1\n",
       "pycircstat2: 0.1.16\n",
       "\n",
       "Watermark: 2.6.0\n",
diff --git a/examples/T2-hypothesis-testing.ipynb b/examples/T2-hypothesis-testing.ipynb
index 9b0cb57..666b461 100644
--- a/examples/T2-hypothesis-testing.ipynb
+++ b/examples/T2-hypothesis-testing.ipynb
@@ -7,6 +7,7 @@
    "outputs": [],
    "source": [
     "import numpy as np \n",
+    "import polars as pl\n",
     "\n",
     "from pycircstat2 import Circular, load_data"
    ]
@@ -92,7 +93,7 @@
    "source": [
     "from pycircstat2.hypothesis import rayleigh_test\n",
     "\n",
-    "d1 = load_data('D1', source='zar')['θ'].values[:]\n",
+    "d1 = load_data('D1', source='zar')['θ'].to_numpy()\n",
     "c1 = Circular(data=d1)\n",
     "\n",
     "rayleigh_test(c1.alpha, n_resamples=9999, verbose=True)"
@@ -141,7 +142,7 @@
     "from pycircstat2.hypothesis import V_test\n",
     "\n",
     "\n",
-    "d7 = load_data('D7', source='zar')['θ'].values[:]\n",
+    "d7 = load_data('D7', source='zar')['θ'].to_numpy()\n",
     "c7 = Circular(data=d7)\n",
     "\n",
     "result = V_test(angle=np.deg2rad(90), alpha=c7.alpha, verbose=True)\n",
@@ -192,7 +193,7 @@
     "from pycircstat2.hypothesis import omnibus_test\n",
     "\n",
     "\n",
-    "d8 = load_data('D8', source='zar')['θ'].values[:]\n",
+    "d8 = load_data('D8', source='zar')['θ'].to_numpy()\n",
     "c8 = Circular(data=d8)\n",
     "\n",
     "result = omnibus_test(c8.alpha, verbose=True)\n",
@@ -291,7 +292,7 @@
     "\n",
     "\n",
     "d2 = load_data(\"D2\", source=\"zar\")\n",
-    "c2 = Circular(data=d2[\"θ\"].values[:], w=d2[\"w\"].values[:])\n",
+    "c2 = Circular(data=d2[\"θ\"].to_numpy(), w=d2[\"w\"].to_numpy())\n",
     "\n",
     "chisquare_test(c2.w, verbose=True)"
    ]
@@ -493,7 +494,7 @@
     "from pycircstat2.hypothesis import symmetry_test\n",
     "\n",
     "\n",
-    "d9 = load_data('D9', source='zar')['θ'].values[:]\n",
+    "d9 = load_data('D9', source='zar')['θ'].to_numpy()\n",
     "c9 = Circular(data=d9)\n",
     "\n",
     "result = symmetry_test(alpha=c9.alpha, verbose=True)\n",
@@ -574,7 +575,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -596,7 +597,7 @@
        "WatsonWilliamsTestResult(F=1.864868424860857, pval=0.18706369950318863, df_between=2, df_within=16, k=3, N=19)"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -606,9 +607,9 @@
     "\n",
     "\n",
     "data = load_data(\"D11\", source=\"zar\")\n",
-    "s1 = Circular(data=data[data[\"sample\"] == 1][\"θ\"].values[:])\n",
-    "s2 = Circular(data=data[data[\"sample\"] == 2][\"θ\"].values[:])\n",
-    "s3 = Circular(data=data[data[\"sample\"] == 3][\"θ\"].values[:])\n",
+    "s1 = Circular(data=data.filter(pl.col(\"sample\") == 1)[\"θ\"].to_numpy())\n",
+    "s2 = Circular(data=data.filter(pl.col(\"sample\") == 2)[\"θ\"].to_numpy())\n",
+    "s3 = Circular(data=data.filter(pl.col(\"sample\") == 3)[\"θ\"].to_numpy())\n",
     "\n",
     "result = watson_williams_test([s1, s2, s3], verbose=True)\n",
     "result\n"
@@ -626,7 +627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -635,7 +636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -657,7 +658,7 @@
        "WatsonU2TestResult(U2=0.14574314574314576, pval=0.11261025234391597, method='asymptotic', n_resamples=0)"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -665,8 +666,8 @@
    "source": [
     "# without ties\n",
     "d = load_data(\"D12\", source=\"zar\")\n",
-    "c0 = Circular(data=d[d[\"sample\"] == 1][\"θ\"].values[:])\n",
-    "c1 = Circular(data=d[d[\"sample\"] == 2][\"θ\"].values[:])\n",
+    "c0 = Circular(data=d.filter(pl.col(\"sample\") == 1)[\"θ\"].to_numpy())\n",
+    "c1 = Circular(data=d.filter(pl.col(\"sample\") == 2)[\"θ\"].to_numpy())\n",
     "\n",
     "result = watson_u2_test([c0, c1], verbose=True)\n",
     "result\n"
@@ -674,7 +675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -696,7 +697,7 @@
        "WatsonU2TestResult(U2=0.06123215627347858, pval=0.5971567816257365, method='asymptotic', n_resamples=0)"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -704,8 +705,8 @@
    "source": [
     "# with ties\n",
     "d = load_data(\"D13\", source=\"zar\")\n",
-    "c0 = Circular(data=d[d[\"sample\"] == 1][\"θ\"].values[:], w=d[d[\"sample\"] == 1][\"w\"].values[:])\n",
-    "c1 = Circular(data=d[d[\"sample\"] == 2][\"θ\"].values[:], w=d[d[\"sample\"] == 2][\"w\"].values[:])\n",
+    "c0 = Circular(data=d.filter(pl.col(\"sample\") == 1)[\"θ\"].to_numpy(), w=d.filter(pl.col(\"sample\") == 1)[\"w\"].to_numpy())\n",
+    "c1 = Circular(data=d.filter(pl.col(\"sample\") == 2)[\"θ\"].to_numpy(), w=d.filter(pl.col(\"sample\") == 2)[\"w\"].to_numpy())\n",
     "\n",
     "result = watson_u2_test([c0, c1], verbose=True)\n",
     "result\n"
@@ -723,7 +724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -745,7 +746,7 @@
        "WheelerWatsonTestResult(W=3.6782700358857188, pval=0.15895485976111798, df=2, method='asymptotic', n_resamples=0)"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -754,8 +755,8 @@
     "from pycircstat2.hypothesis import wheeler_watson_test\n",
     "\n",
     "d = load_data(\"D12\", source=\"zar\")\n",
-    "c0 = Circular(data=d[d[\"sample\"] == 1][\"θ\"].values[:])\n",
-    "c1 = Circular(data=d[d[\"sample\"] == 2][\"θ\"].values[:])\n",
+    "c0 = Circular(data=d.filter(pl.col(\"sample\") == 1)[\"θ\"].to_numpy())\n",
+    "c1 = Circular(data=d.filter(pl.col(\"sample\") == 2)[\"θ\"].to_numpy())\n",
     "\n",
     "result = wheeler_watson_test([c0, c1], verbose=True)\n",
     "result\n"
@@ -771,7 +772,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -793,7 +794,7 @@
        "WallraffTestResult(U=18.5, pval=0.7750969621959847)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -802,8 +803,8 @@
     "from pycircstat2.hypothesis import wallraff_test\n",
     "\n",
     "d = load_data(\"D14\", source=\"zar\")\n",
-    "c0 = Circular(data=d[d[\"sex\"] == \"male\"][\"θ\"].values[:])\n",
-    "c1 = Circular(data=d[d[\"sex\"] == \"female\"][\"θ\"].values[:])\n",
+    "c0 = Circular(data=d.filter(pl.col(\"sex\") == \"male\")[\"θ\"].to_numpy())\n",
+    "c1 = Circular(data=d.filter(pl.col(\"sex\") == \"female\")[\"θ\"].to_numpy())\n",
     "\n",
     "result = wallraff_test(samples=[c0, c1], angle=np.deg2rad(135), verbose=True)\n",
     "result\n"
@@ -811,7 +812,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -833,7 +834,7 @@
        "WallraffTestResult(U=13.0, pval=0.17524424540000616)"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -843,8 +844,8 @@
     "\n",
     "\n",
     "d = load_data(\"D15\", source=\"zar\")\n",
-    "c0 = Circular(data=time2float(d[d[\"sex\"] == \"male\"][\"time\"].values[:]))\n",
-    "c1 = Circular(data=time2float(d[d[\"sex\"] == \"female\"][\"time\"].values[:]))\n",
+    "c0 = Circular(data=time2float(d.filter(pl.col(\"sex\") == \"male\")[\"time\"].to_numpy()))\n",
+    "c1 = Circular(data=time2float(d.filter(pl.col(\"sex\") == \"female\")[\"time\"].to_numpy()))\n",
     "\n",
     "result = wallraff_test(\n",
     "    samples=[c0, c1], angle=np.deg2rad(time2float(['7:55', '8:15'])), verbose=True\n",
@@ -861,7 +862,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -900,16 +901,16 @@
     "from pycircstat2.hypothesis import angular_randomisation_test\n",
     "\n",
     "d = load_data(\"D13\", source=\"zar\")\n",
-    "c0 = Circular(data=d[d[\"sample\"] == 1][\"θ\"].values[:])\n",
-    "c1 = Circular(data=d[d[\"sample\"] == 2][\"θ\"].values[:])\n",
+    "c0 = Circular(data=d.filter(pl.col(\"sample\") == 1)[\"θ\"].to_numpy())\n",
+    "c1 = Circular(data=d.filter(pl.col(\"sample\") == 1)[\"θ\"].to_numpy())\n",
     "\n",
-    "result = angular_randomisation_test([c0, c1], n_simulation=1000, verbose=True)\n",
+    "result = angular_randomisation_test([c0, c1], n_resamples=1000, verbose=True)\n",
     "result\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
diff --git a/examples/T3-circular-distributions.ipynb b/examples/T3-circular-distributions.ipynb
index 3eb32fe..d102d5b 100644
--- a/examples/T3-circular-distributions.ipynb
+++ b/examples/T3-circular-distributions.ipynb
@@ -73,7 +73,7 @@
     },
     {
      "data": {
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       "text/plain": [
        "<Figure size 1600x500 with 3 Axes>"
       ]
@@ -165,7 +165,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1600x500 with 3 Axes>"
       ]
@@ -278,7 +278,7 @@
     },
     {
      "data": {
-      "image/png": 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27dvdm2++6W677Tb33HPPBe9LZ4YQQgghhBBCCCGEEEKkhEQKERWwoL1o0SL39NNPuzJlyriCBQvaQrgHIsQDDzzgxo8f7/bv3++mT5/uOnXqZDZOIrxg/9ShQwc3ZcoU+1tgFdW2bVv7m9FN4cHvS5cubRkh8+bNk2ghhBBCCCGEEEIIIYQ4AWVSiIhCWPP7779vdk10RXiQcfDzzz8Hf86WLZvZPYnoggDyevXq2SVx5wTWXNh0cXnxxRddsWLFXPPmze1SuHDhiO2zEEIIIYQQQgghhBAielAmhYgY//zzj7vooousGt+zFqpfv77ZBmEllDt3bv11YpjffvvNTZs2zU2cONF98cUX7s8//wz+rkqVKtYVkzNnzojuoxAiY5A3tBBCCBEbxMM5Ox5egxBCCJFZ+COV5211Uoiw8N9//7lvv/3WqusHDhxonRJnnnmma9KkiVXat2jRwt1xxx1xN8j8+++/TYxJC2eccYbLkiWLi3UQIJo2bWqXw4cPmyjx7rvvulmzZlneyAUXXBC87/r1692ll15qr10IIYQQQgghhBBCCJF5UCeF8BVClrFy4rJt2za7be7cua5y5cp2/d9//42JhWmsjLCfIqSbDoEDBw7YJbnr3s9HjhxJ1/boKmGRn4V8LqHXE//M9QIFCrj8+fO7008/PSYsvnbs2BH8DBw9etTly5fPnXfeee6+++4zwYqAbiFE7KCKRiGEECI2iIdzdjy8BiGEECKz8Ic6KUSkoHOAqnmCrxEkPBhA3nXXXS5XrlzB26JJoPjrr79MSNmyZYuJK6GXrVu32mJ6uGBbu3btsktqOfvss12RIkUs2Jr8h9ALGRD8PhogYJuLx7p160xcQQDq16+fXa6//noL56bT5qyzzoro/goRa9C1Rg7M0qVLTVydMGGCa9iwYYqP+eabb1znzp2ts43vZ/fu3V3Lli3Dts9CCCGEEEIIIYTIvMjuSWQ4M2fOtMVlYPH5lltusep4FsnoEIg0VNyweMdl48aNQSGCCv/E4c+hIKjQreB1MaSm0yFHjhxptm7CIurgwYPJdmckvs7/iBnHjh2zBX8uicFei24LT7S47LLLXPny5e0SarsUCcqVK2cLqV999ZXZQU2ZMsUtXLjQLt26dXOjRo2yz5AQInVgr1amTBnXqlUr16hRo5PeHxG2bt26rm3btu7DDz+0Y3ibNm2sw6lWrVp624UQQgghhBBCCOErsnsSpwwLzGvXrnXVq1cP5k9UrVrVLg8++KAt7EcK7JaWLVvmlixZ4hYvXmwXhInkOPfcc5PsROC2QoUKRW1VP90rWCghtiTVCRIaWp2Y4sWLu2uuucZde+219j+iQfbs2V2k+PXXX93IkSOtE4fr5FUgqgAiEoKLECJ18H05WSdF165dLS9o9erVwdvIkkEsnTp1aqq2I9sFIYQQImNgvMuF8f327dstty0jiYdzdjy8BiGEECKa+Ov/ucv4Yb8uuyfhOwgTL730kvvggw8sT4BF8mzZsln3BNYh4V5M5gu1atUqEyI8UQLrEkSTxGB/xIJ8qVKlEogRefLkiclFcELIEVK4JIZJzr59+xKIFrwvvEcIGps3b7bLJ598Yvfn73fFFVckEC6oyg6XXVTevHnd008/7bp06WK2NZ5AAc2bN7f/+V3ZsmXDsj9CxDsLFixwNWrUSHAbHRSdOnVK8XjLJXTQIYQQQsQLjJ8p8qFgZv/+/da5zP81a9a0+QLMmDHDvfPOO+7QoUNm1Rp6OX78uBs2bJirVq2a3ffjjz92DzzwgOXxeRdPjPB+T4EAkOXnWS5ecsklNnZnrC+EEEII4Qesm9avX9/GNs8995x75plnXCTQaEekCQbSCBCDBg1ykydPDt5++eWXu19++SW4SB6OhX72ZeXKle7rr7+2C/kXTAgSc9FFF9liu3fB4ujCCy90mQX+FrxeLhUqVEjwOyZciBWeqMP/ZEMgYnDBfgnoIKlYsaItXHK5+uqrfQ/pzpo1awKbJzp2mMAxqUMYY1H1iSeesPvEorAkRLTAsRtxMBR+RnhgoSUpmz6yY5599tkw7qUQQgiRsd3WFOtQMVi5cuWg/Slj3z59+ti4k/skZvr06UFhn8djk5gcoQI+41fsGFOa19DR+Nprr5nwETpW37RpkxUQCSGEEEL4QceOHd20adPsOuOOSCGRQqQaMgLat29vWQ7AwjB+51S1s4AdDvbu3WuTA0QJvkAsroVCFoTXAeB1AZDFIJKG94uKMC4eTMpChQsudGLQ1cAFRRXBA3EAwYLHIgT5Df74fAbp3vn0009N4eVy5ZVXuscff9zdfffdUWvHJUS8QV4MQduhCzEEbgshhBDRBlavzBuwEN2wYYNdyKLzYDzp2dZisUTnggdd4rlz57Yxc65cuexnj0qVKtm4FJtUBP3QCx3IFHF5NGjQwDqXybjzLhT8ULlIFtTw4cOtEMyDbg2eg7E3lrNCCCGEEH4RurZau3ZtFykkUohUg+8nAgUD7/vvv9899thjlmfgJ3RGYEXidUv88MMPCX7PRIHsC2+hHe80VdWfuhhQr149u3iVXUzWmNzxN5g1a5aJRR999JFdADsor8uCajS6IPyALhi2+cILL7hXX33VjRgxwiy+CGZnnxDMhBBpA5ERO4tQ+BmP56S6KIDvuF/fcyGEECI9IJgvWrTIff/9965x48ZBkYDF/0ceeeSE+9M9gQWsZ7kEt956q/vuu+/s3MglpZy20qVL2yU1YI3LJXRfR40aZRlsdGSEFoBRzch4mnlQuOxWhRBCCJG5+Ouvv2xOT2HyuHHjgrdffPHFEdsniRQiWRAHGOgzUAYG+rQ0IwZQUeQXDNo///xzN378eKssShz6HK4FceGCEybEKC7t2rWzCROTP084QrhasWKFXQYOHGjC0c033+zuuOMOm2jlyJEjw99KJpSDBw92PXv2tMozPpeEtHvgDRw6ERRCJA+dcKH2fUDHWrg65IQQQoj0sHv3bjd79mwTIZi3kJfnCQ50IngixXXXXeduu+02V7JkSSto4nb+pzMiqWIdLn5B4Q/CBAIF41WvEIzsCfZ5zJgx1mUBEiiEEEII4Qdz5sxxzZo1s3VXb82X/Cs6Sv0cB52M0wKhpSNCOGct0AQX82FlkEw2gR/p7okVvClTpliV/FdffeWOHTsW/F0krIVExlhwZcmSxSrS7rnnHpscJleVfapwGPM6aLh+/fXXm5A2YMAAs4MSIjOBsIulBJAf8/LLL1vHGVYVLIJg1bRr1y4L5oStW7daJSh2fq1atbJuqUcffdRNmjTJjrupFZdZZPn999+tA0MIIYTwE85VnkVTKEWKFLEMtjZt2gRDqyMNY1MWA1555RX35ZdfBoUUciY433KupsCHQiBEl5tuusnX/YmHc3Y8vAYhhBAiUjRs2NB98cUX5lZC4fG5554bzM7i/1B7y3CetyVSiARZBAShvv322xbuhk8qtk6Ex/mhpLENKp8QJj777DP7sHogipAxgOVQ2bJlfQ9pFhkbZj5x4kT3ySefWACgB50Nt99+uwkWTCpRaf1g2bJlVjGHAoxw0bx5c/fcc8/Z4qwQmQGOq4gSicEWjTDOli1bWlBoqPc117HwowqV9s4ePXrY/VKLFguEEEL4wfbt221cSRETi/cUUgFdCBSkUIzCOe+GG24wcSJv3rxR84egCIvxMN2/dBx71KlTx3Xq1MmKsLwimwkTJli2GkU9fhMP5+x4eA1CCCFEpDh06JDr3bu3rcdSaPzkk09aN+fRo0dPsILOCCRSiDRV3VJxTrXtkSNH7Lb69eu7fv36WVt0RsIiNn5nH3/8sQ3aQ6vuCbhGmGARG2FC2RKxD3kR/K058DHJDO2Oueuuu+xvjaVMRv+tN23aZAHfY8eOtZ+xBKNSjQpy/IeFEBmLFguEEEJkFDt27HCffvqpTZaXLFkSvJ2Og9B8umi199y3b59766233BtvvBGc61CRSLEA41HspnhdCCoFCxYM+/7Fwzk7Hl6DEEIIES6OHz/uZsyYYU4nHhR+sO5brFgxc9Bh3SzUpSQjkUghUs3+/fvtQ8kgjwokcgVuvPHGDJ9s0KHBYjVerB7Yj9x55522WE0FVCx3TPBlRvChQyQ0h4EuEar6saryFsixVyHvgw6V0JZu7FV4fKVKlYK5H//9958dJGJZtOG9wSuYvz+TTiyiPAoVKmTi1AMPPOCKFi2aodvlPUYRpsUeeP/nz58f9CgWQmQMWiwQQgiREeNFJs9Tp04N3sbcgDkCHQZcsEiKZstcuibefffdoHUtRViEdjPOZd4D2KRis8D8a+7cuWFfZI+Hc3Y8vAYhhBAiHBw7dsy6OHFPYD2ONVjWZSlKR7zA9olC9Wg4b8fuirA4JbDk8SA0jgE1i+ks4GaUQMFE49tvv3WNGze2xWdso/giUEnEojRt21hMUWnEQn00ChQIBHi379y5M8HtBMzQoh3aCYLPLF82wqVD4eemTZsm6CRg0Zzb+vfvn+C+2K00aNDALFc88K7FGimxNzxdAUx4QtvHEUMQOaINBBaEl9dff93eT/JHsGHKnj27vS+8DwRz89pRdzMqKgfbJ7x9EX/w3Men+LLLLsuQ5xZCCCGEEKc2zp43b16C8SJFOvxfpUoVN3ToUBtrM25+4oknolKgYMzKpB+LWopghg0bZosBeDx/+OGHVpjUtWvXoEABjEWZM+TPnz+i+y6EEEKI+Ofss892V111lXWfeh2orD0iUFBMzRgmWoi+VWHhe9cEPuNlypSxhVsPsicaNWqUIdX6eJiNGjXKWrKZYCB+sHCOXyyD9T179lhFPdVQBCtHywSDlnJsr1D4PF588UXzZ8c6KBQW0rmEdgR43ROhjwe+9Lx2FuQ9CP/mNg4UodDJQuhzqJ/uwYMHbRKXWMTBu5buFH7vQXg14dSJVVAW6r///vtgEE4kwW+3du3aVmXGZwElFwGGvwGCDOIPggLiVUbsL59pqvKWL19uwpj3PvI57dixoy9+e0IIIYQQInk7JOwFKB6hSyK04IZ8vJ9++skW/tu2bWsWodEIhUHMHa699lob05ObwZiTMTiiCva2dIoz7k0MXcR0UDAuVReAEEIIIfzm5ZdfNstM1uImT55sYxCKoV999VUbv1BUjhX7+++/H9k/RkBkCv7777/Ahx9+GLjwwgspUQ+cdtppgT59+mToNrZv3x546qmnArly5bJtcDnnnHMCDz74YGDVqlWBaGH16tWB3r17B4YNG5bg9jx58tg+L126NHjbJ598EjjjjDMCjRs3TnDf9957L/D+++8H9u/fH7zt6NGjgcOHD9t7nZH89ddfgV27dgV++umnBLePHj068Oyzzwb27NkTvG3IkCH2Gho2bJjgvsWLF7fbZ8+eHbxt27ZtgfHjxwd27NgRiAbWr18f6NChQyB79uzBz0+OHDkCnTt3Dvz4448Zvj0+q2zjggsuCIwcOTLD/25CZCZ+//13+z7xvxBCCJEUK1asCLRq1SqQNWvWBGO9sWPHxswb9ueffwZee+21QJEiRYKv4eyzzw60bds2sGHDhiQfc+jQoUCTJk0CCxcuDEQD8XDOjofXIIQQQvjFuHHjAg8//PAJ61zHjh0Lrg8+/vjjwdvvvPNOu+2VV16J6HlbIkUmYOvWrYHatWsHB9KlSpUKzJ8/P0Oemw/8nDlzAnfccYct5nvbKFSoUODFF19MsIgfCViEf/LJJ21B3gOxhn2sWLFigvs+9NBDgXvvvTewZs2aBALB33//HYgV/vnnHxOLQhf1+RvVqFEjULBgwQTvw+uvv27vw6233prgOSZNmhRYu3atPVckOHjwoB0YvQOnJ6rVr18/MH369AwTE3744YdAuXLlgtuoWrVqYOPGjRny3EJkNrRYIIQQIjkoiGGc5Y25uDAGe/fddwNHjhyJiTdu7969gV69egVy5swZfA25c+e2wid+lxIU3HjzI+YWkSYeztnx8BqEEEIIP9i2bVvgzDPPtPPkRx99lOB3/fr1s9svuuiiBOfQypUr2+2ffvqpL/skkUIYo0aNCmTLls0+bFmyZLHuiYwYHLNwTzdBmTJlEkw4qlWrFpgwYULYF7jZH6qT2HYo119/ve3Xxx9/HLxt8+bNgRYtWgTeeuutQGaGToyyZctaN4bH8ePH7XPCe8b7FHpACbdY8++//5pgUqtWrQSfsZIlSwZGjBhh+3qq8JoGDRpkHT88N5V9L7zwQoY8txCZCS0WCCGESA66jfPly2cFTXfddVdg7ty5MdPBSvFPx44dg/MpLkWLFg288cYb1kGdGv7444/ALbfckmFFYqdKPJyz4+E1CCGEEH7x5ptvBtq3b59gHQ93lnPPPdfOn6znhsLYhtsZo0XyvH0a/0TWcEr4CR7/hBETTD18+HBXokSJU3o+siXIEMAvdsOGDXYbGQj33Xefe+SRRyxLIBzwsf3rr78sAAbwdSXwG99aAva83IFBgwZZWDc5HGQ9iJQhyPyOO+6wMGvCwr338cknn7SMCHzqyHEIN3zWCN1+55133J9//mm34WPcs2dPd++995qX3qlAqCG+x9OmTbOfH330UfPmE0KkDrJ4zj//fPf777/LX1sIITIxjNHJoyOHbty4ce6MM84I5qMVK1bMXXLJJS4WWL9+vevfv7+9DvInoFy5cu6pp56yHD/vdSXHjh07ova1xsM5Ox5egxBCCJFRHDp0yP39998uZ86cyd6HrKyPP/7YVapUydZQvUxixm7ZsmVzx44ds/XTokWLRuy8reDsOIMwYILaPAhvI0yZicGpCBQENzPRIHCbDzaLxnz4X3jhBQu3GzZsWNgEisGDB1vwNP97XHfdda5AgQL2ZQsNrn788cfd0KFDJVCkknz58rn58+fb3zQ0qHvJkiV20MudO3cCQeOuu+5yI0aMcH7DZ3fIkCG2Xy+99JLLkyePCQsEvpcqVcqC2BHQ0guCx9SpUy0kiOtdunTJ0P0XQgghhIh3ZsyYYWPyO++8033++ec2d/AgXDpaF+1DWbZsme1/yZIl3bvvvmsCRbVq1ayQhfEwvzuZQMHYuHjx4lbYJZJn165dVmyUK1cuK3q78sor7T0WQgghROphnYyi7YYNG1oxd1J8++23JlAgTLC25gkUcPDgQRMovDXBSCKRIo5YtGiRu+qqq1ytWrWsm8CjZs2aCRac0wKKGt0YVA4xKF+zZo3LkSOHVdSzSNytW7cUlbpTgW3zmtiG94WBs846y+3Zs8d99913wduyZMlilf9MiNg/cWqEHrC8SecPP/zg6tatG7xt1qxZbuzYsSZQhUIXBqKWH6C8du7c2W3ZssUNHDjQJjUbN250zZo1s88++5PebfOamSjxfKGT6KeffjrBZ00IIYQQQvz/MF6vXr26u+WWW2yROXv27NbtypwkVvj+++9tnMucB3GFeQjd6AsXLnQzZ86015Z4fJwcq1evtmrG6dOn+77fscqBAwdc5cqVbV43ZcoUt3btWitEuuCCCyK9a0IIIURMcfDgQVufZS1r27ZtJ/yeMUn79u3t+oMPPmhjnVAoQAbOwRQNRBLZPcUBDKLpKujatatV+9BRQJv1qdgb8ZxUljPB8CpazjvvPPfYY4/ZJRxCAIvNLBZTZYNQUq9ePbsdAYYvH68va9asvu+HSBq6aagQy5s3rx3ovL/ZxRdfbJM4Ks7ocvATujtee+01s/XiwAx0+2BHRhdRaieTyUEHEtVziHw8J4LZyarnhMiMyHZBCCEyH4cPHza7zA8++CBYNPTwww9bgQddr7HAvHnz3HPPPRe0/GTM16RJExvzUdmfHujuHTNmjLv77rtPeSwar+dsbLN471NbCERlaGh1KK+hYMGCsnsSQggh3P91SrB+Wrhw4RPej5dfftncQijyZR2P/0PBjQeXFArQly5dGtGxh0SKGGffvn2WtzBp0iT7mTyBt99+O90iAuIEFfI9evRwCxYssNvOPfdc8+j3PtR+gHKH7+umTZvc5MmTg7c/88wz5olG9Tzt4yK64e93zTXX2HW6XTwRac6cOTZhIxvlVPMjkoIDHUIdF8/uq3z58jbprFOnTroniORftGvXzmyggCpBJuLYjQkhomvBQwghRHihOIVqeLoQmjdvbuOuQoUKxcxkngIU5j1AEQqvAXHi0ksvTdNz7d27140aNcoy3KJRlIjGczZ2WnTaYFHBPIEiO8bcDzzwQJL37927t/29EqNxhxBCiMzIiBEjXJUqVdxll12W4v0o+r788sttbYu14tatW7uoHnv4EtstwsK3334bKFCggCWkZ82a1dLb//vvv3Q/3+rVqwPVqlWz5+Ny9tlnB7p06RL49ddfAxnNv//+G9i3b1/w5/379wfOPPNM2+6GDRsyfHsifBw7diywbNmyBLfdfPPN9rd99dVXfd02n6Onn346cO655wY/x5UrVw4sXbr0lJ73nXfeCWTLls2eL0+ePIFp06Zl2D4LEQ/8/vvv9v3gfyGEEPHL5MmTA3/88Ufw5xUrVgQWLVoUiBXmzZsXqF69enCceNZZZwUeeOCBwJYtW9I97r3sssvsufr37x+IBaLhnM3clUu3bt0CP/zwQ2DYsGE292TMndz7zP56l507d0b8NQghhBCR4I033rBzYNGiRQMHDx5M8b533XWX3bdixYq2DhvtYw9lUsQwhAWjiqGc4ZdKe3V6qndQtOiSwCaHaiJatemcwPcfG52MbtcmyJt9Zn89aCvq16+f++qrr2KmAkskDd0TZcuWTVBlx9+bLhy8fT3IuOjbt6/bvXt3hr2VfI6ef/558+N74oknzE+PVvJrr73WPPjwv00PLVq0MNsz2v7pEKHyi8+rEEIIIURmgK5nrABuvfVW98ILLwRvJxOMcVYs5GbQXUvnBxkTZCE89NBD1gU8fPhwV6RIkXSPe7HC5fFYjYrUwfwAT2w+S1dffbVZx9JF8dZbbyX7PlN5GXoRQgghMiONGzd2RYsWda1atUrxfEg2FhbtWFm++eab6c4qDifRv4ciWfAVww4JzzAEhvRYO3344YeuRIkS9lzY8dx+++3mUfbqq69mWKo7g9DQ4OvcuXObhdPcuXNtmx6PP/64u+2225QzEWdwICRc+9dff00gQL3++utmK0ZbfUZz4YUXWrA2E0/8gPkMclBGLBk5cmS6wrWvuOIKEwPxXua7c7K2OiGEEEKIWIcxE5YCjIPGjh1rtkixlM+1atUqK5Ihy468PWxHWQxnjMiCeHqLo8gB9GBsuHLlSnuPROpgnonlUyi8fzt27NBbKIQQQqQw7qCQfMWKFbYenFyhOjlOXlh2hw4dEhQSJ4bfV6hQIRgjEEkkUsQQM2bMcPfee29wYZ8qcSrRs2fPnq4B+80332zPRxA13qsM3MePH59k0Ep6QbVjMZe8CQ+yAsaNG2eTg1ia5IhTI/Hfunbt2u6GG24Ihm4Dn8WM7K7A35aOIwKwCfEmw6VNmzauUqVK6QoE4js3dOhQeyz5Lx6//fZbhuyvEEIIIUS0QFER8wXGangIM4YnXJGxWrSzefNm16xZMyvk+vLLL61ohs5YirHonEivOEGhCq+fnLLQIOf0zMcyM3S08LcIZePGjeqoF0IIIRKxbt06W8/ycrRSM+7AFYc1V/JUyQxLieXLl1tBbmhxeaSQSBEjiln37t1dzZo1rfOBgXV6YYLRqVMna6slMC5btmzWZotogYVNRlRbhXZH8DMTHCqvGNR7sMBLILfIvGAZ8N1339kkxeOdd96x7gp+l5EwwV62bJl76aWX3HnnnWcHYKwJqHzbv39/mp+P9nQPBBUqvwh3P378eIbutxBCCCFEJJg4caIt8DNWY8w+ePBgGz8xh4h2WyrGd4REUqjC/INx5Zo1a2yciT3CqUDQ84svvmjzqAkTJmTYfmc2sMgicJ15KIISfyvmuF7VpxBCCCH+j1deecWE/K5duyZYV00OrPu9ghLWwAisTgmvSDij3HROBYkUUQ4V2ogT+OzzYcQ7tWXLlml+Hh77/vvvm7UTVk4ICQgFKHLY7eDzeaowuGRCMGbMmOBt2EeNGjXKfGDTk5chMheow3RX0O3gwWd19OjR7ujRo6f03HgPIyRQtUVlHd8JbKj4TmBjkB4LKG8ST04Fk/cqVaqYrZUQQgghRCxD1gRdsBR6rF692oqcorkDmow9Cl2KFy9u4zvGj+RnkIHG3IQ5SkZQsGBB99lnn1lnbdOmTTPkOTMjFAsh8nz88ceudOnSrk+fPrYIwxhdCCGEEP8/rOF27NjRTZky5aTrqqxzkf9LV0TVqlXN/vxk9/dEivz587tIcxrp2ZHeCZE0VJXUrVvXFDOqv99+++10VZhTOURFERkQgP0SeQC33HJLhr71vXr1sjYibHz48giRXjgseQdfLMOaNGli3QpMkjMq7IcKOKq1eE5vsoQ3cWiXRGoh8B0LAYK5sUvDyy+xz64Q8QyLQ1Ro0K2nMEshhIhNyFVAnPBYu3atLe5Hc9AiXayIEsxBsPUEfJXJJrvxxhszZBtbt251f//9d9zkkcXDOTseXoMQQgiR3HrYjBkz0rVmi/sOtv4UojOuO9nYhcL4XLly2XWEjYwoYD+V83b0jjgzOfPnz7cBNgLFJZdc4ubNm5dmgYLKcAKxWXRFoMDaqV+/fvZBPVWBgsVYKl4QQDweffRR2x7WTkKcCqHqMFV7+Abz+Q+dJB86dOiUtnHTTTdZdR0dEBwk8VgmVJGupdBQotRQr149a1mnem/btm2uYsWKbvr06ae0f0IIIYQQ4YBuVbq1sXcKLTSi4CJaBQom8J9//rl14TIHQaBgIk6XA/OojBIoCKZkTlanTh23d+/eDHlOIYQQQojkxjfNmzc3R520Wv1jZU7nK9BdmpriCq+LImfOnL4JFGkhOkedwqxpjhw54q655hrzf73yyivT9K5s377dAt26dOliFUZ0ZKxfv9499dRTGfLBe+SRR1zPnj1N9PBAfcNfVMFxIiPBlozAnyeffDJ4Gxkq+OVh33QqzWB8zziIYwHVuHHjYP4LE1s6mdICJwCECh6LSsxkVh1FQgghhIhm6Ja47rrrbCJMkQhjrGhn6dKlZkOFrSzjtTx58pj9Et2xjRo1ylCLWcabZHLQ1a7sMSGEEEL4yWmnnWbFrxTrptVm8/HHH7eiDWwUn3jiiVQ9JpqsnkAiRZSC9QyV2HPmzLE09tTCgu17771nrdrffPONDaqZdGBHg4fqqbQ505bjweIw26hfv366n1OItIgJdAJ5fPLJJ+7w4cNu165dGTIR5TuGrRS5LXRVIDZQTYh9QFpEEIQ6vre01yEsZlQVnxBCCCFERkPmF3MOFvfz5s3rpk2blqAoJNpgIo29JkVc2HaeffbZ7plnnjGhAmtbxosZDQIIlgsEiBcoUCDDn18IIYQQIpSePXua60fr1q1T/cbMmjXLvfPOO7Y+xhpwlixZUvU4CnWLFClil2hAmRRRwl9//WXdCQ8++KANvNMDihmt2uPHj7efsZxh0bVYsWKn/AV54YUXzOv16aefTjI3QIhIePRdfPHFllXhWZChFtPWhj1UetmxY4eF08+ePdt+JnSRPBgq6dKyf4h6OXLkCP5M9V00tM8J4QfyhhZCiNiad3To0MHGN4ANLHMGhIpoBI9kQpX79u1rRSpAQQjzk1Mpwkpusk4nOoVYdKXHI/Fwzo6H1yCEEEIANpWjRo2yItm0dk94tp0UkVO0Qe4qGcTRhjIpYgh8w/AbGzFihLUtMxBPK5MnT7aWHgSKM88803z1qTA6VYECCMz7999/E+RPgAQKESn47DGh9gQKeOONN9zIkSNtUnkqFlBkwCCAkK+CqMB3i64IPI7Tsn+eQAEDBgywropffvkl3fslhBBCCJERMLZBoCBvgoX/qVOnRqVAwXjuiy++sNyJbt26mUBBPsSiRYtMVMlogQJee+01u9x5553u4MGDGf78QgghhBAeiO0UxrKW9eqrr6brjWEsh0BBxycFHLGMOikiDF775EXwP1UghE4jWKSWP//803zHUNy8gLsPPvjAXX311enaH8QIWoSKFi3qqlatGgzgXr58uQVwCxGtLFu2zCrfCE9s2LBhcHLLZxrhLj0gzN1333323ECAERNXKrdSCx0e5FXQ6YQAMmnSJBMUhYgnVNEohBCxBYv+jPXTMu8IJ8yN6DL/+uuvg17JFH3cc889voZ5UyzWoEED604n3yIeiYdzdjy8BiGEEMKzM+fy4YcfmmV/WsAWimwx1r0oWqfwPZbP2xIpIsjcuXNtEPzbb7+ZPc3EiRPTtHiJbz6tzj/++KP9TGg1qhn+rOmFIGwsnagcZ2E2Pa1GQkQKr4PC6/Kh+q5r167upZdeMjEwPWDT9Oyzz7r+/fubYIfQQO5LlSpVUv0cqNqo454YOW7cOOsEESJe0GKBEEJEf/5EvXr1XO7cuV00c+TIEZvPvPjiizYGw1OZIhTmJ9mzZ/dlmz///HMCW894t7SNh3N2PLwGIYQQmZO///7bzl+hY7L0jD2OHz9ucQGrVq1yTZo0MaEjrVCQQeYXtpp0q/qF7J6inJkzZ1rlEgIFqtfChQtTLVDw4R06dKi76aabTKCg1Znnw54mPQJFqDUOVUN0Udx///22ICtELMFBPfTAPmjQILdhwwa3YMGCdD8nk2Ps0whM5LtBZkW1atVM+EitrVTx4sVtH/jOcnC+7bbbTJQUQgghhPATMhboSGjVqpVr2rSpVdpFK59//rlZeTLuYuJdq1YtC/VGtPBLoJgyZYp1vBIy6RHPAoUQQgghIoe3HkTRKs44pzL2eP75502guPDCC92QIUPStT90YrAeHS2kzwNFnDJYxhBuUqdOHauqzpYtW6pbkAlCIVQF8EtlUB3qf59a8McnZBgxAv8zyJkzp9u4caM6KFIJ793evXutAov/Dx06ZBcONqH/cyGkkPt7Fxa4aVfnQscK+QdMwM477zy7eNe9/znwUOXF/+pwSR0IASjCdBl5/PTTT/bep9XHuFKlSm7FihUWNPnuu++azdqSJUvM0zk1LXm5cuVy06ZNc82aNbN8CxRrvvtkaAghhBBC+DERRphgIZ7JLwVSflolpRcKQBBSvvzyS/uZrlXGb9h3+i0YLF682MbrjM3atGkTle+PEEIIIeIDCtVxrSFna+XKlbbOlB6WL18ezJ8gn5V1wrTCuhhdFJ6tZjQgu6cItjIPHDjQ/GBZnE4NLK6ysMlgmgE09jMslKZ38I5aRjsPj6cjo0iRIul6nniGFiwsehBusOzhC8wFUYLLr7/+ahVqSVXfJxYc6HJBXPCECfAEC6raEDESCxtUkSWG5yDcEMHCu3BAoVqfSrBLL73UXXDBBWF5f2IR2uC++uorE+buvvvuND/e62Tq2LGj/e2vuuoqN2HCBOuySG1rHzZtn376qYmVTMqFiHVkuyCEENHF9u3brVKPToRzzjnHfI6jzaeYcRSVfxRNMVknQ4y5Tffu3dPsyZxeGNdR/EUGGeP3zEA8nLPj4TUIIYTInLAWy5infPny6Xr833//7a699loror3jjjus+DU9kJvqiRusR/o5DlImRRSyfv16V6JEiXSJCt9++611TezZs8cWoMeMGZMuT3s+eKGiSJ8+fVz16tXTrd7FA0xOEIBoc+JvhCDBBXECEcIjT548rkCBAkFRILFIwO89USKjvtyIFIgWfKG9jg0uoUIJF/Y/dF850CBWIFpwufzyyy34nMq0zNzCTicS1gHz5s2zLoiyZcum+7mwf2rcuHHwO4n/X2rDJ5mUEwSZ3pwMIaINLRYIIUT0sHTpUhtjMDZknEqHAp7F0baPDzzwgFUTQuXKld2wYcNcqVKlfN0unexvvvmm69SpU6btTI6Hc3Y8vAYhhBCZx+GD8Vh6RYnEPPfcc65Xr17m1rFmzRorYk4PWEVRdEs2BuuNfiKRIspgcsCC5pNPPmnCQGoXillAp3UHu5r0VG2HLnbTuUH1OIvxmbXSnvdz586dNjEKvXhfSAQGhCRvcd+7sOAf7QNgOi/o9vBEFu9CJgMDeOAgxoEx9EJoe2YSLvgM8DcPnazTnURYfFozXRCHUK4XLVpk3TG02/EdT+v7eeDAATd//nyJFiJm0WKBEEJEB3TnMl9Yu3atK1OmjE2ML774YhctIBL07t3bcsPoJsaylpBsMjP8tlpiDEiu2DfffGMdG2w3MxIP5+x4eA1CCCHiny+++MIccRASKJQ9VVul5cuXWxcF68Mff/yx2XqmFwpna9eubeNGujKi4bytTIowgKhw11132YeI6nwG5Kmp3GEQ//DDD5v/PWBNM2LEiHS1P7PNDz74wG3bts299957ZlWTWSZqfNmYjHAhvJiWJuAgwSJ927Zt7X86DZjExeqCPQLL1VdfbZfEEzK6LRCnPFGGz1S/fv2COSTXX3+9u/nmm13VqlXt8bSexSv8fUMFCrpS6Eri8zB9+nTrNkktfF7mzJkTzIl56qmn7P3lemoDHhGX6MDg78PfBSsoIYQQQoj0wByDtn+EAHKzGB9GC4zF6Z6gqAaYWJM9kd4KwPSMARn3UzlYr169sGxTCCGEEJkX1tlKlixpVvvpyY1I7AzCehFry1h4YmV+KkRbHgUok8Jnxo4da+ICi+X8j0CQmgVgAuRQ21jwpKqISh+6KdKygM4HGGsn7zFY3OBPy37E6kJ8WkUJbLJQ6qiQp42ci9dBwBcxXt+H1Aane6IFn425c+daVgqT2RtvvNEOplziXbSgg4EuJwQHRKz0tP4jBGFR8Oijj5o/YOnSpU2cJCckNQLiQw89ZAsJfB5Hjx7tWrRokc5XI0RkUEWjEEJEFgqh6PyN1nMEnaaMlYAxOPle9evXD9v8IHR8d/DgQevgyKzEwzk7Hl6DEEKI+CTxuMM7V53q+uNjjz0WLO6g4OJURQ+KZLGOwhIdK0w/kd1TFIBHPSoXH1DC2Fh8TM0CKIvFCBRYEGHPQ/4EuRFpYdq0abbwibjBAmw8g1XOlClTLAx56tSpNvHwRAlvoZ12qNQGlGdWWFyn/cwTeDzRgoMpnQZMJG+99Vbzq4s3+Mz89ttvQRs1hAMELj47aQGxh+8bAhCTX8KxU5Mdw/boxnjrrbfsxEXHVOvWrdP9eoQIN1osEEKIyEChRLdu3dzLL79s4+G0zhn8ZubMmWblRAEWMD8ZMGCALTCHg/fff9+9/vrrbsaMGVHVVRJJ4uGcHQ+vQQghRPzBWlCDBg3MZYNuh4xixowZwbUlrDxjLd801eftgPCFDz74IHD66acHeItbtmwZ+Oeff1L1uC+++CJw9tln2+PKli0b2Lp1a7q237NnT3uO66+/PvDff/8F4o1NmzYFXn755cDNN98cOOOMM+y1li9fPtCrV6/Ad999Fzh27FikdzHmOX78eGD+/PmBPn362OeI95jP9A033BAYOHBgYN26dXH52YJhw4bZ673vvvvS/Bp37doVqFChgj3+rLPOCnz88cepehzbad++vT2OC/sgRKzw+++/2+eW/4UQQoSHf//9N9CuXbvg2IGxcbRw6NChwMMPPxzct8KFCwdmzZoV1n3gnJQ3b17b/osvvhjWbUcz8XDOjofXIIQQIv7o0aOHnZ8KFiyYYeuS+/fvDxQoUMCet23btoF4Pm/Hr4dLhKEqm8omqqGHDx+eqiA4Oi3waaXz4rbbbrMOimzZsqV6m2zPax96+umn3VlnneU6deoUN5ZGBAB++OGHZqOzbt0664ygWoxgcd6vAgUKRHoX4wo+PxUrVrRL9+7dTRGeNGmShcD36tXL2vaxFUAdbtasmYXtxAt79uyxriesrtL6/cHCgE4ULJv4Dt9zzz3WFfXII4+k+Di2M2TIELPWevXVV63SkID7O++88xRfjRBCCCHiDeYLbdq0ce+8846NIbBSYh4RDdCN2rJlS7d161b7mYy9gQMHpjqvK6OgUo/ASrqtO3fuHNZtCyGEECLz0bNnT7d//36zZsooN5f27du7Xbt22frboEGDXDyjTAofWbRokQX0nkygQFzAlqlr1672M4N67F5SmwPA42ljXrhwobU0x4so4QW5kFiPOLFs2TKz0GnYsKG1T9WoUSPskx3xf2ADNWvWLJv4IRpxECaHAXszMk/SEj4drSCEXX755cHvE21p2ASkRnD0LJwIqOe7CT169HDPPvvsSb+ffJ8RNHhvJ0+e7K688soMeDVC+ItsF4QQIrwWnVjJUgxBUQVCBWOwSPPXX3/ZeIcJNOMZxoOjRo0KqwUVNrDMH0qVKhW2bcYa8XDOjofXIIQQIj7AKh27eT/WYj/66CMrCma8R57qddddl2HPTUEyRS8ffPCBu+yyy5yfKJMiAhBKzSCJ6ufUwkImFekvvfSS/cz1/v37p+nDvWHDBlvIZMISi95kSX14x48fb18UFsKp6K9Xr559MclEULZEdMHn7uuvv7a/FwvrBLZXqVLF/l7kM6Tl+xCtcOCuVq2aO+eccyxciKCi1MAEvW/fvqamA90RdP6cLJuG7e3bty/V2xEi0mixQAghwicENGnSxMZcjJEp5rnjjjsi/vYT4IhQsnLlSvuZHIrBgweHdQGZ3IuaNWvaOYnirYIFC4Zt27FEPJyz4+E1CCGEiH1Y6+nTp4+t43qF5xnFli1bXNmyZd2hQ4es4NVbV8oIWIsmy5f1PMZPfo+ZUnveTl1JsDgpLCgSYnLjjTe6nTt3puod48NA14QnUNBNQZBcWtW3EiVK2HNgEcMifizCYi6Tifvvv99ddNFF9j8LtXSU/Prrr27cuHFmKySBIvpggozdFkHx/K2o5suSJYtr27at/S2ZsKIs8zeOVZYvX+4WL15swdgcXFML32UqCocOHRq0YmBhASEnJRAxQgUK3j+qAoUQQgiRuWGMcO6559qY+PPPP4+4QMEkl9BuuscRKC688ELbr5EjR4Z98ZjJL+NSutHTMl4TQgghhEgPjHvg8OHDGfoGHj9+3NaOEChuuOEGs/TP6DVs1qSBdbtoQXZPGWR9Qxvz999/7woVKuQWLFjg8uXLd9LH4DWPnQuTDVqhmzdvnqrtsXjPZACv+1jPYeCLTAX+W2+9ZQvBvH/46fJeqPoptvn555/tb0smy+bNm13JkiXNk5i/bSxWPK1Zs8a8lRFk0gNCG90lnGyqVq1qE/jUvA90R7EAccUVV5jHcyy+dyL+UUWjEEKED+YCjEsinQdGXhkZXNOmTbOfGSO9/fbbEe0EpViMuRUZYSJ+z9nx8BqEEELEBxRcX3/99Rn6nI8//rgVo+NMsmLFigxfH8VSn4Jici42btzo/EadFGGcJLDwiEDBh2fKlCknFSgI1abrAoEC+xjatVMrUADBb9hCsV2ql2IRFq15HYgs7dq1M89a3o8ff/zRPfPMMxIo4gC+B0888YTZkU2fPt1ECoLc+Zt36NDBMh9iCbyNQwUK9r9WrVrWGpcasL7i+ECOyuzZs93NN99snSep2S5ZLJyYeA5P7RZCCCFE5oBzP12ZzDuARfhICxSTJk2yfUCgYD5DwdGXX34ZVoHCy/XDJtaDSbwECiGEEEL4waZNm1zr1q2t+NQjowWKSZMmBR13cCrxo4B79OjR9n80ZJolICDSzX///Rdo3749HjaBrFmzBr777ruTPuann34KlCpVyh6TI0eOwNy5c9O83Y0bNwby5s0bGD16tO1DLLFw4cJAw4YNA6eddlogZ86cga5duwa2bdsW6d0SYYLPf/fu3QN58uSx78Ctt96aqu9NNHLDDTfYa2jcuHGaHrdkyZLAhRdeaI8tXrx4YMuWLSd9zOLFiwPZsmWzx7Ro0SLmvvci/vn999/t88n/QgghMo7jx4/bWINj7AMPPBDxt/bo0aOBRx991PaHS5kyZQJr166NyL588skntg/nnHNOYOvWrRHZh1gkHs7Z8fAahBBCxN6YrHDhwnb+eeKJJ3xbM8udO7dtg/GWH7AGy5os2wjX+Cm1521lUpwCgwYNshBcvOaxtcEn7GT2N1RP055Nhc93331nCfCpYdeuXcHrtONgO0OehR/p8X5UOc2YMcMssVAYef1YAP30008WLoPFk8gc0EVBqBDdBwRQEzZPjgsXOmliKbeC/aezYsiQIWl6XPny5S3bonDhwtZRRMg470NK4PM8duxYq5xku7169TrFvRdCCCFEtEPnxH333WeWkeR9kc8W6erBihUrutdee81+7tixo3WTY0kZCbDDrF+/vuvXr5+Nq4QQQggh/ILcKzpbK1Wq5Lp06eLLuO/ee++1vIirr77aDRw40PnBe++9Z2tv2JBH2/hJIkU6+fPPP02gAPIhsGFJib1797oaNWrYoiQfAhYpS5cunaoPKZY5DP5XrVoVvJ226mgHK6oJEyaYMIG91YEDB9ynn35qNjlt2rSJidcg/IGwRyzOCFjE7uyff/5xdevWtQMxAdyenUE0U7RoUffVV18lCBlCeEyNHRNCI8eAyy+/3LyTq1WrlkCITIpbb73VToiA0EOovBBCCCHiEyaPDz30kBszZoxNij/77DNXp06diO0P47Ny5cpZhlzu3LnNiuCVV15xZ599dlj3Y//+/cGiFgKyyfhCLBFCCCGEyGgYczD28Khdu7abO3euL/aWvXv3dt98841ZhDP+Y93MDyiwb9SokeUBRxsSKdIJH5r58+e7wYMHm89+SrA4X7NmTbd27VqrJJ81a1aq1SoW+pcuXWqJ7vjYxwLsMxMZRBg++NmyZXNff/21vQ7CwqkGFwJOP/10q4Dju8TnmwP93XffbYv3dAzEgljhwf4jNiAmHDly5KT3p5uKDiPEji1btpiIuWfPnhQfw0mkR48edn3OnDkx1XkihBBCiNTB+Z0ipZEjR9pY6eOPP06QixVOjh496tq2bWvjM4q0brrpJhMqGO+Em2XLllkOxoABA4K3xUJXuRBCCCFiDwpQKbCmizRUqPBj7EEBbN++fe06zjMUtvoFHRQUvzC2izYkUqSR0EVBFhlPJlAgLlD1xGA+T548bubMma5IkSKp3h6VU7R405Hw6KOPumh/bxAjsKbhw87iK4vPKIGINJpEiOTgs4EVGp+fJUuW2AQUO7MyZcpYCGMsLMYjTKB08z1PbZcQoiXHhIsvvtitX7/evie//fZbio959tlnTQSkRU/fKSGEECL+wL7IC0x8++23zdYoEtABzsR82LBhNubo3r27jVsYv0QCrKV2797tPvzwQ3fs2LGI7IMQQgghMgcUnDPu+fHHH923337r23Z4fuw94ZFHHolK8SBcSKRIo4qG4PDRRx+letGyXr16buHChS5nzpxu+vTprkSJEid93IIFC2xC4sFjGzZs6KKZRYsWWeYErU90TmB7M3HiRJvYCJHWzAZUXT5TdFY0aNDAMitoqYtmsKtin6l6TIt4QFcVJz5e64oVK+wY88cffyR7f567SZMmVlkJCDjYZQkhhBAiPihVqpQVPtCxff/990dkH7DjZEzG2OTCCy+0QhLsJrFYihQPP/ywCSbMM8JtMyWEEEKIzAUFqNhbUjjrVy4Y68YUo/z++++2fkr2sV/QEduzZ0/LGItWJFKkgaefftoG6AyQyZhIib/++susjrBk+d///mePozr8ZJDXQOsNHrR0IEQ7GzZssDwOciewquHLy8ThZCHiQpyMa6+91uyQ+O5w4EaoQPQLzWaJNkqWLJlg0swxIzXZEZdddpm91ly5cpnQgaVDaiyjOMk0a9bMdejQ4ZT3XQghhBDRAQUazAlO1rHtBxQ+PPXUU1YgRdFE5cqVrSOcfLlwg+3nm2++6Y4fPx687cEHH3Q5cuQI+74IIYQQIv7B2YM1zdDCEQpS/YCC03bt2llBCILI2LFjXZYsWZxfUAxMwQmvJ1rdSiRSpBLsljxFa/To0VZRlFLHBZXOLK7SVTB58mSzQEoNePGz6MhibGofEwkOHjxo9lN8YRcvXuzeeecd+2Kx37KgERkFnyUskDhR4MfMhB0LKCao+/bti+o3muwZ7BrYVyb3J4MMl2nTprnzzz/fToosDpzMyoD3Besnqgrff//9DNx7IYQQQoQTCpu2b98e/Dkt9rAZBQVHjLu8zAdEEjK3sLiNBK1bt3bt27e34i0hhBBCCL9tJatUqWLFIhs3bvT9zSZ7gixWXDJY1yngs50ma9nQokWLqF23lUiRSn8w/PGhS5cu1iGRUsVP8+bNrUWaNm06C6hASgkeQ9g08EF56623TOEinDvaYD/5EmFbxQe8f//+1k3Bh1yB2MIvOGg3bdrURIrXXnvNFGY+g3xXojVcm46o5557zjIkypYtm6rHlCtXzk2ZMsWde+65Zg931113meiZHOR49OrVy64Tarl69eoM238hhBBChIcffvjBuigrVapk845IQNER4xBECcYhY8aMMbsp8vEiBWM/9qVWrVoR2wchhBBCZA4oiMUB57rrrnMXXXSRr9vCzpz8CaC4lfUjP9myZYsVxLDmzJp1tHJaIFp7PKKEo0eP2oSBSmjEBgbuyQ3WWcB/4IEH3KhRo+w+dF+crC2IBUgCUvCjf+WVV6JWzQLeA6qZCMMmyIXOkkhVVonMDZV+WBEglOGX/MYbb5jlWLSDDRyH3JP5KHOcufXWW62TAqGCHJzkREBEGu5LFwbCDYsM5513nk+vQIikwRKELiC8NLE4FEIIkfpJI3ONX3/91VWrVs06sCl0Cid0RFPswDiFsQRzmCuuuMJFAsZJofMhOmdz584dkX2JV+LhnB0Pr0EIIUTkSTzuICyb9RQ/M7h27NhhzjnECGCf/+mnn/q+FtyzZ0+zesK+k7WjaD1vq5PiJGBpxOI89k5UFKVUTdS7d28TKKj6xpomNb5lKFk879ChQ92aNWtcNMKXFM97FoOxeWIBlUVTCRQiUuDXx3cNwYxF+goVKrg2bdqcNCsmkng2cFRKHj58OMX7oqKPHz/ejjecsB5//PFk74t48cEHH7iLL77Yupp4H6Q9CyGEENEPC/C1a9c2gYLqPcSBcAoUjE2Y6xDOjUBRv359y8aKlECBVSZFJ/v37w/eJoFCCCGEEH5AxifWTqwteVxwwQW+ChSsBbFN1q5w3KBQxG+B4sCBA27IkCF2nfWiaEYiRQrQGcHAmEVARIeU/MFYtEeVAoJySWdPDTVq1DDLms8//9w86aONcePGWU4GFk8vvviiCTZYzAgRDVSsWNFyGeikwCKN6r/33nsvKhfp169f72bOnGltfStXrjzp/evUqWPiA9BlhV9hciCiImZwMuX/119/PUP3XQghhBAZy5EjR6xwYdOmTa5QoULWQRHOinAmx1TTeZNWiq0QSSJVlY5gQvYEHaHenEoIIYQQwi9Y5/zqq68sgyu0QMIvWKeiMMQrhCcmAFtLvxk4cKAVnF955ZWpXquOFBIpUnpzTj/dvMFYXKxevXqK4SqtWrWy608++WTwenLQ3hJaSc2AHLuWaIKJC1Xfd955p9lcUaHduXPniPrSCpEUiIjt2rWzzygL++SjoEz//PPPUfWGcUKYMWOGiSmIK6kBqydvoo7VGl1MycFzYsGGdRzbEkIIIUT0FkLhB7xw4UKXM2dON3Xq1LB2KK9atcpde+211tGNpQGTZDKumPtECuYYFG1hnUvmnRBCCCGEn7COhGsM9ke5cuXy/c1+/vnnLV+VMQ/OGZdccokLBxdccIGN99h+tGcJK5MimZafLFmy2CU1XmKEqtCmTYs0H7SU/uheWzcTEhS7cHvOpgYWUR9++GGz0aFCHbEimrMyhAiFCS6+ysePH7eQ7WbNmkXt55fjBgsV+fLlS1Ftv/fee61bi5MLCxqXXnppsvf97bffwnKCFSIUeUMLIUTq4VxN/sTatWutgOGmm24K29v35Zdf2tiI+U7x4sXt50jZO2ExtXHjRhVXhJl4OGfHw2sQQggRfn744QezWQp3YQZrxV4XAy4ZFGWEe+x5wQUXRGxtTJkUpwAJ60wWECBSgsE9woTnI/vhhx+eVJXaunWrdWbQ3rN9+3YXTSCgNG3a1IJbbrjhBps48XO0LvAKkRQNGza0fBfEQELp+fmXX36JujeLk0TNmjVPeqzh+zdy5EjzaMZLEGsI/k/uvqECBd/paLS+EkIIITIzFCt99913ZvEULoGC8cCAAQNsXMQcBpGEwodICRSMZbCb4vUzNxJCCCGE8BPWVSgyf+qpp8L6RjPeokAE6NwIt0DhjT1jYW1Xdk+JmDJligWXEBq3c+fOZN84qp/5kK1YscLsVahCyp49+0nfcFqrJ06caO3Vl112mYsW2P9SpUq56dOnm9hCNwWvS4hYhIV6Og9Qq7FjK1mypAXURxMoyVxYKKDrIyXOPvts6xApWLCgVRxiw4Z388n8FYsVK2bthEIIIYSIPKF+x7Tdk00Xro6Fli1b2qQcsQJ7AyymmLBGCsY2//zzj82porGYRAghhBDxBWMPHGMYdzD+CAcUqlPcfuzYMbP5Hzx4sAsXffr0sTXeWCpclUgRAu2inqLVsWNHy2JIjm7dutnCPnZNLB6m5CW2e/dut2fPnuDPBE9HqmopqUkLITF4+FOpTQX6PffcExMKmxAn4/bbb7fPNJV6dAU9+OCDFlQZDRQuXNiqKAnTxm7hZFx00UVmEUewEo/hGJXSyWbLli0mgpBlQcaMEEIIISIH3QLYNZK3EM7JIsII46D33nvPOr6xcuUS6Zy5c845x7Iw5s+fb3MjIYQQQgg/odD8m2++sYLOcNg9EVZdt25dWw/GYuqTTz5xZ555pgsHK1assLwx3DsodI0VJFKE8Pjjj7tdu3ZZ9TGBIslBpwXp6DBq1ChXoUKFk1q6YJ8UbfZOmzdvdpUqVXJDhw41734mCiyEChFP5M6d204Gb7/9tvvggw9MjMPKLBq4+OKLrcvDg5MH9nHJga0cHSKIiHxvWWRIjmeeecY8nrF8oqVQCCGEEC5i1kb16tWz/yk4OFk3ZEaxadMmV7FiRSuKwLefjnG6KCIFXa2jR48O/nzhhRdaJ7cQQgghREZDsSZ5pYcPHw7eVqVKlbAUZeOWQQbFunXrXIECBcxRhy7acPHMM89YUQwZwyVKlHCxgkSK/wdp7ixiesJDtmzZknzDGORTjQ09evSwroOUYDKCnQtfinC1E6V2klCuXDmrtF6wYIHlcKh7QsQrfLZbt27tFi9ebN9DbNcQG6Op7W3ZsmXWvUWWBl1dyUGrIJ7SQDfF119/neT9smTJYgsBVE1++umnZn0lhBBCiPCCpRETRIqDChUq5CZMmGDnaL9hzkIhFUIF26VjgY6KSPHtt98Gu1qXLl0asf0QQgghRPzDWg8FIsOGDXMPP/xw2Lf90EMPuVmzZlksAAIFQkW4+Prrr92kSZNsLQjLp1hCIoVz7tChQ0GbJy80OynIqGjUqJFVPxEu3bt375O+wXRlzJs3z3zAihQp4iLN0aNH7cvCJIG2IyYJiBVCZAao1kOouPvuu93999/vmjdvbt//aABVnZMIl5NVWNL1xf4juNx11122AJEU5cuXd127drXrnJhDvbCFEEII4T9PPvmkzQMogKJrOU+ePL5v8+OPP7a8Czq6CYgksDHSHQs33nijFXfR3YnlgRBCCCGEn4WqL7/8slntY9cfTrp3725FsVhKUSAeznHPH3/8EVzfZsyF1WgscVogmkqJIwRBJogOdD2sWrXKPN+TqoKqVq2aVSWxqM//yXVbEMSCtVPRokVdtL3Ohg0bmqXMkCFDrLJc3RMis0JAPK1/KNosGkRDCxytgFhApaYNkPbB6tWru7lz55oYQYVkUpWZ5M5wzMLiisUBXrcQfgyGzj//fOsCwlJECCHE/1nEUlQA48aNs7Z/P2FaN2jQIBNGgO2RRZHcnMVvKARhXuX5PjNHohhDRJZ4OGfHw2sQQgjhz/kh9LwQ7rEHa62PPvqoXR8xYoRr06aNCyft2rUza3CK5JNb347m87Y6KZyzP97333/vZsyYkewfsG/fviZMsHiIdUpyg30mB6hVLBrSQREtEA6DxQ3WU4sWLbIvigQKkdlDk+gkYuJMTsXUqVMjvUum8ocKFIQdJWcThyBBpWTOnDntdSRXHZA1a1ZbJCEgM2/evHaSFkIIIYT/xUF0L0PPnj19Fyg4vzMp9gSKxx57LMU5i9/s2LHDsu+eeuqp4G0SKIQQQgjhB6zFYm1E5yhZw5EYezDuwpIb2JdwCxRr1qwxgcITSKJFoEgLEin+HyzgJdf5MGfOnKCP11tvvWUWTinZKaFWoQ6FfjEiCfuMBy2huwgUhOkKIZy77LLLLJOFYHvsz2gHjJbmsrFjx5qw2Llz52T3ia4LL4CSfZ88eXKS9+N5tmzZYvfRAoEQQggRniIoqumwZezVq5ev22L+QVf466+/HrQ34OJ1MEQCCsBWr17tPvjgA7dv376I7YcQQggh4h9ygD/55BP3008/uc8++yzs2yd/4r777rO1m/bt21twdbgpVaqUvXYKWHHdiEUytd0TFUaEmOAXRrVxUuDhzuI+gkPLli2DC4IpceTIETd79mxb9Iwk+Np36tTJvfnmm5a18dJLL5kYI4Q4sfqQkwiB1C1atDBh7+yzz47o24Qt07333muLG1w/88wzk70vlZMshOTOndu6L/Lnz5/ic3PYVyeVyEhkuyCEEJE555I7QTAkto/MZ95//3135513RsWfgyq+WrVquUsuuSTSuyLi7JwdD69BCCFExrJt2zYTC1q1ahXWt3bZsmWuSpUqZnNJ0QhiiYpDEyK7p5Pw7bffuldeecVsnAiTS25SgY8sAgV+9SwCJsfu3buD12mrjrRAQcVSzZo1bXIwfPhw99prr0mgiDAErTNJTepy8803R3r3MjWcQPr372/VfpxQqlat6n7++eeI21Fxgv3oo49SFChg4MCBJqbyvUe9T8nSacOGDdY5klzXhRBCCCHSz7Bhw0w48PBToKBakEBqBIocOXJYQHckBQqKKqhk9CC4UQKFEEIIIfyy1mTs41G4cOGwCxTkirL2ikDBuh7FIuEWKNauXev27Nnj4oFMaffEAh6dBd7g+aabbkryfrRMf/XVV+b9zsIlXRdJQXBt8eLF3bvvvuuigU2bNpnHPi3WM2fODCa7i8jy+OOP29+iYsWKtgDOpUuXLtaSNX78eP15ogCEAbJn8FG+7rrr7DsUSRBLQk9we/fuTfJ+dH2MGTPGBFKEDTpCkmPkyJG2mEH3BaHaQgghhMgYCKlu27at2SyGLtb7wfr16y3zgYkpHZSMXxAsIgWWVnSA3n333cq/EkIIIYSvbN682VWoUME1bNjQMjojJZLUqFHDikWvueYa98UXX4TdkeP33393DRo0cCVLloyqXOT0kilFilGjRrmVK1e6Cy64wPXr1y/J+yxfvtwWlWHQoEGubNmyyT4f4bV4wU6aNCnifvZ8OStXrmxdE4sXL47oZEUkBJGLRWREr4suusgu3EaVPOHHIjpgYYHvTq5cuez7gwgZaQjPxo6Kji5OxknB7954441gQCdCRFL06NHDPns//vijCbFCCCGEyJhWfy8om65GP8MK6QKnK3Lnzp12/uecX7p0aRdJsHU655xzbB4SySwMIYQQQsQ/hQoVsnVackZZ3wg3OO6Q+4CrDoXHU6dODbv9YOD/uf+wRsTa4hVXXOFindMzow8WGRTeQh4LkYn5888/XZMmTdzx48dd/fr1XYcOHVJ8Thb6aO0mryKSPu+0OdFeRAA4C6u0Ogkh0g4ViXPmzLGTHqHzX375ZcTzZeiQOHDggHV3JQd5Gvfcc49VMFLJyP0Tc95557nnn3/ervfp0yfZ7gwR2yBYcQ6gkoPOukWLFqV4f+wPWehigalgwYKW2XTs2LGw7a8QQsQyZNg1atTIjpu33nqrzTH8HO8zKWabFFYw5meiHglCi7Po6mCS3LVrV+VeCSGEEMKXcYc39qAwe+zYsWblX6BAgbC+23ROsE5EJ0WxYsVsbJbU2rLfDB482E2YMMEKoceNGxcXxc+ZTqSgcwKvrksvvdS1a9cuyftgBbVx40b7oNN1cTLhgd8/+OCDvlZMnQzsqMjBoPIbiycCdIUQ6YcwvilTptj36vbbbzebpEhBECaWYHzPWTxO6Vg0dOhQO1FiWYW9WFLdXYgZCDC0BpKVIuILrL86d+5s1hs//PCD5ZVQ4ZqcTyW5J0899ZTdH09NPus8x9NPPx32fRdCiFiDbkdsjghr5PxLvpVfnQSMBW677TazksL/mAKGSI35scSkm4NcjNAiDyHSm9V3+eWX680TQgiRJP/8849r3769e/nll4O30blAEWY4oRCUuTXzZtaMZ8yY4fLly+fCzdy5c92TTz4ZLDjEbioeyFQiBR0SLOB5Fk6oTYlBiXvnnXdsckH4W3Jq2IsvvmiVQikF1IaLV1991aqmueCBFkmxRKSNSHbeiJPjZT1g39CmTRv3wgsvRMzSjRMfHV4ngxM1FnTYiH322WfW4ZUYci5Q3YEuMPysRfzAwA2BitZPvCnfeusts5pDdE8KbEKw56ALh+4LFr44n6TUfUGeCZ2JoRchhMiM9O/f31r8GTMgImAn6wfMTwjFptOb/+msTC4vz28YC1GgxfkjpeIJIVICewwvp49LNFisCiGEiE5wt2A9l3VYrKsjwcGDB22uTCHghRdeaAJFJBxs9uzZY2tDrEczhycPLV7IVCIFA3myJvr27evq1auXpCLmBWp369bNValSJdlgaqpOBw4c6CZOnOgiBRMEfOo7derknnjiCZu80PIkYgdU319++cU6d0R0woI+1jnPPvts8PtG1WQkOXLkiJ2MECGSAvsHz9KJcPZff/31hPtgDUfIFCc2hE4RH7B4RTYRAV4eiO78vGDBgiQfg0UHj/FEiS1btrjJkyebZUlKXYl0G3kXLKKEECKzQSadJwAzVrjqqqt82Q7naYRnxh+tW7e2YoSkiq3CWWRDF17jxo2t2EGI9EBBjZfTx0VOAEIIIZIDdwtygyksp3M13OBCQQfFkiVL7HxFN2ukOgB79+5tWRhkUDAOi6vi54AI0qZNG0qkA5dffnng2LFjKb4zH3/8ceCRRx4J/PfffxF5B9luly5dbH9ffPHFiOyDSDsdO3YMVKlSJfjz6tWrA3nz5g2ce+65ejtjgKFDh9p37qGHHgr8+++/EduPQYMG2X7kyJEjcPDgwSTv8/fffwfKlStn92vSpEmS99m0aVPgtddeCxw/ftznPRbhYteuXfY3nz9/foLbn3jiicB1112X7ONeffXVwFlnnRU488wz7fFt27ZNcTucI3///ffgZefOnfY4rgshRGZi//79gSFDhvgyJ+A5n332WTu+cuncuXPE5h6Mexi3itiHc3Wkz9m9evUKZMuWLZAvX75AkSJFAvfcc09g+/btyd5f4w4hhMh8rF+/PirWKjhfVqhQwc6dOXPmDKxYsSKi+3P48GGbr69duzYQb2OPTCFSMKj+4YcfUrzPN998E5wAfPvtt4FohskJi93sKwuMQojwMXLkyMBpp50WaN26dcSECgQIJnNz5sxJ8X5Lly4NnH766XasmDhxYtj2T8SWSDF79mwTS0eMGBFYuXJlYPz48YGCBQsGnnvuuZha8BBCiHiC8f6TTz4ZnJ/06dMnYgIFC8QUPLCovGTJkojsg8g4ouGcPXny5MCnn35qCz1Tp04NVKxYMXDJJZcE/vjjj2RFDe+7EHrRuEMIIeKTKVOmBLJnz27rLpEa/wDnpcqVK9s554ILLggsW7YsYvuSGcYemcLuiWyJcuXKJevTdezYMfOcB/xVCZ9ODO3V5FCQaxFJEJY6duxobd+0lXv2VEKI8NCqVSuzVsPeAcuFSFg/0R7Pce2mm25K8X4c9zyv6Hbt2qV4/MImaMWKFRm+ryK80HqKRVliiy9+xkohKXr06OHuu+8+y1258sorrZWW/BUsnSJtbSaEENEI1rEjRozwLaeK58VeEmtZIEeqe/fuEWvn57yCDzNjBWxvhThV6tSpY9kqWKRhn4HNJJ+xTz/9NMn7Y8WM1YZ32blzp/4IQggR50HZ2FxjRcyabSTgfFO7dm03b948lyNHDsugKFu2bET2Zfz48WY/Hu/z87gXKQ4fPmyDGkgu0ITFmA0bNtgCzoABA5K8DwIFyen4ekfqQ8GEpXPnzm7IkCEWhMqioxAi/DRv3ty999577t133zWBM9Inir1795o3dFKQpcGxb8eOHbYYnRSc+AkvrF69uk0QReyCR3n58uXdzJkzg7fx+eTnihUrJvkYBn/kViRekIJIBcULIUS0Mn36dNezZ89gcHRGwzGbscVrr71mPzPmR7CIJBRHsHg8e/Zs17Rp04jui4hPWPy57LLL3ObNm5P8fdasWd3//ve/BBchhBDxy2233ea+/vprN3XqVHfOOeeEfftkFt9yyy021uMcxfiPItBIsHDhQtesWTNbu2YdKp6Je5Fi0KBBbteuXbZIl9QAf82aNa5///52ncV/PnzJBdHmz5/fPfDAAycs5oQDFooQSV555RXroPA6P4QQkeHee+91o0ePdiNHjnTt27eP2GIuAgVVaOzPt99+e8Lvzz33XDd06FC7zoLH4sWLT7jPJZdc4s466yy3f/9+qw4VsQ1iNhW+iGjr1q1zDz/8sAn2hK56Ipsn3kO9evXsM/LJJ5+4rVu32gAMQYvbPbFCCCHE/3Wl0XnGOZ+xeOXKlTO8arBly5Z2DGe+QedmpMb8c+fOtTmHB4vCN9xwQ0T2RcQ/dPv++OOPLl++fJHeFSGEEBHg6NGjrkuXLu63334L3kaROEV44Wbfvn2uWrVqtnaSK1cuK9K45pprXCTYsmWLzcvpJqlbt66t+8Q1gTjmt99+C5x33nnmezVmzJgTfo+ffKVKlez39erVO6nPGd5ZkfJC80LzCDcVQkRXRgXfTYLsI0XLli0DpUqVsjyB5CDDgv0sU6ZMkuFTeAPz+6xZswZ2797t8x4LvyHEFW/nLFmyWBbF999/H/xdlSpVAi1atEiQcdK7d+9AsWLFAmeffbblUbRr1y5w4MCBmPK3FkIIP2HeULNmTTvWlS5dOnDkyJEMfX6OxU2bNrXnP+OMMwKffPJJIJJBlZw/2BfyAkR8EQ3nbMbNZEJu3bo1MG/evECNGjUCuXPnDuzZsydmXoMQQoiMo3HjxnZcZ6wVyQyKX375xcZ57EuePHkCq1atiti+7N69O1C8eHHbl6uvvjpw6NChQKyi4OxAwEI/+WNeeeWVSQbcvvnmm/Z7wlh27NiRbGp6pHnrrbdsP/v27RvpXRFCJAEB9nxHX3zxxYi8P5ysTrZY8uuvvwZy5sxp+zlgwIATfs9AgNBCfk/IshBpQYsFQoh4h3Mn58hzzjknsGbNmgx9booH7rzzTnv+s846KzBhwoRAJGFM0KFDh0CjRo2iYi4k4u+cTRB7vnz5TAwrUKCA/bx58+aYeg1CCCEyjhUrVgQKFy4c+PbbbyP2tu7cuTNw+eWX2/mFc9S6desiti979uwJlCxZ0vaF92XXrl2BWCa15+3T+MfFactooUKFrFXo448/PsE/FQuokiVLuj/++MMsUJIKoJ4wYYJ79NFHzfOratWqLlLhKISKYSdDWHakAvOEEClDiJHnEYgVRCQh2DKptkhsI7D8wdNx1apVrlixYgl+P2nSJPN+zJ49u9u+fbvLmTNnGPdaxDKcS88//3wLF5NPtBAi3sALGKsj7JiwYmrTpk2GPffff//t7rnnHjdu3DizXvzss8+srT/csB/APsC///5r845I2NwKf4mHc3Y8vAYhhMjskI2YLVu2BGMRbxwSbjZt2mQZFKyDFCxY0M2aNcsVL148Ivvy77//ugoVKrglS5a4AgUKmK130aJFXSyT2vN23I46169fbx9uPlQs8icG8YE36frrr08ygBrthrDsn376yYJaIsGcOXNs0tK4cWPLopBAIUT0QpZDq1at7DJlypSI7QeiLMc9vAsT06JFC/NWxO+RnILEGvWtt97qypQpYyKvF9gphBBCZHbwJGbC2KRJE9e6desMe14m4xRSIVBQXEBxUiQEioMHD7o6deq4tm3bBscGZBJJoBBCCCGEH1DcyboF4oBHpASKlStXuhtvvNEEiksvvdRyuSIlUHhjMPI5EChmzJgR8wJFWohbkYJQEwJAv/zyyxOCP1n8ZxLA7cOHD08yGBRBgPDQPn362CXcrFixwtWvX9+qtvjyapIgRHTDMWPYsGE2yUdYpOoy3Pz3338WgLxz50730ksvJbuPWbNmtePb5MmTT/j9008/bddXr14dtv0WQgghopkOHTrY/IFzaEYVDSFQIHowJ0GgoIObbsZIQKUeoZCffvqphRcLIYQQQvgFnalvvPGG+/nnn21NNpIsWLDAValSxf36669WsPndd9+5Sy65xEWapk2buo0bN7rLL7/cZSbi1u4pOXi5dE9QEUUl8ZtvvumiDcSVSpUqufz589uEQS2sQsRWy2LNmjWtmwsFPtwnFdT/999/3z311FPuzDPPTPI+Xbt2dQMHDjTLOwTR0PtRKbp06VJ33XXXhXGvRawj2wUhhEibQHH33XebtROFA59//rmrXbt2RN9CLCHLli1rFxHfxMM5Ox5egxBCZGYQBRh7PPHEExEryqZw8/bbb3eHDx+2NVjsr3PkyBGRfTl06JC5/GAhjt1UvJHa83bciRQM+qkOplU6qQ86FUJULZ177rlWKZQ3b94Ev1+2bJkt8jVs2NBF6g9XsWJF99dff7l58+adsH9CiOjnwIED1i6IrdKiRYtcrly5XLTtH3kU/P/2229nqHWFyJxosUAIEW9gm0j2BB2KJUqUyNDqwXvvvdeNGTPGOigQKOjCDDfY2VKQoPypzEc8nLPj4TUIIURmgrxg3CYiMeZJziYbO2zWkCkypbOVdeJIwLoM78vChQvdtddea//Hm91/ps2k4IOGwIDvelJhsp6VCWpdYgHg2LFjrlmzZqakRaLliApmtk8OxsSJEyVQCBGjXHDBBfYd5kBMJo4XRhlu0KBHjRplqnzi/evevbtd79mzp3V/JMXevXvd999/H5Z9FUIIIaIFT0igo5kcu4wc6zMhRqDwQrIjMVkfOXKk5VA1atTICqOEEEIIIfyCdQU6FRo0aGBjq0gzePBgy//1rDeJCYiUQMF7w/o1wkTOnDmtOCbeBIq0EFciBX7s/fr1s+tJtUzjI+t1TxBCktTjGbBffPHFNmgPNz169LD2ok8++STT+Y4JEW8ULlzYgjDxNOzcuXNE9oF2QbokevXqdcLv2rdvb/u4e/du98orr5zwe6yqChUqZCftSIksQgghRCR4/vnnzaOYSi/mDxklULRq1cp99NFHZrM4duzYiGVQ0EGRPXt2d8UVVySZzSeEEEIIkVHgLEHew0UXXeRy584dsTeWIk6sr731GQpRGJdhvRkJyOS4+eab3fLly22d+ptvvnHly5d3mZm4EikInMMHnhYSFudCoaL5ueees+u9e/e2gXlismXL5gYNGuQ2bNgQ9i8OHSAILPjER0v7kxDi1CCA6fXXX7dLJLqzqFQ455xzLN8mMZyI+/bta9f79+9vCn4onBxZnNmxY4f78MMPw7bPQgghRCSZP39+cM7w1ltvmaB/qlAI1bZtW/fee++ZKEBBEufoSHHllVe6lStXWjZfcvlVQgghhBAZAVb85E9ghc0YJBLgrNOyZUtbcwXWXynWjFQexqZNm9wNN9zg1q5d6woUKODmzJkTsfcmmogbkQJFjKoneOSRR07wuOKDuG/fPvOUPZn/OmJFOFmyZIlVVt13331JdngIIWKXhx56yERTOhe+/fbbsG6bjjIydh5//PEkf09o59VXX212UJ5g4YG44R2POIFTASqEEELEMxQ1YfOEqMD/nCczYo7SqVMny4BiIozwf8cdd7hwQtfkLbfc4jZu3Bi8DfElM9sJCCGEEMIfGPuwBvvMM88kWF+gkyISkINQt27dYLEIlthPPfVURMdBrA+Rf1a0aFFz38jI/LNYJm6Cs0llJ+wEgYFFudBOCAbmxYsXtxBbwlDInAjl/ffft7BtfMnC/aWhvYdgFE85O/vss8O6fSGE/3hhTKtXrzZREhulaGHGjBm2cIE39rp16yxQ2wPxgn0lyAnf7EjY4InYQAGWQoh4gKKh0aNH2wI+rfd0Z58KTLO6devmBgwYYD+/++67rnnz5i7cIIowB8IPGjtHiROZm3g4Z8fDaxBCiHgFy0zGHDBv3rzg9UhA5i+2/qtWrbLciU8//dR+jjSsBXfo0MG6WhPnJccjmS442/OLvf/++0+wasKPHYGCLwah2qEQFvfkk09a2zViRbhD+fB7p1oLqyoJFELEJwgAeE+fd955EQuppJ2QbXMyDKVGjRquVq1aJqSEVjoA+4s9BWSUJ7cQQggRjRw5csStWbPGFvCptDtVgQLoUvQECqyjIiFQABNg7GQ/+OADCRRCCCGE8JWKFSuazT52SpEUKFasWOGuv/56EygoSMfZIpICBUWrHvny5bNC0MwgUKSFuBApWOzHN92zVgkFfy9aeeDFF188YWCOL/vEiRNds2bNrBU7nDz77LOmKo4ZMyZJz3ghRPyAeIpQwQmSsKZw06ZNGxNDEWUTwwIKx0aORYsXLz7hcTBt2jS3devWsO2vEEIIEU7oxmZczvnuxhtvPOXne+mll1zPnj3t+ssvv3zCHMVvNm/eHLzOBJiu8SJFioR1H4QQQgiROdi1a5cVfIQWi3fs2DFi+/P111/beA5nnZIlS7rvv//elStXLiL7QmF69+7d3VVXXWVB3SLORQoC3xYuXGiLf4mDRqgM5gOBxVNyCh4BsVQWUe0cTosVMjQI5suIiZAQIvrhWDNo0CD36quvus8//zys26aKgaoBKhoSU6ZMGcvEAbwZQ8Ej0bOD4jgrhBBCxCvMKegwPFWGDx8ezIOim+Kxxx5z4YJ5D8UQTMhnz54dtu0KIYQQInOybNkyd91119maAuOQSEMHKRkU2FfffPPNVoQSKcvtw4cPuzvvvNPWf7EBDc0HE3EqUgBVwKVLlz6hi4KFQH7nhWp7YG2yZ88eFwl++eUXC+OrXr36CQuCQoj45pFHHnENGjQwazryc8IFAdmTJk1KkDkRSp8+fWxxZtasWW7RokUJfjdkyBCrjGjatGmY9lYIIYQID+REsKifUVaMWMh6VomM8xNbKYYDxhfMdZYuXRr2bQshhBAic/Hnn3+6ffv2uQ0bNrjffvstoi47dG8QSv3vv/+6Fi1auKlTp7ocOXJELA/jpptuslywLFmy2JgzqaJREUciBX901LGkwN4J6KK44oorEvyOtmvS0z/88EMXTviiIFAAGRgkywshMg+IpljQERZ099132yJCpIKLQrnkkkvM9g4GDhyY4HccKy+88MKw7p8QQgjhN9u2bbPiAc57TBxPFYoBqCKkUu7hhx92L7zwggs3p59+unvnnXfcV199FezmEEIIIYTwC9xhGAPRsZA4Izic6xv169d3r732mv3cr18/N3r0aLP4jwTYaNNd8sMPP9haCsWgkcomiyViXqTo0qWLBY4kFhsQL7zbEnuw035EDsXBgwdNNAgn/fv3tw8n9lIEtwghMh85c+a0Sks6Fnr06BHWbXP8Y9GkYMGCbt26dQl+98QTT9j/KP0EbScFx1YhhBAi1uF8SFcjxU6VK1d2rVu3PqXnI4yxcePGVsV3zz33uNdffz1sIdVULg4ePDj489lnn+1uu+22sGxbCCGEEJkL1lFxYvj555+Dt2GXef7550es6OSGG25wU6ZMceecc44bN26cdbOGaxyWmJ07d1oHBe8Pjj+s+zDWFHEuUmDXRBAsHl+lSpU6wX+dCuUqVapYmnviCiM8Wj/++OOgD3s4WLBggQXo0fadEX63QojYpWLFiiYWEFpNRk244ETNsYhKAyoLQuE4yqIGFaBkZ4SCDQbHU7wcd+zYEbb9FUIIIfzyK/7mm28sMJsuilPpbsZWifPnsWPH7H86GZhvhIO9e/famKJz585h7xAXQgghROaD4kbWNulcoDgjksyZM8dde+21llFMIThFI3fccUdE94mC0EcffdTVq1fPuksKFy4c0f2JJU4LsBoVo2DnRJcEH8hQD3U6JPhQ4ItGyxFhsZHm6NGjrmzZsuaFxocU73chROaGKk4Eyx9//NFOqlhAhaviku1x8k5cXfDdd9+Z6k9bJBUJoR1f7OvMmTNtQPLss8+GZV9FbIDoReXM77//HrbPsRBCpJfNmze7MmXKuCNHjrg33njDtWvX7pTOqVTv4cVMOOPkyZOtii+c9OrVy02bNs198cUXLk+ePGHdtog94uGcHQ+vQQghYhXWLxjzsCYbydzKYcOGuQ4dOphQUr58ecskvvjiiyOyL7t377b/8+fPH+w2Ya0lXEUr8XLejtl3C21lxIgRdv3BBx9M8Lu33nrLBAraaurUqZPgd6hqkdBlsHQhxI7KKgkUQgjghDVy5Ei3f//+oNVSOCBjAkuKpNofWWihIpPOCQKzQ/GOtexzpCsmhBBCiFOxeUKgqFatWjDkOj1ggVizZk0TKJgcIxKEQ6BgLhMa9E0II10hEiiEEEII4Qeh445ixYqZPXSkBApccxAnGMOxLsF+UGwZKYGCdWbGgayxHD9+3G6jQ1cCRdqJWZGClh6+FNmzZ0/wxaDNGqsnoMsidBGODw52JXiBhXOBbf78+RbU/dxzz50Q4C2EyNwUKVLEKhCGDx/upk+fHvbtcyzEM9GDY6aX44MVBl7dHg0bNrTQp127dpnfoxBCCBFrrFmzxi1btszmEIju6Z1A/vbbb65WrVpmgXjZZZfZeTEcFd1Mflu2bOnuvvtuE1y8c3ekgiGFEEIIEd+Qq4swwfgpNP8qEmBzSYEInbCMf7DQ/uijj8LexQqMwwjorlq1qvvll19s7YTCFZEJRQoW9KBZs2Y2yfB4//333a+//mp2T4lVva1bt7pzzz3X2rvD1c2AzRPVWqS6E/IthBCJeeihh6yak9BO2uDCxcqVK91VV13l6tata+2IHnhL0m2BdZ7XsQZZsmSxhZHQY7AQQggRS1x55ZVmeUg2XXo9gsnD49y5du1aa+vHagkRPxysXr3affLJJ+7LL790CxcuDMs2hRBCCJE58fIqKVRkQT6SkAFGxwLdo6wDY+/UrVu3iARkI5YQLfD000+bWNG8eXP3/fffB+2eRCbKpGBikCtXLms3WrJkiX1IgUU2OhXosBg8eLDr1KlTkmHbfIDDNZFAmEDhW758ubv88svDsk0hROxB/gMLJ1RGhksAoAqUigiqSMnKCT1GUV3apk0bV6BAAbdlyxYTKGDjxo0mYPCYn3/+WdYSwpA3tBAis4DFAGL+1KlT3QUXXGD2AqVKlQrrPnz66afm60snhxCZ8ZwdD69BCCFiBYoXBwwYYBlYkeqgoCAd+2ncc+hgRaCIlFPN3LlzrSge4YYODtZ8KU4XmTSTgm6IdevWWUhKuXLlgrfjA4tAwYSBxbWkwKs1XAIFNk+IJX369JFAIYRIEao5sX2ic4GKzHCQM2dOO24iQiQWUe+9916XL18+O/FSberBgABhmGoBHiuEEELEAuTCYVdwKlDbRdcjAgWT0kmTJoVFoFi0aFEwkBHuuusuCRRCCCGE8AUKwhnreOTIkcO6KCIhUFAc8thjj1mnAgLFbbfdZuOiSAkUjAU7d+5s6ySsobAvEigyeSdFchD2SntN9+7dTRjwmDlzplUDh7OTgS8SAkq2bNlMrCA0RQghUoLDcY0aNcyaDs/sSPgqhjJw4EDXtWtXW4DBGsNro6RqAU/sOnXquPPOOy+i+yiiA1U0CiGiGYqYsDdkcktG3Y033piu53nqqaeskpBxPXZLtPn7DVkXjRo1ciVLlrR9p1hLiMx+zo6H1yCEENHIkSNHrBACp4UJEya4Bg0aRGxfcG5o0qSJda1Cjx49XO/evSMeSL1hwwazwKIoPTR+QGTSTorkvNURKM466yxLeQ/NhGjRooUrXbp02KqT4fXXXzef2qFDh0qgEEKkCkQAWgV/+uknEwgi4fEYmolBVgZCK4LJggULEgRoU8UpgUIIIUS0Q+cf3Q8IFNWrV3c33HBDup7n1VdfNYEC3n777bAIFIDFIudbuhvjqLZMCCGEEFEIhZIUKTL2iGRhBMIEhd/8z6I2gslzzz0XEYGCLDBvDOiNzXDAkECR8cScSEE1ET6wfEBDGTVqlP3P7/LmzRu8HZXmmmuusU6Km266KWxqH15tbdu2TWBHJYQQJ4OOL9oH+/fvbx0V4YLqUI6VVAR4oHQjRngZFUIIIUSsQcEQE1wm2kwo0xOuOGbMGLMagOeff961bNnShYuiRYtaVzZdjJoMCyGEEMJPGCdRdP3DDz+Yy0O4oSCDDoWqVau6X375xQrOFy9ebIWSkcjiwGaK3FACstkP4S8xJ1IwSfjqq6/cnDlzEvilffDBB3adSqlQLrroIhvUE1wdLv+0J554wmXNmtX17ds3LNsTQsQXWNblzp3bderUKWzbvPbaa21AsnPnzgS3e8dUjr1//vln8Ha8sTnGYQclhBBCRCPbtm0LnqcQ/4sUKZLm55g9e7ZNUJk0t2/f3nXr1s35yaFDh9ydd95piwMexYsXd2eeeaav2xVCCCFE5oS5/qOPPhrs2GTMUaxYsbDvB64O2DtRtPnvv/+6e+65xxxzyMUMN8QGYBVKYDfdG4gUV199ddj3I7MRU5kU5DzQJXHgwAETKbzOiLFjx1q1L90S27dvj6i9El6xVapUsc4OhacIIdLLuHHjbJFi4sSJrm7dur6/kZwKWMxJvIDD7XR3bNy40bopWrVqZbcvW7bMOsVoB927d688sjM58oYWQkQbnL9q1qzpZsyYYRZPzB3SahFAHhOP5Rh3xx132CTe73kGtrVYPyJMrFu3TuKEyHDi4ZwdD69BCCGigc2bN9t8H1GANQjGO5FgxYoVtv5BjhgiCd0UFIekpwP2VCAygIIUbD6B8dh7771nGcgi/cRlJsU333xjAsWFF17oKleuHLzdsyGh9dqbOPAFGzZsmFUjhVNE4UtUoUIFy8EQQoj0wuCA9koqGvDR9htO/klVmHK7J0yEWj6VLVvWFS5c2E7iX3/9te/7J4QQQqQFsugQKOik5vyVVoGCfChyJ5hUIVTQtR2OQii6FMnO+PDDDyVQCCGEEMJXWITH8hkXh0hYKlFUwjiNdVQEioIFC5pNJ0Ub4RYo2Jebb745KFBg4U9xpgSK8BFTIsX48ePtf7443iQBaxIvEDu0c4HMCj5QLKQRmBcO3nzzTQvLpvop0mnzQojYhhPykCFD7BgXmhMRLu/FlStXBn/G5oJjLp7Y69evD+6fV2XhHZuFEEKIaIEuCoIOqcRLq00AVV4IFAgVVBd+8cUXvtrG7tixI3g9R44cJq5cd911vm1PCCGEEJkXxjnM+T0QKBgvhduV5vDhw7aO26ZNGyvMrFOnjokCCBaRgDUO7K6JDZg8ebLlmikPLLzEzEo6nRFeWHZo+9E777wTVLtCPdOyZMliExI8zMIhGPAlJ2meL5fCsoUQGQELI3RSDBgwwO3Zsycsbyre25dccomFQ3lugPny5bPFGsDKzqNRo0b2PzlBx48fD8v+CSGEEKmdaOJrTNFSWuB8xlwDqycmqVOmTHE5c+b07U1nAkwVIxl6QgghhBB+gkU+zjSNGzeO6Bx+zZo1VpDx7rvv2prt888/b1bXuXLlCut+UIiJbb/HAw88YIWZCCYi/MSMSEFYyq+//moeVqS8Ax0S3oJZ4sDs+vXrm48r4Sbh4MUXXzTbk169eoVle0KIzAHHMCoaOGmHA09kZaDwyy+/BG/3jrEMIrC2AyocWMDBCmPWrFlh2T8hhBAiJRYtWuT279+frjcJcZ6CI8ISzz33XDdp0iSzNvQTOhc5r06fPt3X7QghhBBCYKFPFiUuMLg2RMre6dprr7V9oCCScRfrHuF0pKGLg4BuLD3vu+8+W9PwilxYdxaRIaY6KW688UbXoEED65LwMir4chG64VX0hsIHnFBXv/n555+tNapjx44uf/78vm9PCJF5oHrzySeftErLrVu3+r49TshLliyxRRMGDB50UuTNm9c6Omh99I6xt99+u13/7LPPfN83IYQQIiWwLsAW9oorrrAAxrRCsdH7779vxQGER4ajOxprR/InXn/9dd+3JYQQQojMDZb4OCEsXLgwgRtNOCAz+N5777WCEIq8a9eu7ZYvX27OOOGEyIDSpUvbOq7nzBOumAARJyLFTTfdZC04o0ePDt7mhbhi6ZQtW7ZgMj1t2Z5NSTjo06ePy5o1q+vatWvYtimEyDwggNL22LNnz7BsD6u8xCFVZ511lmVTJA7QRiDGPzvcoVZCCCFEYhD1KR664IILXIkSJdL0BmEhy5ge3nrrLZs4+wGd4XRge3OVM8880+YyOo8KIYQQwg8Y1/z444/Bn3GnIaA6nCxdutSVL1/effTRR1YM0r9/f+tYzZMnT9j2gU7bFi1auFq1alnBOzbXrB/jFsGahog8MSNSeHjtP7QoeZW7rVq1Cv6eDzoVv4899lhY9gdRZMSIEa5bt276UAshfAHLCao7qbQMDbT2G6oJFi9eHPzZO9bSScEikDfA2bdvnxs+fHjY9ksIIYRIDB3WjMmB/9MSdI3NAB7EgN0AFX5+QNUgPtCIKa+++qov2xBCCCGE8HjllVfcww8/bOukf/75Z9jfGNYUXnrpJVexYkW3adMmE0coQKfIO5z2TlhZ02n73nvvWWEI2Z/kYvhVlCLiWKTYtWuX++233xLc9sUXX7i//vrLWnSuueaa4O2ocKSv33XXXWHZt+7du5sne4cOHcKyPSFE5oRMCII1EUTDAQOYUqVKWe7Eli1bgkHeDC6w38MGA6iC4CKEEEJEChb/PZGBoGw6sFMLE1SCsv/55x939913B7sp/AAbWibFnM9ZLBBCCCGE8JMmTZq4QoUK2XoCxY/hhO7RunXruscff9zytxhvYcdZqVIlF25YtyVCgDUOwrIpFmHtWEQXMSFS9O3b1+XOndsNGDAggUgBJNKHtke/8MILbvfu3WH50C9btsyNGTPG9e7dOyzZF0KIzAt2SxwL6WL47rvvfN8eJ2wGM+edd54t4HhwzA09BoeSWEwWQgghwgHCAt3NZMPRVZ2WqjrEgt9//92CE7GV9aOqDwHEA5GCOQTWikIIIYQQfo47yJlcvXq1dXGG01py6tSprkyZMvY/3a3Dhg1zY8eONUvOcEBR+/PPP29jPY+3337b/fDDD1aIKaKT02OlfRvfVqp44ciRI+7rr7+264TjJYZFtXCAIFK0aFHzNBNCCL9BILjyyivtZBsu78qdO3e6evXqBW9r0KBB8LiM7R6sW7fOBI2rrroqrHlAQgghxKpVqyzjAd544w13/vnnp+pNYT7B+W3Hjh3u0ksvdZ9//rllzGUknBOxa8RK4Pjx48HbVbknhBBCCD9AkChZsmSCwsZwjjuOHTvmOnXq5OrUqWOdFLjfLFmyxD344INhE0lYL2a7ON907tw5eDsCSZYsWcKyDyJORQo+1OvXr7cPM605MH36dGvr9hbFvEWyrVu3hm2/NmzYYJkY+KgReCeEEH5DdSd2T5x0qQDwm8KFC58g+hYrVsxO+Fg+EXQFHIvJqMCaLzSQSwghhPCbAgUKWMHQ7bffnmTxUnL+yPfdd59NmnPlymVdivyf0RDK+PLLL1vmxcSJEzP8+YUQQgghQhk0aJBlP7BWGe4CQgSSa6+9Npi79cgjj7hFixaZxVI4oMCSwk6KQ+iwpYsktWNDER1EvUgxZ84c+x8xImfOnHadSifgw+YpcSzc0dUwdOjQsOzXwIEDzdNMXRRCiHBy55132rEuLXYWGcGePXuC170TvXcszpYtm7v++usTHLOFEEKIcMD8gPb9Tz/9NNWPIRx7/PjxVk03YcIEy4jwgyJFith+EeTdqFEjX7YhhBBCCOHx5ptvmjhAcUS4Ohco/hgyZIjlBSNUkBVMQeNrr70WFmt8rJ2IB8B9h2JyMjMfe+wxK3gPV16xyGQiRZUqVYLeal999VWChTIqemmh5gvo3c9vdY5EeNqGMrotXAghUoLOLfwkCa6mo8tvCLiqVauWVSF43WresRd/SbrawDv2YgMlhBBC+A3nn9AKwdR2No8aNSqYczdy5Mhgp3ZGQUdhaFchdgdt2rTJ0G0IIYQQQngCwYwZM4JvBgWEiANekbff4KbAWIfMLcQCsr6w4uT/cPHSSy+5p556yqw8GdeR/UUn6//+97+w7YPIJCKFt+B188032//z5s1z+/fvty8cAXeASkabNl8OvNfC8QXAAuWhhx7yfVtCCJEYOrjy5s1rHV3hCOxmEYjBz6xZs+y2cuXKuYsvvtgdPnzYLCxCj9EIy8qlEEII4TfNmze3CfD27dtT/ZjZs2cHx+89evRw9957b4bu09KlSy2Mkf1iviKEEEII4RfM0e+55x53yy23uHfeeSfsbzRB2GRmTps2zcKxX3/9devgoJMiHK/do0OHDu7qq6927777rq1HsE8iNolqkQJ7kbVr19r1m266KYG9CEF3iSumqPT1m3379lnLNl+CcAV0CyFEKAwA6OR6//333U8//RQWX8stW7a41q1b2890rSW2fKpYsaIJGnSahTMfSAghROaDrmo6CsmpO3jwYKoes3HjRrNcoiu7adOm7tlnn83w/cqfP79VMDJHoBNRCCGEEMLPzEqsoJmHcz1c/P7771YsgpXSgQMHXPny5a17oX379r5bTB06dMjs/hFmvOJIOiYoFGGfwmVxJTKhSMFCHG3YtO0QZscHMDSPAvbu3Ru0GwkH+KwBrUxCCBEp2rZt684991zr7PIbMoHw1Q7FOwZ/+eWXZrnHvhCSBbJ8EkII4Rd//vmnFQsBgn2ZMmVO+pjffvvN3XbbbSZo0OkwevRoXyaxFEzRYUgVH9l1QgghhBB+0rdv3+ACfTjAXYFOBQomEUaeeeYZN3/+fMuD8LtzgvHbZZddZvmc7AfFKh4SJ+KDqBYpUMNatWrl+vXrZz+vXLnSbdu2zYJXatasabfxhWASEI7WpmPHjlkwN/uUO3du37cnhBDJQZXmww8/bEIu1QThAkHC627LkSOHCcULFiyw25o1a2YhXWqvFEII4Rd0QOzYscMVKlTI9erV66T3p6PhzjvvdJs2bXKXXHKJFTxRCJUR8NwIJt9++23wNkK4Ee6FEEIIITKa7777zgoWPbsjhIJwzL/Je+jYsaOrXr26uScUK1bMxj+IJFmyZPF123PnznXXXXedrcX+8ssvNtb64osvrJtCxBdRLVIkxuui4INIKzWdFYsWLXJ//PGHK1y4cFj81liQ86q3hBAikiBSMFigisFvCMGifZMsClo6aSmtW7dugmNzu3btLKTL66gQQgghMhIKlgYPHmzX33jjjZOKAcwVGLdTbZc9e3aziSLTKaOgm5H9uOOOO8JaMCCEEEKIzAedoczBhw0b5t58882wbZd1V3IpmesDIsny5ctd5cqVfd0uHbCNGze2MGy6RShkx4p69erVrn79+uqeiEOiVqTAL5bsh++//z6oEGIrEmozQjvPDz/8YNYiXmaFnxACQwdHiRIlfN+WEEKcjIIFC9rxkGOT32HVVEdQwUDlwmeffXaC5ZMQQgjhJ8wHmBTT0Ue2hCeUpwST6eHDh9uc4aOPPjL7wozEqyikq1FZdUIIIYTwk5w5c9rcn3EQXQXhKFTEvaZSpUpuw4YNZms5efJkc5ih+MNvGFtt3rzZukUefPBB64rt0qWLy5o1q+/bFpHhtIDfK1vpZN26da5kyZJWIUWnBBe+kOzu7t27wxKSHcrixYutvYjFOEK7hRAiGsD3+uabbzYP7GrVqvm6ralTp7ozzzzTtsNAgcAsjsssHBHgXaBAATtWr1q1yto/5cedeeDvfv7559tnggoXIYTIaDjPcP75+eefbZ5AZ19KTJkyxXIoOEdRdcekNiNArA89vzE3kQ+yiCXi4ZwdD69BCCFSw/Hjx61bk5zecI496JQg54K5PTRt2tS6R5n/+/layZ247777zD0HlixZYqKELKVjm9Set0+P5nZu4IPIYti8efPsi3jppZeaQBFubYV2Knxsb7311rBuVwghUoIusiuuuMK99dZbvr9RtWvXdjVq1LBjMnCSKVu2bNAbE2jHvOGGG9zEiRP1hxNCCJFhIEowP5g2bdpJBQpEDCbTCBRUGhKwnRFwbsMHOTQLTwKFEEIIIfyyO6pTp45dsHkOx9iDvK0+ffqYhTMCBXm848aNcx9//LFvAgXru9jrU6hO16xn7QnXXHONBIpMRNSKFJ5a56llXiCdZ+tEWzWLY/wfjgMDX8gHHnjAnXHGGb5vTwghUgsDlIceeshNmDDB/frrr2F/47xjsneM9o7Z3jFcCCGEyCgIvK5YsWKK99m/f791PVOxhYcxlgQZNZnHhvbw4cM2kY7SZnQhhBBCxAn79u1zK1assOILchj8hmKQChUquJ49e5oFP7ZSa9assewtv6DYkbHdXXfd5X788UfrVj1ZMYqIX6K+k8LzjvUWwJhsAEnufFlpufabDz74wLzYWrdu7fu2hBAirdCGiQ1TOERbBkrYZjz66KMJjsneMdo7ZnvHcCGEEOJUbQ1feeUVmyynpvrPm+QWLlzYMpTIVMoonnvuOcvM+/zzz9VBIYQQQghfoXsTy3kW8rGf97t7gq4Fcn8vuOAC9+GHH1oHRZ48eXzZJqILRSUUPS5cuNCs/p999lnLnWjRooUv2xTRT9RmUjCx2L59u01Mypcv73LkyGGTky1btrgiRYrYQtmkSZNsgaxo0aK+7gvbx+qJSmUhhIhGOJHPnz/fbdy40deFE47LHJ+xfKJzg1OIN3DZu3ev27lzpytXrpy1gnKclg1G5kDe0EIIv7yJy5Qp49avX+/69u1r4Y0p0b59e/fmm29amCPnxFP1L6ZrguBtArI9q0MhYp14OGfHw2sQQoikoCCbNU+v+M9vKP5u2bKlZVBAgwYNzEra73zJJk2auE8//dTcatq0aeN69+6tTMs4JqYzKdhpFsKAyQWt1QgUtPywOAb4orEo57dAQVsVSiKVykIIEa0QLrV582a3ePFiX7dTqFAh84l87bXX3FlnneUuvPBCy8SAuXPn2nUGGr/99puFmwohhBDp5eWXXzaBAjG8Xbt2Kd4XWycECsRxqv9OVaAgz6JWrVqWZ9GjR49Tei4hRPL079/fvredOnXS2ySEyNTQuXD77be7unXr+m7ljFsMtk50TyBQUGT40UcfWXG2HwIFBYyhTjgUn9D9unbt2rCIIiI2iEqRwvNaQ5SgzSg0jyLcVblMcujiUGC2ECKaqVq1qsuXL5/Z0/kNC0FUq6KEJ86lwC/8sssus59l+SSEECK9ULCEvRJgM8icIDm++eaboA3h888/7+rXr3/KbzydE2Q+URileYAQ/kBxzbBhw8JWMSyEENFM9erVbS6NDVKuXLl8286iRYvMMQaLJwrCEUYQC+6+++4MX3M9dOiQ2ThRYN61a9fg7ZdeeqkbM2ZMcO1AiKgVKWjrnjlzplXqQqhIwReoVatW7pNPPjHfND+hggqR4s4773RZs2b1dVtCCHEq0L3AoCIcx8bEKDxbCCFERkNV9dGjR+0cc++99yZ7v61bt7rGjRvbHOGee+5xTz311CmP/0O7FPFGrly58ik9pxDiRP7880/XrFkzy3lJSYQUQoh4JnTcwbEQJ5k33njDMiczGsZVTz75pAVVE4iNKwKWS2R45c2bN0O3dezYMTd48GATJ7ByQqzAqYYODiFiSqTAR7ZatWqm5vEB5ksKTFIIjBk9erR75JFHfPeGxct227ZtNngSQohoh2MVuRAzZszwfVsMcCZOnOiWLVsWDM/mOl6DWPENGTLE2lSFEEKItML5hXBqJuiehVNyi5x4J+/fv9/sCt5+++1TqgAcNWqUndOOHDkSvI2OaiFExkNXLmPFGjVqnPS+rAkwxgy9CCFErLN79253/fXX27gndNzhh4MMXad0rb344osmjFDYQfcERdkZuT3yxOiQI/Qby0xsnuiWQAxhbVcF4CLmRIpQlixZYgocrdaXX365BVjTIoQvLZXDfkIXRcGCBYMLcEIIEc1cffXVlgkRDssnwktpQ0WM4DhJuBeDHcRdbDE6dOjgSpYs6ft+CCGEiC84lzCpBf4vVapUsvdDFF+1apVV/+GhfM4556R7uwcOHLDqQs5jiB1CCP+g85fcx379+qXq/twPm1HvwthTCCFiHdxjWPPEstIvNwQyf8mUxB6aDMv8+fNbODfrnayzZjSsD7C9Xbt22bGaMRVdG4ghfheai9gnKj8hDEKoZDp8+PAJeRTFihWzcC08zfwE9Q9/NCqT9UUSQsQCHCOxxKD6lOpSP0GgIDeIS1KWT0IIIUR6YNyN7QBhiikFVhO4OH78eJclSxb73zsfpRcsFpi09+rVyzq2hRD+sHPnTtexY0dbICPLLDV069bNFtq8C88hhBCxDpkQLOhjd3/WWWdl+PN/9dVXVuxBZwM8+OCD1j2REdldoUUjoYHYbdq0sWJFBBgsM1u3bu2LdZWIT04LBAIBF0XgU/a///3PrtPGiUjAFwsvM7xpwwXtVizCUZ1VunTpsG1XCCFOBSzq6Gpg4kcLp18wGEEU8VpDR44caQOSKlWqWCsplag7duywrgrvmC7iF87XVDaycKC/txDCb+iaaNSoUfD8Q15desCCAJtEuhCFyCxE+pxNMQ22zqGuCP/++6+NKREpsXY6mWNCpF+DEEKkl7lz51rWlR+WTh6IBnRnjB071n7GeomOBubqGbkewHiMvAk6WRcuXBh8TSwz+/n6ROyR2vN21HVS0BIE7PR5551nIoFnY7J06VK3cuXKBMEyfkElFWnzEiiEELFE4cKFXbly5dyXX37p63aYRIYOPDhGw+rVq21Q0qRJEwvy3rBhg6/7IYQQIj7g3PHjjz+e9H6cZwi0Bibg6RUoCNwmOLJmzZrmCS2ECA/Vq1e3Of7y5cuDFzJlKE7kut+WzkIIESnoCsNO/pVXXvFtLEXxBsUXCBQcT7GyZB01owQKtsF6afny5V3jxo1tXLZx48YEYzgJFCK9RJ1I8dNPP9n/eJfRVUFVMCAWdO/e3ZUpU8YC9PwEEYROioxsgRJCiHDBsWvq1KlmWxcOfvvtNxsIIVwQXkrlhme74R3ThRBCiJSgGq9EiRI2gU/pfNOwYUOzhK1WrZp76aWX0v2mEkyJ/QATeIXwChE+KERkbh96Offcc12uXLlUICiEiGu8DAg/rJmxVmJshLvBwYMHTURYvHixGzBgwClldoWKExRC8ryMxRCVOZ5jzUnhB90aQsStSMECF+EqkC9fPhu0ZM+e3QYwN9xwg6/7QHANi2wSKYQQsQjHLtrovvvuO1+3gyBx2WWX2TEavIEJ1RSeSCHPYCGEECcD0QFbVyxfkvMt5nd06FGpR9cg2XGn4nFMBsWUKVPc999/7y6//HL9kYQQQgjhK507d7ZxR0qZW2mFwkRyuq688kqzXUaQGDRokG3HczvICBgzNWjQwC1btszWZikqQZx47rnnbEwlREYQdekl3oIWC1ye1RNfNqBd6Z9//vE9yJoMDL5klSpV8nU7QgjhB2XLlrVjKMcyWur9ImfOnLaw9Pfff1sLKcdqWj05dquTQgghRGp5/vnnbQ5QqFChZDspuH3atGkuW7Zs5mnvVSOmpQKwf//+VqlN7hwgdggh0gfZEVmzZs2Qt4+FNSGEiDewPqbr84033rBgbGyQrr/++gx7/nnz5gXDsAELy6FDh7qiRYtmiMMMzjbec9WuXdtVqFDBVa1a1cSWtI7DhIj5TgqqcSE0F4KKKb9FClqY6tatqwR6IURMwuCHBRiOZSzK+LkdrPGw32Cw5R2rQzspZPckhBAiJRC3qfiDV1991USIxHz88cfuxRdftOujR482+9e08sEHH7inn37aujF0bhIifVW0LVq0sAUrFtv4rpIjic85QqOyXYQQIqGQW6NGDTdixAj37LPPZuhbg53Tww8/bC4zCBQXXnih+/DDD83y+VQFCsSJzz77zLowyO46cuSI3c467Pz5890LL7wggUJkTpEitJMiHGHZsH37dqsI9iqshBAiFuEYRvulZ5vnFwxe8PUO7XpTJ4UQQojUgJD+yCOPWEferbfemqTVKrYCrVu3tutPPfWUu+uuu9L15jZt2tTVqVPHvJk9IV0Ikbq8GOw9CamnYLBr165u/Pjx7uuvv3Zvv/22iRQzZsywhbG2bdu6vXv36m0VQmR66DQjT7dy5cquY8eOGTZu+uSTT8ym8q233rLbODavX7/e3XPPPacUWI2tJkUhV111lQVisy569OhRG4d5KBBb+M1pAT/LbNMBi2pbtmyxLx2LXwxyFi5caF8S/M6pgsqfP79v23/99detdYntnn/++b5tRwgh/OTYsWNW4UDVKJdwtbNy7MYHk4nrTTfd5IoUKWLHdBHfEDrLOZMsFKoqhRAiNVCpxxifiTyierFixRL8ft++fe6aa66xIiIEBmwMCbpOS3YS1oTepJqiJ787soWIt3M2lbTdu3e372BK359du3a5IUOGuLx587rHHnvM+YnGHUKIaITl1QMHDtjYwyOjxh7Mqdu1a2fzbChRooQJFTfffPMpPS+FIh999JF1SNDdCpwbEFbICwt9LUL4fd6OulE6C1p4qFOlgVDApIIvND61iBW0MfkJXre0TEmgEELEMmeffbYdS6dPn+77tt5//32zz+DEw3apuOB/KkcQfkX4wfcUr3X+DlhxLVq06KQtw+3bt7cQdBYLqZicPHly2PZXCJE5+fPPP22i8uSTT54gUJBD16RJExMoKFRiAp0WgWLJkiVmQzh48ODgbRIohEg7CxYsMCvkk31/ChQoYLkvfgsUQggRjRBg3bJlS1tPRKjIqLEHz9uvXz9XqlQpEyiyZMli9lErVqw4ZYECECbYb/4nm5cgbMZe/C+BQrjMHpzt4Vk9MSkpX768W7dundu0aZP5X/oF7U3ffvut69Kli2/bEEKIcEGoFdYYdFWwWO0XtPwTYkr3W8mSJd0PP/xg1n34ZIrwM2bMGOsIpLIGgeKVV15xtWrVsk6XPHnyJDnwveWWW+x348aNs0UGBqaejZcQQvgF/vYcn5KqqOL8NWvWLHfuueea3Uxaj0lz5851v/zyiwnpHTp0sEm9EEIIIYQf0L05c+ZMG3swBskIC3nWJ7HRYz0UqlWrZsHYFJSll8OHD1vgNiHbgPjxwAMPWLEInRrnnXfeKe+3EHEhUvBlHjZsmH05aO8GKqDopsBChIufoETSepIRaqQQQkQajmUEdtGFhl+wX9x3332uXLlyrnbt2mbXgUhBePbtt9/u2zZF8rz88ss20Lz//vvtZ8SKSZMmuVGjRtmiX2K4nfBzgtC8QgC6MIQQIhxcdNFFJ9yGJ/JLL71k1999912bD6QVbAroDGvWrJkECiFOAQof0jIGEUKIzAgd6cy5fv75Z5sXnwqshz7xxBPunXfesZ9xlGFcdO+996Y7F4LOeZwOKGCj02Pz5s2uUKFC9rvhw4ef0v4KEZciBWEvvXv3Nm81wmVCg1jDwTfffGPVxtddd13YtimEEH5B6BUtmxzb/BQpGjVqZJfE4dmEbHFcv/baa60rTvgPXRFLly513bp1S9BiXKNGDbNrSIovv/zS/Kaxe/riiy9sEEzwGsGYyVmrIH5x8cDqSwghUgth2djHJDWJX758eTAom2PZHXfckeqOaCbZbdq0McGVSbw6+oQ4dUJDU4FiFOzYmLMDFiGMF3A/EEKIzMT3339vmROVKlWyn8uUKWOX9MJzUUDGPIwiMnjooYfM7ol5fXrYs2ePCRPYAXtzNmz2sdT3RAohooWoyqSgiwH48mHtBCyWPP7442HxxmYhD3GEqishhIh1WJxGnODYFi6we/ImrAyuWOxm8CbCA1U3LNQRWhkKP9OtmFwIGzZPPI5zbY8ePaxSp2/fvsluh4Ey2U3epWDBghn+WoQQ8cnEiRMtr6h+/fpmDZjYKoEuPLKNsIHq06dPqp+X6kJsCrB2EkJkHLNnzw5esC9hbMl3F7GCCwtdWIwiPAohRGYBSyeOfYxnmE9lhLMLeRZ0xCNQUHBIpztd8ekRKJgXPvrooyZEMHdDoMDaCRtM5upsS4ho4/RoC88DvGe9SQtfJBZLevbs6eu2vTwKWT0JIeIJjmlU0JNL4Sd4WxLOTPUH7Nq1y47locd2EZ3wNyOPggpkqiAJqn3mmWdsQJwcVDdTWOBdWKAQQoiTwbkIGyYgXPfiiy9OMBZH2N62bZsrWrRomoOyeSznHTJ2hBD+wLw8cUUv1yls8CzahBAiM0AeI3aUFSpUSDL3L7UcOnTIbPWYhzFvz549u1nn0R1Pt3t6YQw1evRoG3vhFkOG5MqVK62o48wzo8pUR4ggUfXJZJELsmXLZgtcgNL34IMP+u6PTWu58iiEEPGaS0E3g58i7NNPP+1ee+21oLUGlRteWLd3bBf+kzt3bhuQ/vrrrwlu5+ekfN89/1SsUUIXA6+44grrvMA+KqmwWToO1XUohEgrLGJSbZg/f37XvXv3BL+ji2vatGnunHPOsaDsnDlznvT5AoFA0JuZCu+tW7daF7YQwh8oINy7d+8Jt3MbC21CCBHPhI47KIz4+uuv3f/+9790LfrzXJ9++qkJFLt377bbGjdu7AYPHpygiCO1MN9n/NS/f3/bRwRkbJ5YSyVwO71ZFkJk2k4KbyGLLzgLI3yJ6tSpY2Haof7afjBnzhxbUMM7XQgh4gUyIhigcIzzeztYCp133nm2wBSKRIrwgaBAFc7MmTMTdErwc3KVONgcEpzmdcEALcCIF0kJFEIIkR527Njhnn/+ebs+aNAgO194jB8/3qqzYeTIkWZxcDIQNDiuEf7oIYFCCH/Bju3++++37yzOB1w+++wzy5Hx8smEECIeQYjF1u6DDz4I3kZBRXoEig0bNriaNWu6pk2bmkBRrFgxN2XKFDd27Ng0CRQIHTwOGz7GRAMHDjSHGA+OzdWrV5dAIWKGqBQpPFjwCtcCyeLFi125cuVUGSqEiLtcCto7Ocb5SatWrazyfsCAAcGBFdYdIJEivFCNM2LECPfuu++6devWWXcLfwMWFaB58+YJhH9+j+8pFiyIE5MmTXIvvPCCBWkLIURG0aVLF8uauPHGG21S7sFxqkWLFkELqLvvvvukz0WHIEGSCxcutIpBIUR4wAqSIkLs1fA558L12rVruzfffFN/BiFE3PL222+bIPDII4+4gwcPpus5jhw5Yra6FPjNmDHD1h979+7tVq9ebcfR1PLPP/+YLWbZsmXdrbfeasIEnfHM99LThSFEtBCVdk984YBWcCwqEgeA+gF+b2k5KAghRKxAZf2oUaN8F0M8ChQo4DZt2hQ8lkukCC9kSmC7QJYTwhGD16lTpwbPpVQzh/69CL2mVZnFQaqX+fshWBB8LoQQGTXOHjdunB17hgwZEqzowzqGymyyi6gCROhODUzqv/jiCzd06NA0hWsLIU4NbJkRI1588UX3448/2m1UAHs5ZEIIEa8wP2KOixCQI0eOND2WjgcyITp16mRzMUBcwC6ZY2hawDYT+6bt27fbzxx/KdxInPUlRCwSVSIF1ZwIBbNnz3ZfffWVtYHjoU0uBcqiX5BFwcEGRVMIIeJRpKAynlZSxF+/8QZHXli3RIrw06FDB7skxTfffHPCbbQH42MqhBB+QLcyk/M1a9a4MmXK2G1YzNFBgeUB5w18makCTA66MJiYMy8ARFVECiFE+GFRLDW2bEIIEcv88MMPFpBNcQWFFunpGMNW99FHH7UuDLjkkkvcq6++6ho0aJBqG6a///47OEaigw3HGSwued527dqlKsdLiFggqkQKvqxcpk+fbj+TEcGX1u9OimXLlgUX8oQQIt7wjm1UsvopUrBYhMUQGRjA/7TFXn755b5tUwghRPTDeJ7JOBcPuiYQLpho42mfJ0+eZB+/f/9+C8bGkg5BtXjx4mHacyFEYpYsWWKiItXA5EiGQlaFEELEi70d9rdPP/10uro2sXYib4ucCI6VjHeeeOIJez660lIrcLz88svW9b527VrrJD3jjDNs/FSkSJETsiCFiHWiKpPCgwAuuPnmm639m0UvP2Hhji+3FtKEEPEI4m+uXLnsWOcnP//8s/mD053mdVIQ1kUwsxBCiMwH4gKXxFCQ1L17d7uO/RPZSSlB4RJVhGQdYWMnhIgMn3zyiatUqZJlyUyYMMG+l3RIzZo1y51//vn6swgh4gYEAbo+d+3aZXZNqYX7cnwsWbKk69u3rwkUtWrVMncYfk6NQLFo0SJ35513uhIlSlghIJ2kuM148NwSKEQ8ElWdFFRRschFlRTgi80XOLUqY3ph4Q7P7jPPjKq3QwghMqyClW4Kv0WKu+66y5UuXdpCmBcsWBAUnIUQQmROyLahsnrEiBHujjvusNvwUCYcm4l/q1at3AMPPJAqaxkm5wcOHHBXXHFFGPZcCJEU2IcOHjzYqouxZsayhGpe/NDz5cunN00IETeQPcHxjcys1NoysZZJsPa0adOCxYIcM8nfOtlzMC6aNGmSZf589913wdvJrqADg/0QIt6Jqk6KN954w77Q27Zts5/DFfrCwp2snoQQ8Uw4RAoECoSKa665xn7GBoDAZqrrhBBCZC4WL17sRo0aZcKCt3hJhx1iBd0VnCsY+yc3af/www/t4kFOnQQKISILYdl169a161iXkDvGd5jA1uHDh+vPI4SIWX799VfLySUDywN3l9QIFDjAPPXUUzYfRqDg+EjHKF1njRo1StVzrF+/3tWvX98ECvInyO1atWqVCRep3Q8hYp2oEim8cFWqcAEFkWqNtLRWpccnjtBswnCEECJe4RhHp9qePXt835YnMDPQq1OnTrIBzkIIIeITqgEJc2QMf++995o9DNepvkYwx4Jw3LhxZuOUFDNnzrTHUcW4cuXKsO+/ECJpyBs7dOhQ0PUA+xI4ePCgzauFECIWYYyCAEsOBeOXtDwOGzys48nawgKPzgds8MixSMkVZt++fe6LL75IYOHUpEkT9+STT7qtW7e6d955x0QPITITUSVSePz111/2/5QpUyx01U/FkCAaDizKoxCxBBXqnEQ56RE0SfvfP//8E+ndElGMd4xDlPUTFpO++eYbu+6nwCyEECJ6+eCDDyzgOnv27DZpB8b0dFacfvrpNqEvVKhQso+vWrWqa9y4sevYsaMm6EJEETfddJNlygB+6XxHsWzDwq169eqR3j0hhEgXrDm+9NJLJhSwtpIaEGkZr3D8I7cCa6gvv/zSTZw40RUvXjzZxzEfb9eunVlBcRzdvXt38HeMjxg3IQILkRmJqhAGWqJCIVQGD1o/8fIvLrvsMl+3I0RGQWgkAgW2B/Pnz7fq+ObNm1tLIJ1HQiSFN1DimOdnkHWPHj1scMbnkUqSpI7tQggh4pc//vjDsigAq4P8+fOb9ZPXVcf4vkaNGic8jupsxv2IGJ6QccYZZ4R9/4UQyfP666+bbRs888wzNt5jPoKNG993IYSItTHL//73P7tO5gMFdycbe9A51rNnT/fmm2/a2gwB1t26dTNxI7kOUYr35s6da0IIc2WvmK9cuXLmdMBYSQgRZZ0UDHI8WNRi4NOpUydft8mCHW2rtJ0L4Sec8K6//no7cV144YWud+/e6XoePA7Xrl1rVYoEvmOnQyshvs7Hjx/P8P0W8QGDJ6o1PGHWT1upG264IcEATSKFEEJkHhAhfvnlFxPHGcdjZ8ACJmOUhg0bmmdzYrA1YIzEpN9DAoUQ0QVd21QIe99NxES+zyy4sfDGnFoIIWIBRALGHFdddZWNWVIz9sDKkq7QSy+91A0ZMsQECvImyJ2gUC85gQLrp+uuu8460bB3YttYQs2ePdstWbLE1nSEEFEoUoQuZPndQeHBgh1dFAqhiW3IM+ESai/DZJjbPPuwxPflJONBxTe3eZVBJ7tvWqH9j1bAihUruhUrVrhXXnnFMlewPYC2bduaJUJKF48FCxa4K6+80uXNmzd4W61atawKgBOgEMnBsc5vkQLxjbCv0IlqqAAthBAifmEcxriLcTVjnTPPPNNsEHbu3GmTevyVkxpzz5s3zyb5/J6gbSFE9MH3mTlL4vmSEELEGnRvjhkzxm3fvt19/vnnJ73/woULXYUKFczejuKLK664wqzvPvvssyTtK0PXpXDAYJ0ma9as9ngKThWGLUSMiRSokHz5/fY090QKEdt4C/l8ZjwQAbgtcWgvGQ7cTq6DB10I3Na6desE9y1cuLDdzsTZgwl0WkFxR1QYPHiwK1GihGvWrJl75JFH3Msvv2y/f+6559zy5ctTvHig9IcKFOD9HFoFIEQkRIqkhGZ1UgghROYAAeLVV191GzZsMGtKqhRnzJhhGVrjx493559/fpKPIyR76NChtgigamwhoheqgUPnJUIIEYtg8YRQMHr0aBNfk4P1lfvvv98ECqwrzzvvPOsco/A0KetKOkMfe+wxc7vwwLVl3Lhxtv40fPhwEziEEDGQScFE5sYbb7Q0+99++80scb766it32223+bZNFuxotRLCTziJcWILrR6kqwKRgjZBhBMuQvgtUowcOdI6g2jR9xOJFEIIkXmhawILGC8ri2KN0qVLB39PEdKHH35oNlDYEUJKiwRCiOiAsNfOnTtbd1T58uVPcD/AOkUIIaKRH3/80TonqlWrZj9jS5lcwDWuHFg6Pfvss9Z1AS1btnT9+vWzzohQGNPgdsHazoQJE4IuHEuXLrXjJGjNUYgYFCmuueYaC40BbzH34osv9m17+/fvt4s6KWKfP//80/6nUs+D4CK8kGlNDsX7jHmTYmjfvr213iX2INy2bdsJ9+XklFawLsiRI8cJt3udQkzMyZhIzWvkpLho0aIEv/v111+DvxMiOTjWYcPBxDKpttSMgO9X/fr13erVq+3nVq1a2c9CCCHiF84tdK526dLFXX755W7z5s3uvvvus9/ROYrlUyjkzjHRx2Lh008/9V04F0JkDE2bNrX/H3300eBtzNuZ0/A/xVdCCBFtrF+/3gqisasjwLpMmTLJ3vfrr7+2dSQeA9dee60JFmRnJc7pwe4JcSJ0feaWW26x8RCh2EKIGBYpAP9/r5WUg4OfXuaoqFCkSBHftiHCQ1IZJljMJGUzk9R9+Zwl9VlL7r5phYE7FgahfP/991ZpiDCC3dPjjz+equeiA+P555+3xWCv+wI/RFoWS5YsmeZ9E5mHokWLBttQ/RIp+ByGftYrV67sGjRo4Mu2hBBCRAdYPNEtwdgdkZoOCbKyKlWq5AYNGnTC/WvXrm0WmPxeuXBCxA6MIYUQIhbnweR6MjZJzsGCAgs6xXBzAZxd+vfvb0WqSRVTcD9PuCVvAktvxA22I4SIA5GCFqnJkycHF4fJpfCT3bt32/8FChTwdTtCAB6EnPQeeugh98MPP5gaj58hpMXuqWbNmiZGUKE4cOBA80ns3r27dYNwchQiOfLnz2////zzz769SRy3GbCxYIUPudcBJIQQIj7hnNKnTx+7ji0CHRUrV660cQ1dEl7BiFdpDTfddJPZLnjnJSFEbOBXkYsQQmQ0oeMOxiJkY1FwmrgQlfkq9pSszWDzhBMH3WLY0YdmaW3atMmcNuiUgHr16lmXBRlcDz/8sOy7hcgAoqq3GrsbL5Q4qQp2PyZVHLSUBSDCQfPmzd3Ro0etSwhBoWPHju7BBx9M8/PQeTFx4kT7n64KwiZ5broxhEgJgr44tvopUgA5QgULFrTrLFSFK6xbCCFE+OnWrZtN8BnfHDlyxL3//vs2RhkzZkywEIhsLmwWKKzwkEAhRGxA93dq4RiwZs0aX/dHCCFOxt9//23FoRSGemC/HbrOSHYEYxYskbGgRKCgIJT5K4IFAgVCx6xZs8y+uESJEhaizXMDYgYOAr169dKaohDxKFKE2uhQaT5q1Chft8dCHQJF4swCIfz6fA8dOtT9/vvvFgyPZVN6LQ6oYqLriInA3r17zUpBn2ORGvLly+e7SAHeAJCg7gEDBvi+PSGEEOGHyfm7775r1ym8eOyxx+w6k/2bb77ZrjPBb9OmjZs3b555NAshYgu6t2vVquXGjh0btGZOzNq1a93TTz/tihUrZmGxQggRSciKGDFihI07PJv3UBYvXmy2xBR7Mjfm2PXFF1+4qVOnuiuuuMKyK1iPJLuievXq5hTAeKZs2bKWa+shy0ohMpaoWp0PzQ/YsmWLmzNnjoWu+gUHIxbshBAisxAOkQILD8K5PfzMFhJCCBEZqECkKxTuuusus3qiurBRo0YJcraYwH/88ce2gPn666/rzyVEjIEAQaEV9rL33HOPVR3TCYXF54EDByxclm6q22+/3U2bNk1+7EKIiNOkSROzk0dgCLWpo6OT8cjo0aODhXUc2yiy8KyzJ0yYYF0YFINCtmzZrIMCCyiOf0KITCJShPq9YWNz9913+55JoVZzIURmgmOel8fjFx9++KFVooS21gohhIgvxo0bZ50U2bNnt0k/4vSll15qE3+qDdetW2cZWlC8eHHLpxBCxB4Um7A4x2XJkiVu7ty5VpmMjS1VxizuVa1a1eXMmTPSuyqEyMQgmDLewGGCAgkyEj3++usv99prr1mG1qFDh4JdYgRjMz/G6skDUQOB4pJLLnGPPPKIa926tbvgggsi8pqEyGxElUiRN2/e4PWrrrrK1a5d29ftUU3MdoTwm2+++UZvsoiaTgq8wf2EwSGZFF43ReixXQghRHzQoEEDN3DgQDdz5kz39ddfW6WhF0pJZwW3sZjJIqYQIj645ppr7CKEENEEmZ1NmzY1+6Y33ngjaMNE0QS/69y5s9u8ebPdRtg1ggXHMgrrXnnlFXf55ZebPRSUK1fOTZ8+3WwrZaktRCbOpAgNsPbCaPxEdk9CiMxGOOyesALA7sNDIoUQQsQf2CLQKYEYAcOHD3elS5e20GwsYKhK3LRpU6R3UwghhBBxDuuH5HUiRHhdEXR01qlTx0Kvuf2iiy5y77zzjuVOUERBDkXjxo3t+pgxYxJk7tSoUUMChRARIGo7KQgX5uASmlOR0RBenCtXLt+eXwghog2OeRxf//33X1tI8ovQltlQAVoIIURsQ2Dk//73P/fTTz+5e++9125r3769a9asmV1n7E5gJT72lSpVivDeCiGEECLeIROHogm6H8jI6dq1q+VgMeelwxNbOnIq3n77bdeuXTsTNLy5cdu2bd3DDz9s+RRCiMgSVZ0UtFi1aNHCrjO5wevWLzhYcWDCR1cIITIL3jEvtFLED0K74dRJkTy0JHu+qIAVVzg6CYUQIr20atXKLJywZT148KC7/vrrrUpx2LBhCbKIJFAIIYQQwg+Yy2LhRPGdB9k4I0eOtHws8ihY88OakqKJAQMGuK+++soNHTrU1gGvvPJKEyywJ+7bt68rUKCA/lBCRAFRF5zNROfdd9+1n/0UELwFuvPOO8+3bQghRLThHfNYGKcS1g+WLVtmQjPUqlXLQsdE8iHjgwYNCv5dbrzxRrd8+XJXtGhRvWVCiKhj2rRp7ssvvzSvZ3yeqUBkcn/rrbe6f/75x1122WW2SCCEEEII4ae9MOMRbJz4f/bs2a5jx45u1apVwQJoLJvosCAvEeiYoCCMMGw6LrzcCiFE9BBVIgXQigX16tVzt912m2/boQUM1EkhhMhMeIvh3jHQD6hawY/cEykQoEXSsMiX0s9CCBEt0OXVqVOn4LGKyf3HH3/sqlev7lq2bGldFRUqVIj0bgohwsSxY8fc2WefrfdbCBF2evXqZYLDfffd5+644w43fvx4u50iPAKxly5danZPa9ascdWqVQt293v3E0JEJ1Fl9wQbNmyw/2nbOv10/3bPs9dQJ4UQIjPhCbOhFkMZDZW0N9xwQwLhWQghRGyDRQIhlB69e/d2t9xyi4kVb775pvvkk0/cOeecE9F9FEL4y3///ef69Olj1iiMKbds2WK39+jRw2xWhBDCL7wcCW++2bRpUxMpEB5YO6QTnUK8WbNm2XoiHRSNGjVSEZgQMUTUiRRffPFFMNTaTyRSCCEyu92TX1DB4gWP+X0sjwfwSV25cqVdqE5ev3598GfvIoQQkWTfvn2uZ8+ewZ9z5szpdu/eHZz4n3nmmb4WFwkhogPs3d555x03cOBAlyVLluDtpUuXNn93IYTwg1GjRpkw8eOPP5o9PNfJmfjrr7/cRRddZAIqoin/U0AxceJEK4Du0KGDbJ2EiCGibjZB0B4QYBMagpPRyO5JxDI7duxwdevWddmyZXN58uRxTzzxhHlBJ8e2bdtc69atXZEiRazKsVixYtYiefz48QT3oRoy8eX7778P06sS8WL3BL/88ov9T2WtSBmsUsqWLWsXKoSwOuT61VdfHfxfCCEiSffu3YPjcsYdWPp99NFHbvv27frDCJGJeO+999zw4cNds2bN3BlnnBG8vUyZMlZkIYQQfthNvvHGG27Xrl2W34fF5M8//2xrGhMmTDCxgnURMiewdyI/i7USFU8IEXtEnUjheZczEUIFjQeRgvb4q666yqqLuVSsWNFNmTIlzc/z7LPPunvvvdcqkwn7KVGihC04E0r76KOP+irqiIyHsCYqkdLj989JF4Fh/vz5VknA84RWOCaGSQPfp2HDhtmJe/Dgwe6tt95yTz/99An3nTFjhp30vUv58uXTvI+ZnTlz5riCBQva9X79+rlrr73WxAEWdho2bBi0tYvXTgrYu3ev/e9XOHe8sHXrVqv64f/EF+92z0pBCCEiAUUQM2fODFr4MYZlkXLevHmucOHC+qMIkYlgkdALoQ2FeQYLiUIIkdH8+uuvtuYFrE8ARV2sazC3xvbpp59+snW3kiVL6g8gRAwTdcHZtI8Di+9+5kV4g6jQNlW/uPjii13//v3dpZdeam3xLCo3aNDALVu2zJUqVSpNVlhPPfWUtddzGTRokB2EqWJDNea2cePG+fpaRPrBsuWBBx6wgCc+254dTlqhMgB7GMQEwp+otMYbtmvXruYPndRnunbt2nbxwK+RhXJO5HyOQsmVK5e1TIr0w3e1Xr16QcGiffv2JlSw0IMwVLNmTfsbpvczcCp4GREpdd5kBByPQP7kKVOoUCFf/w5CCHGqfPvtt27z5s12nXFDuXLl7CKEyHww9/zuu+9OGL8wB1XnpxAiI20mKchcsmSJdUqEOkB464ZZs2a166x/hGNdTwiRCUUKrzKDxXz8bf3C69IIRwuYt1jp8fzzz9skDxsdFi9ZxEwK7HhYdPbsr1CKWWimMvmzzz4L3o82N56TLgsWHv1836INPiehAUrhhJZC7JBSW3VUtWpVC3aiTZqTbfPmzW3iT7siItMHH3yQqu6fBQsWuCuvvNIECo9atWq5hx9+2D4jqZ0g0HnjiYKh1K9f3x07dsx8Hp988kn7WfwffB/paEqKKlWquG+++cauf/nll+7111+361OnTk1wP7pe6KhYunSpu+mmm8L+1nrHPD871QALALp+vKoXkTKbNm0yccuzXcOajcogBEUhhIgUjF8orIH8+fPb2EUIkXmhc7tFixZ2bGAsSWAthU/Mb/CAF0KIU4VuCYohPPtgD4QIrOZwEaFQUwgRf0TdarZ3sCEAx0+8BbpQL81wwKLd2LFj3eHDh8326fbbb7eFSsSHxx9/3O7TqFEjCx/zfvYWPbEHSs46hQVnfpeZBApAoAiHZVdyokFqK+EJkkNUwGaJBUisurp162ZdEfDcc88l+HunBCfrUIECvJ8Tn8iTg4rIIUOGJOii4H186aWXXOXKlW0hGyGMRdLPP/9cQsX/g78RHQKrV6+2SRnwHiJEeD8jFO3Zs8dVq1Ytyffes2VLSiCKJ5HC4/rrrw/LdmIZLMGY9PM3QcBCfMUui865F154IdXHBiGEyEg4X3EMZ7zDuYOiBfk7C5G5QbT86quvbO7CPIjxC4uJ3EZYrRBCnAoU8nXs2DHBusYFF1zgunTp4h588EF34YUX6g0WIo6JuhVtLFGARRoOTH7ZzngLdKmthD9VVq1aZaIEFeosBhPw4/nlISxwm/daUYip0g9dfKfC1qtkSwytcNj9cNAW0QkWT4ktzBBY+JwzwPc+h1gA+V15TuUTotidd95p9lMeuXPndp07d07wXWRB/sUXX5RI8f/gO8l3k++o933lNr7DnujAd5XOlqRaTjnudOrUyYQghMhI4H3WEEz9gnZc7/mVaZIys2fPtkDaHj162ICcQTiQPfTKK6+YUHHddddFpOtGCJG5YazAmAFhAotJOkKFEILg2unTp+uNEEJkmBX7qFGjTPSk2A9YO2G9okaNGpY54VkWCyHim6gTKWgl92Bi5JdI4S3UsUgcDqicX758uVWl4dlJmyw2T6kJ9vnjjz/sviNHjkzyd4Qo8zyeNVRmggVjzwYpEttOLQcOHHCXX365GzNmTPC2W2+91f3444/uhx9+sL8d3TIpfR6818l3YtGiRSeESXm/SwlEBxYZKlWqZKGXJ4MKSk1C0gYiRYcOHZL8HfZudGHMnTvXRQrvmOdnNSyinEeZMmV82048QIB9mzZtTjh+I3pRpYhYjz2gRAohRLggfJJFASxc4LHHHpNAIYQQQogMhc5xLyPz0KFDwduxdCLTlWxXIUTmIupEilC7IjoE4sXyhKpqL2+DyuLFixe7V1991Q0bNuykj50yZYotXhcsWDDB7RzIqXKjOp/OjMyoLiM2RSJ8OD0Lw4hU3mcA6KpBVEDA4rNAN0xqoCOHDBKqDLCGAYQE7L5SEjm8XAw+f6NHj07VIjX7nC9fvlTtl/g//8xly5aZcJgYhAu8eskhieSAKxwiBZ9t7/vpZ8dGPIDg+P777yf7e3Js5AEvhAgnHHc8gYLzFR2VQojMC12eqXUfoBNUCCFSYuXKldYxTiYnXRShvPzyy1YcIYTInESdSMHCGcG/LPQRAu3ndsIpUiSG7SaXu5F4EJiU1RMdFFjKZM2a1Srwzz77bF/3V5w6O3bsMDulhx56yLonEA0IOwfEBk9wOBk1a9Y0MYJFhIEDB1qlNXYxVOnzefAWPlnYnDlzpitQoIBti0yTQoUKWaUCVQseXvfFu+++a2KaF7xNxgJtl+RpiOQJ/b7ix0uXSmjeBKLAI488YkIiwdoEIkcS75jnp0jhhdnz2sNlqRer0AVVuHDhZH/P5yW1WTNCCHGq0LW5adOm4M/kU+k4LkTmhsVEIYTICFgDo0Pcy2mE888/3+yQ69Spk6wjgRAicxB1IgV4vv0s6iVVkZwReIHZfgohHgQkc8Ala4Duh48++sgWK7/++utkX//69eutUp7FTjopQoNTEShYqGYhEPWZn7kAQULhDgMXqQPR4OjRo+Yvz9+I6kSEg7TCY6nIf/jhh62rgk4S7MOwhvHgs0EVpFeZQKcFYdlcElfxh1qe0c2xfft262jy7KkaN26sP3EK8H1lEXnjxo0mGBIsGgriEd95xEbvvt5gDK/NcON1NvgpUhw8eND+v+KKK4LCmUi+6ySp/BIPOuTI+BBCCD9hfFCsWDGzn6OwwbNbYMwihMjcMM8QQoj0gO312LFjLQtz//79rlevXkGBgjWHJ554wj399NMJ8liFEJmXqBQpvEV2Fuf9zhPwKn79BLGBBWqsYFiYvOqqq0yguOWWW5K8PwFBbdu2da1atbJWNw7Y5cqVC/6eKvyFCxfa9VD7INi6dWuKVbkicrDYSCUSvounCh0RkydPTvb3iB+h4kPLli3tcrIJiCYhaYeOJjpasO2ioylxtZn3904sSGG5dbK/iR8cPnzY/vfTJo2AVWDBS6LpyaFbKbmBeag/qxBC+NFdRyHMG2+8YcUPXnYW54iXXnpJb7gQworhsJUFrzAuObz7CSEyNxTdvvbaa+bWwJobTimffPJJsJiNQjbWw/r27etr8ZwQIraISpHC87JkcZ/Jkx8HLa9bIxwLQEkFXif2/Q8FGx8u8Oijj7p69eqluAAthAgfiUWIUqVKWXcE9ljYbiUWDqPtu+od87xjoB98/PHH9n9mzMlJK3TYjRgx4qT3EUIIP+ActW3bNuvYGjJkiN1WoUIF16RJE5c3b1696UIIy6Sg2A5r2hw5ciRpAedZfKY2i4wiHi4cf7zxdM+ePc19QAgRm7B2N3XqVMvbnDZtWvB2BIm33nrLrpcpU8YKeLt27Wp22IgWoVbJQojMTVSKFF5YH1ZMtJwnDozOCLyqVbx3o5nSpUubpY8QIrrhmDJgwAAX7XgihV8ttQw0vW4NiRQnx5ucCyFEJKDbbfDgwe7bb781G4a77rrLKh2VQyGE8Jg1a1ZwEXH27NkZ8sZgP9u/f3936aWXmsBBtTUZjFRbI1gIIWKL3bt3u6pVq5oFMjCOQNgkf48cCmzJ6Zpo3bq1jT1Y56IoQt1XQoioFymwT8G/3RMs/BApwtlJcSo8+OCDkd4FkQGQQSLiG3JiYgFPmPWrk8ITmb1cIXHyiT8Bcd9///0Jg3T8Wglip/Loxhtv1FsphMgQ1q5da7Z8dOtS9cyCAQIFWVTYz0mgEEKEUqVKleD1IkWK2Nw88XECoWHnzp2pfuMSOwU8//zz1lnBeCgpkYJFTi4eJ7OdEkL4D2tp3pwyX758VqDGfAbxccWKFSZQkDvBXAdb5IYNGwatgGNl7iyECC9Raf720EMPBa+vWrXKl23ESieFEELEUifF6tWrg9cZrIqT24cRJJdUFREZRpwPX375Zb2NQogMAcsWxM+OHTtakCX2KjNnzrRFA+yd9u3bp3daCJEsiBR79+5N0q6Z36UHxFI6uOjETc5BoF+/fjYu8i5+FDEKIU4OgiQdVQgOfOe9DnqsnujGzJIli1u6dKm5otStW9fmhn///bd1TvEY7ieEEDElUhC26uEFRGfWTgohhIilTAoq4DzI6RApQ5VR7dq1k/09VUYM9IUQIiNAPCYg+4YbbrAcihdeeCG4SDh//nxbSBBCiOTwsicSQ+EfldJpgWJEimbwq2/btq114JYsWTLJ+3br1s06TL1LWro2hBCnzrFjx9yoUaNc2bJlXbVq1cz5hC5MusK/++47d+2117pevXpZsUOJEiXc5MmT3cSJE+06nZsFChRwnTt3Vki2ECL27J4KFy5sBy9U1gULFviyDRRe2tHUSSGEyEx4xzy/OinmzZsXvE6rr0gZ2qBTyu6gRTqpikUhhEjLoiLiA2Nfz1YFaz6vYplQ3AMHDlh3xWWXXaY3VghxAiwuAgJFjx49XLZs2YK/Q+SksJDFy7TA4uXy5ctNdBg3bpxr0aKFmzNnTpJCBUIGFyFEeGEe8tprr7lhw4YF5yR8//m+3nHHHW7EiBFuzJgxdjtdTr1793bt27dP0DHB2GLz5s1pFjKFEJmPqBQp7r///uBB7Y033vBtOxxEGRQJIURmgWMeC1V+TPR47vXr19t1wtEUhHZyqCqiDbp48eJJ/n7lypWyzRJCpBs83Fu1amWLiB999JEVAXHbPffcY8fsQoUKue3bt1u4Zffu3fVOCyGShEBrT/SkA8ITPYHrZcqUcY8//nia3j0e541/ypcv7xYvXuxeffVVWwwVQkQHjBUobuC7f8kll1i+BGMIMqzIljl69KiNLbCv7dOnj80Bp02bZnlXU6ZM+f/auxM4G8v//+NXi0IpISJLKkLWLJUWIVvWFhRlTxuRKLQgIiEVylaUFiEkyr5VlkiSUIgkWyTJ1nb+j9fn+7/P7xgzY5azz/v5eExmTjPnnDkzc9/XfX02G5ANBShEJGaDFGR0eXbu3Bmyx6H37p49e0J2/yIi0YZj3iWXXBKS+6Y6o1+/fu6pp56yTTDmLXTu3DkkjxUvbrvtNstIpOVTwsU7i37KpuvVqxex5ycisd9SbtKkSfb+mjVrbCPw4YcftuzlnDlzWh/5wH7vIiKJoQe9l0xIICEUiSgkKQYOxxaR8GKOBG3XvvvuO6uIAIHEnj17unLlyrkGDRpYO18qMb2Wa1WqVLFrPq+SimDGiy++aPt4zKF455139GMUkdgOUhCpJRo7bNgw98MPP4S0L++uXbtCdv8iItGGY16oBlozeNW7uPzjjz/ULzgFyFxmsU8ZNJlJtD4AFSlUEpL9TNBHRCQtKlWq5MaPH2+JORUqVLC2DHzMOpv/R5YjgYtWrVrpBRaR0xo3blxQXiVmTNSpU8cys5mXRqXX4sWL3Zw5c/RTEAkzWj5SGcH+G8EH1ggEJKm2BEloVFNVr17d5k+Av93Bgwe7u+6666Q5NbxPcsSgQYP8gQ4RkZgOUpDJdfXVV9v706dPt02vULR9ypcvn/XGExHJKHbv3m3HvlChzzlatGjh7r333pA9Trxg45BhtQyy5YKd7CNvgV+rVi079/E5IiIptWLFCle4cGH/saN58+b27+rVqy0Yimeffdaf3UivaTYkRERO58iRI5YdvWDBArdv376T+s7jxx9/TNGLyNeyVmRdyrV/6dKlLUBRo0YN/RBEwoSEYCqjSF44evSo3ZYrVy67LjnvvPPsY+ZQkFRFkgPXKVmyZHHdu3d33bp1s/e96m+CFzVr1rSPc+TIYRWaIiJxEaSAN7hv27ZtlrFBCVlyw0XTgmxiLxIsEkt27NhhiwdKr2mxw+AqFgIM2U1uID19pwPxNSwyJOPgYvDGG28M+v2yaKWFCBvuYJAaZcFyemQpffLJJ5bFROCc15Kh44GtD0VEUuLjjz92jRs3tv7wrBG84bYHDhywbMe//vrL2jXQZu7JJ5+0TcHKlSvrxRWRFGnXrp0Nt77vvvvsWjowgzo13njjDb3iIhFEMvDtt9/u/7hUqVLWppd5E7Sg/fvvvy2AQTXE77//bp/TtGlTa+VEFYXnzz//tODEypUrbQ1CK1sRkbgKUtC/7qOPPvJ/TGSW4aGUowcTCys27NgQSusCSyTcaP9St25dmyvAhjC/w2QiEcTr379/sl/73HPP2VArT7Zs2cLwjCWa8PsSinZPmzdvdiNHjvR/7LUtkpQjKFGxYkW9ZCKSZhx7yX7kOO9VZrFuoJqCRIUrrrjCvfXWW1Y5wSZEw4YN9WqLSIrRIm7WrFnuhhtu0KsmEkOOHz/ufvnlF1sHoFq1albFdPPNN1twomrVqv49sXnz5tltGzZssI+ZN0HV5U033XTK/bLmYO2xceNGf2KEiEhaRWVtN0P8iNoGBg4oXQ82Wp5wsPYiwyKhRKDt2muvtU2Biy++OM09GufOnWsLBto0sGCgn2vfvn2tLQwZkskhKEFww3vzyjglY+D3g5LdULR7CjxGs/nF7AuvbFhERMJXiczx+MMPP/Sf41kjUDFBW4YJEya4yZMn23BMEZG0JFTQykVEYsPevXtt34HqByotvQSGCy64wLqWzJgxwwIW7L3Rro3qCioj2G/ImTOnJaHRLjKxAAX4ulGjRtnn3HLLLWH+7kQk3kRlkOLSSy+1tiFsooYySOFlE5NZLLHfHzWpNwJRKf1cqnZS8rmpRdYC2QnXX3+9++abb6x9GcOk3nzzTfv/Dz74oLVtSu7Ns3z5civHDOxTT+96BhV/9913yT4Pesiy2KAND4+vTYqMt0hFKCopEh6jWewyeE1ERELn0KFD1sbp22+/9d9Gu7izzjrLn/VMFSXYaCADun379taST0QktQh6MtNGiSgi0Y11QZs2bSw40adPH0tU279/v9uzZ4//c7zWsuxv0AayRIkS1gaKNcSjjz5qlfIPPPCAf03heffdd13Xrl39H59zzjn+Cg0Rkbhr98QmKlnh9M/94IMP7Davz3kw5c+f39/fnwOyxK7ATfyE6IvIRbknd+7cSS6sq1Sp4hYvXnzSHAdO5gl5GQgpNXbsWAsqDB061LINKIlk4fDSSy/Z4oENhMATfXJYWCQcpOt9HLjoSIiFxjXXXGPZT/w9MaSXAB3PQTIGL2hAIDjYAo/RZOZwHGfwmoiIhM4TTzxhVRNkPLKuCNxIIEOSNk+sWUiGYB4RAQq0bt1aPxYRSbUhQ4a4rVu32rUH10kJZ0auWbNGr6pIBJE4RsBh/vz5/tvo5vDYY49ZgkLgDEvWB5MmTbJ9CFquo3r16tbV5Oqrr070/jdt2mQzafha9k7q168fhu9KRDKKqAxSeLwDo1d6xkDRK6+8Mmj3X6BAAYv6EiGuXbt20O5XJCGqJ6677rqTWphRVUGAgF7RBE54C6UuXbr43y9durT97pMZwfDsc889Vz+0DOCHH36wf4N5HPWCY/yOBw5VpFJHRERCi3M461iOuYEBCipD2Yw4ePCgq1SpklVw3n333e7EiROuRo0amkUhImnSqFEjvXIiUYyKCQIUtN+94447bA+AfYeEuHYjiXHp0qX2MUFHgpC0e0puXmuxYsXcwIED7XGYkykikmGCFMWLF7d/2UAl+5uBf8HcXONijvvzNu4kdv35559J/r+E5Yn79u1L8nM5mQfavn17EJ6ds02C7NmzJ1mRQYYjMyZS8j3SBu3LL79MtI1PYIu00yGjgnZPfI8acpwxcKwjOBvsoWazZ8+2f+l3zsYYv1siIhK6qjiO5WB9vHDhwlPWFrRN/frrr62ijRkUS5Ys8bdwIGCR3AaEiEhSevXqpRdHJEqwB/Daa69Z2yYGXYPAAV0aqHYg8JDY/Fdatr3++uvuv//+s+s3OixQTcH7iWGGK+sH5luiW7duIf7ORCSjitogxf33328tcsBMASK9oWgdwoBBBSliX2oGQIfqc5PDhsHKlStPKcX0+kanpt0TmRDPP/+8BVu86ot58+ZZi53UtC1bu3atBWVCXcEh0YNjHce8YKPKjd8lb/6LghQiIqExbNgw9/jjj1vAgXaWiRkzZowbP368HZcnTpxoc4iYXYWOHTuqxamIiEgMo8UjHRlIcqRC8uKLL7akx8yZM9u5n3ZPCdG94Y033nA9e/a0tupgptXgwYNdoUKFknwsEhpZb/A5H3/88UntokREMsTgbOTLl++kYT6rVq0KyeMoSCHhwuwTyi2///579/7779tGQ6dOnez/ESigqie5N0/NmjVtg4HsCIJ3c+bMcU8//bR75JFH/G2bqLSgFJOB3d6wbTIn+XxapzHsir6U9957r/9vTOJfqIIU/fr1czNmzLBgHPNhWPDyOyYiIsHDMZYEg7///tvNnTs30c/h/E8gAiQ00Ft6+PDh1kOaTQxlQYtIerDRyaYmbeSo4KaaK/BNREK3BliwYIEFDGiLTsCBAAXJYZznkwsesBfA3yytnglQ8PXcF5WWyQUowHxOAhXMvfLmVoiIZLggBZu5tLepV6+efUwWOouiP/74I6iPw4YdbaS8DGCRUGnRooW1wmGBQECBAIU3wDI1qLyYOXOm/UtVBYEG7ptqDA+DwQmGsJEBghdkUzLcikUJGxcEKUaPHh3U71GiF+W89C0PRZACbIB51Udskh0+fDgkjyMiklHRomnkyJEWBB46dOgp/5/+0ASJ//rrL+sb/+STT9rt1apVczfddJPr379/oq0nRURSqk+fPpbB3bRpU3fo0CG7ZqfvPdnbvXv31gspEsK/vVtvvdV9+umnth7g7+7zzz+3AESTJk0SDVIwN7Bly5aucuXKNtT+wgsvtMRF2kGyNkiJChUqWDIa+3GJtY8SEQmmM3xeU/woNWLECNehQwdXsmRJO8iyICJSHCyfffaZu/nmm9369ev9g7pFgu2WW25xZcuWtUWBSKQqeciUmTVrVpItQtKCuSYsilkck43DxSoL4WuuucYVLlw4aI8j0Y0EAi582LCg9ZyIBMfu3bstyYDEguSQyFO7dm0blkkrSSqQ+Zv0sNznLeHsLRHJeNJzzr7iiivcq6++an3v6U9PdZd3G61s33vvPRcOWndIvOPv88iRI/4OI7R4ItmxVatWtibg7y4pJCqyZ0b1pJc41qZNGzdgwIDTtnpmrcCcC5KFT1dlISIS7PN21DeU83qbU2JGZQWRYw6cwRr452UVk3WuIIWIxCtv9k6wKyk4RjOIm7kUYEFbtWrVoD6GiEhGxOYEQV/WwFREMgw7KbR9JEDB8Xjq1Kl2EUDggqpLsG7WsGwRSS+SBkuVKmXv0+KTzQZv/ZdYH3wRSX1i2SuvvGLzpRo2bOgmTJhgt9Pumb8//u6Ss2jRIkvyJajhVUIQsEjpzMBBgwZZJSaDtamOZ10hIhIuUZ1ORSk75aTnnHOOBSgyZcpk/fSDOeiaSHLOnDmtx56ISLxat26dy5IlS1DLdJl5QunwF198YYtmNsDKly8ftPsXEcnIaJ9HYILgsjf4OjHTpk1zL7zwgr3/5ptvWvUxLf4IGDNkO9itUkUk48qfP79VeIFMbm8+DtVb3mw8EUk9qpKaN2/uLr/8ctsDowKCPSqvfTOSC1AwL+Luu++2Nk4EKNjjItBBm6aUBijQrFkz+zun8oJrRxGRcIrqIAXl7QwY9jbVrrrqKvv3k08+CdpjsKlGW5KvvvoqaPcpktDixYvV6kkiimNcmTJlkh2qllpUtgVWZzDgnXZStM8TEZG0t9HzdO3a1Y7fSbV1YB4Q/aZB+wfaouLtt9+2lqbMniLRR0QkGG6//XYbuIuOHTta9QQt5piPx6amiKTO0qVLXc2aNV25cuWsXRpVkCQZcE1FMhiJuslhDtWLL77oihUr5j744ANr60iCA4m97dq1S1Gbx8B1BwGKjRs3WgtfVWCKSLhFdZCCgypD/rzIrzfsL5hBCpD5qyCFiMQzjnHBrnLwjsVeb9NcuXJZ9s1TTz0V1McREckIqH7o2bOnq1+/vn/DgA2CpDInybJkcCb/Ml9t4MCBdjvtV7p3727vs4Ho9bMWEUkvqrY4ToGgKBusDz30kJsyZYq/oktEUm7ZsmVu3rx5FkygEmL16tVu4cKFNkPwdAEG2jyShEZ7Jq9FJF/PXNccOXKk6PG/+eYbV7x4cZsp4zldSykRkVCJ6pkU999/v/1LNQW9+H7//Xf7mMUQF2QM6woGNu5YVNGu5JJLLgnKfYqIRAuOl2TTsIANFrJ2WBjj2LFj/l6p9D+vWLFi0B5HRCSj2Lp1q/WhPnr0qFWqEaxICvPZyFom25EgxKRJk/zZls8995zbu3evVbl17tw5jN+BiGQ0119/vb2JyOkRSHjjjTesQ4jXxvHBBx90+/bts8qkwoULp+hlpLUT7Rw593sJY1RT3HfffSmqnAhEggOzBUluYJ6FqidEJJKiOkjhue666/wl7ZS7cxE3Y8YM69kXDF52MZnGdevWDcp9iohEi6+//to2tIJZSTF79mwLfuTNm9c2ycDimmweERFJPVqm0Orh4MGDyQYoMHjwYMtcJjDBv3ny5LHbOR6/+uqr9j4BD+a6iYgEC63kkkPbJxE52a+//mrDq3n77bffXKVKlazFEwEBuoUwgyKlSWKc2/v06WMBDwISDMnmY6/rSGrRFpL5FSQ4KEAhIpEW9UEKDr4///yzDRBiaPatt97qmjRpkqrhP6fDzIuLLrpIQQoRiUsEYDNnzmyVDsHyzjvv2L833nijmzx5smXwlCpVKmj3LyKSEWzevNkCCYUKFbKPGzZseNqvoR+8187p5Zdf9mcxE4zu1KmTtYpq0KCBq127doifvYhkNBxjAjHUl+ovjmNZs2ZVkEIkwPbt292QIUOsesKrPCfptnXr1nbOTk1QgBmXzJrwksNo7fTaa6+lOkGM1pJUTFSvXt3f2mnYsGH6uYlIVIjqmRQcQClhr1KlikWbQSsR5lQwoDVYODloLoWIxKtQDM1u3Lixq1OnjjvvvPPs4xo1agTtvkVEMoIvv/zSqoXpO+21ND2dn376yfrAs0Zu1aqV9YL3bNu2zS1fvtyde+65bujQoSF85iKSUVHpFfj2559/uu+//96SVmjRLCL/07dvX9uzonqCAEWFChUssYu/F1o8pbQt0+7du62DCMO0CVBcfPHFbty4ce6zzz5LdYCCodwk/JL4q79XEYlGUR2k4MBdtmxZC1RcffXVdhs9eok6BxsnjVWrVoXkvkVEIokBasEemk2QgsHZtJLyhmZTKvzII48E9XFEROIV61uq3MhipIXD6bDJwaDsAwcO2DH99ddfPykLk6pj5g/RMor3RUTC1aqO+Y4JqyxEMhqCAB4q2PmYtk5UQJKYcNddd1nSbUpQFUn7xmLFitl5nfM9iQkEOUhSSO3sCfDYdBGh8kn7XiISjaI6SAE2wX755Rcb/EffXbLEiCDPmjXL9ezZM2iPQ/YHUWpaSomIxAuGp7KYvemmm4J+37t27XLffPONLZpZ6JIJzIJaIm/EiBF2EcIGKO0RuTBKiYkTJ9rPs1GjRiF/jiIZXf78+a3lAm+0zEsOx1jaPKxZs8aCwlOnTrW/74SYE0QgQ0QknKjWZV0oktFwfmZviv0k5kV5WEuvXbvWzZkzx1WrVi1VrZ1WrFjhKlasaIG/P/74w95nLU97J9qUpwcDtklga9asWbruR0QkQwYpvFYiZJl5m2zTp0+3nr0DBgywjLFg4KRCNJpefyIi8WLJkiX2L23zgoGg8cCBA21W0Ny5c/2VaCzK2Tx77LHHgvI4knYffPCB69Kli+vVq5f9TCgFr1Wrltu3b99p++Z27do1JAEtEflf73ayIGnJ5ClatKj1cT+dkSNHuvHjx9talb/xggUL+v8fCTze8VhEJJRmzJhx0ttHH31kx6d7773X3XDDDXrxJcOgSoLkHjp/1KtXz33xxRcWRPCqKahaSG07JoZqP/DAAzZvggAHw7CpmmTdwPVWWrC/RaW7VznBOkJzBEUkWkV9kCKQNwCQEwAbLnj33XeDct8XXnihu+aaaxSkEJG4wsL0qquusuzaYKDcmIGt9EadPXu2/9hMpVu5cuWsJFki66WXXnL333+/DeWj1JzNAzZB33zzzSS/hgsqfqZ9+vRJUZuYEydOWGZX4JuIJI/kGv4eqXQ4cuRIil8u1r1eGxWCxGRkBiI4zLq4X79++hGISEiRHR74xvGsd+/ernTp0smuM0TiBWvgMWPG2DXPPffc49atW2cJtd26dbMKiJS2cwpEAIFEBK7ZRo8ebR+3bNnSP78iLfcJEpTq1q1rwZOxY8em6T5ERMIpJoIUbIix+VWgQAH7mLJ4+qHjnXfeCVo/vVtuucXuW/35RCRecEzj2BYsXmCYRbmXuesFkCXy6GvPoHQG4nnImOLjwOzthJ577jlrN9O2bdsUb7YS3PfevPOziCSNCieq2t544w1/pXBKqtfuvPNOq8Jg2OXjjz9+0v9nVhuZzLRaUZsnEQm1//7776Q3khz27NljSSzBSogRiWYkBrRv395t2bLF5ciRw9bQO3bssDZKafkb2LBhg12rkVy0f/9+SzCiEp6gxelaQZ4OX//KK6/Y+uG+++5L132JiIRDTAQpiE5T7sZBmyGDDA7khMAFHjMkiFgHAycHLga3bt0alPsTEYkkLho3bdoUtCDF+vXrbQYFVRNk2x88eNDKkP/880/3zDPPWLavRBbnSTYM8uTJc9LtfMzvQ2I+//xz2zQlKyylevTo4Q4dOuR/o/2XiCQ+F8hDpiWB49tuuy3F2ZoM2eQ+aM1AlnJgT2v+/6OPPmrvU2nBxoaISLjWG6qilIyAdW7guZy2jZdeeqlVLv/00092DZSWORFHjx61Gau0hFq6dKnLkiWLDaBn3+vmm29O8/NlbcA1mqddu3bWliqxOVYiItHmzFjJPJsyZYpVT3gZu0SXb7/99qC2fNJcChGJJ8GeR+Eda9lg87Lya9SoYT2JaTPCIFeJLYcPH7bMKgIUDONNqXPPPdddcMEFJ72JyMmYoUZAlwxjT2oGZ3bs2NEScdj8mDZt2inVF2yQkMl5ySWXuGeffVYvv4iE1O+//2697VkvkPzAsYnjD4kLbLiKxJMDBw7YubVQoUIWTPCQNMAcNyoqSD5IC1rmlixZ0iqT//nnH5tpQUXFk08+aclgacVMC9o/cn/Hjx9P09pDRCSSznYxILBtBUEKMsk4sHNxRrsnIsNDhgyxTZNgzaUg4iwiEsuCOY+C7HwvSMFwRAZle8dkqinor07AQiKLjQP61gZmfIGP2UhIiMpBLrTq16/vv432DaB9DL1wr7jiijA8c5H4s2zZMtu4mzx5srXIS80mwahRoyx4yNe8//77p/wdUr3kzaAYNGiQAoUiElJsfl5//fXWdYAZVsWLF7fb2VgdNmyYmzdvnlVm0gGB4KpX5SUSa5jjwN4ScxyoFgetVGm76AUQWCOnxe7duy248cEHH9jHtEt99dVXXcOGDYMSSPj111+t6p3rNv422dsSEYklMRGkSBiwoL82B93ChQtbqR2bMjt37gzKRgrDCOn/xyYNjyMiEouYrcPMCLJpguHjjz+2TbGcOXO6ihUrui+//NJu5/45DqsXenQ455xzXPny5d2CBQtsoCU4n/Fxhw4dTvl8hv59++23J9329NNPW4UFPWw1a0Ik7WjbUKRIEesznZrNB4IbVFGgf//+iR7HmU1BAOSGG26wDUMRkVCi7z5rDJIbEraU5P/VrFnTKjNZe7LpKhJrCCAwV4IkAdqLg1ZMrIu5zknP3hBrcQZiM2uV9lHcV+fOnV2fPn3SXI2RGJLTmFNFlRMVHyIisSZmduHZHBs7dqzbtm2bBRJACyg2yr7++uugZXpSGkf0fNWqVUG5PxGRSNi4caPN7OGYFgwMhKNXKoPiaO9EEOTaa6+1AIVEX4tEMrDfeust+z2gdy6VLmyUokWLFtaaAfSnpdw88I3KmGzZstn7bEiISMqQcfnyyy/b8RFsQtx///2pyrgMHJTNPApaPySGygySdUaMGKE2DiISlvZ1VNEmDFCASk02dz/88ENbg7Rs2VI/EYk5I0eOtHM4AQoSsrjeYZ+Jc3F6AhTM9KOtOOtxAhTc9+rVq61aIxgBCv7uSOD1MM9CAQoRiVUxE6To27evXehNmDDBNWvWzG6j9QhtTILZY48yVjKFOSmJiMQqjmFZs2b1B3XTi7J9Kta6du3q76/OsZgsfAK7Ej2aNm1qGwn00S1btqwN4KNForexQMCJbDERCR5aK1SvXt3aOJBVnBb0jyZbkyH3bDCMGzcuyTUuc9k2b95sWZ4iIqHGuuHqq69O8v+T2MBGbq9evfTDkJjAdQ0BBE+nTp2scpE188qVK60Vanr2mQh2MMuiXLlyNsuPBCBao/E+twUD7SAJotStW9eG2YuIxLqYCVLUqVPHAgi0puDCjOxOIsZemwpaU3BCSS+y3RgKqyCFiMQyjmGU3pMpHyw5cuSwoYn0GuZCtEmTJja/h81vhrpK9KC1008//eROnDhhF1pUvQTOKqGtYVL4f2RMikjKMQuGSrOLL744TW32qL4gy5IKYdo08DeYWIYlAzYDH1NEJBxor8wMq6TQ7SB37tz6YUhMBCcYAE8njgceeMBf/ch1DvtJnMPTmwRLm1WSDbzB2OxfUd3M+jyY527mAV555ZUWUGHtICIS62ImSMGBnR69nEhoRUG02IseMxA0f/78dnCmTD69GjRoYFF1FlsiIrGGygYCCRzLgjEoMbD93cSJE+1fKjRYzHsbZpUqVUr3Y4mIxBpv0DzatGljw+avu+66VN/P8OHDLUBIAJiBmpdffvkpn0MVFLfT1zrwcUVEQo2N26eeesr99ddfp/w/EiKeeeYZV7t2bf0gJKqDEwQJCE4wFJvfZQZh//HHH0F7jAMHDli7M+aoMr+Ftrgkck2dOjVoLXIDz/8ED7lOYw6MEhdEJB7ETJAiIa/lE0EKstZoacFmGb0Eg7EIo1KDQbEiIrFm1qxZ9q8XzE0PZhsQgGjbtq1lGtFmzzsGc5z86quv3K+//qrZFCKS4RAsuOWWW6ylg5eJmZZMxkWLFlmbKNDXnczIxDAsmxlttD4NZqtTEZHToY0dQdgiRYrYcYqKXQb0vvDCC3YbWeK9e/fWCylR2aqMtrUEJ5jjRHCCuQ2ce6kuvvDCC9P9GKwBaIdL14+3337bztEEROj80ahRIxfMQAvXZXPmzPHfRgKviEi8OMPnXVXFUM9fKhyIRNNihDZPn3/+ufXvpR8fAQsyzdLb4oRABVHqefPmBe25i4iEg9fTnOqz9CDwS9Yum2L0Rr/mmmus/znBCSrYtCgWD1loXOQxEPCCCy7QCyNxj+Bs0aJFrQXe66+/7h588ME03Q/tUxiiSS/p5s2bJxmAmDt3rq1NyZRcs2aNK126dBC+CxHJiNJ6zuYa/OGHH7bjkbeFwPGKwCrVYLSdCRetOySlSLC699577f2bbrrJ9enTx1WtWjVoLyDtVVkDeK3Hmc9CkldaqipPh8H0Q4cOtfXHd999Z63KRURiQUrP2zFVSbFlyxYLQnAxR2keLaBA1Lphw4auQIECdtHotSNJD9qkEFk/ePBgEJ65iEh4HD161C4eaX+XXmTIEaCglPjuu+/2D8ymQoMTTIzFuEVEgob1KC0cyC6mFWla/Pnnn7Z+JUBBEJhNjcQCFLRS6dixo71PZqYCFCISCYULF3affvqpHbNoK8ob195szoYzQCFyupZL/G56uIZp0aKFmz9/vluyZEnQAhQkz9JmiYHy/A2QxNW3b1+rMg9FgAJULjH/ims9BShEJB6dGWsLIzbFePvxxx/9LZ8mT55stzEACZws0rt5RgCESooPP/wwKM9dRCQcaFN35MgR17hx43TfF8dSsAHHwtsLAHPspXw5X758NuhVRCQjYDOOdiceWj3Rhz0trZdYp7Zq1cqtW7fOKoMZlJ0lS5ZEP3fw4MHuhx9+sM8jA1REJJJoa0fLGd6YTyYSLVm6nCOpAr/zzjutFSOoQHzrrbdc9erVg9YqkeugG2+80XXq1Mmuu6jQ+Oabb9zTTz9t10zB9MUXX/jf575HjRrlChUqFNTHEBGJFjEVpOAE8+WXX1r2BiVunGjIZOOiccGCBa5du3Z2gff1119bC6j0YPONwbBe/3URkVjAMevaa69Nd0bb2rVr3dKlS+24SyBi+fLlVs6cLVs2q6Qgg4eWUpT+i4jEOwZgkhlJyyXa3aVXv379LBGGymD+pRo4MRxj+Vy89NJLQemdLSIiEk9V5AMHDrSEVuaiEKxgj2jXrl1BfyzmWVAtUa5cOavW4LqIlo904GAeRTCRzNC1a1cLhjBLQ0QkI4ipIAUYzOWVtvFvkyZN7H3akOTMmdP6DZ555pkWqEgv7osTDu1ORESiHQFcyvC9vqvpLScGmUjMAPJaPVFlRjCYnsSUTT/11FPpfiwRkWjHDB7WlwRu2QBJD6omnn32WXufzY0bbrghyc9lNhrtnmhPcc8996TrcUVEROIFAYPXXnvNBmJ3797d/fbbbxYomDRpks1u4vZgoo1ThQoV7PzNY9erV88qKphHwfog2Kj68CqVaA8pIpIRxNzg7IQYDMvF3Xnnned++eUXG8Jx/Phxq7RILy5CL7nkEovIP/HEE0F5viIiocJmF33LyRzKnTt3ujKSyBCivQgVFRxP8+fPb4t/giC1a9cO6vOW2KcBlpJRqinImkzP8XX9+vXu+uuvtw0HjtdeW73krFq1yioogrG2FRGJh3N2PHwPkj6cG2k5hssuu8xaPTVv3tySCYKJvSXue9CgQTaHgll9nLuZdRGs9lFJYauOTiJUyYuIxLK4HJztYbAgZW9TpkyxC70SJUpYL0B6DRYsWDBoF3G8cAzQfuedd4JyfyIiocSxilYk6dlAQ9asWd13331nlRJlypSxWRQEKOh/WqNGjaA9XxGRaMXGAC0d5syZ47+NrMz0HF+pdmNdSYCClqJDhgxJ0ddVrFhRAQoREXEZ/bwcOBeKcyNz84YPH263Mxw72AEKEmLLli1rFeYEKJo2bWrVE1Q2hiJAwX3TZpfHAo+hAIWIZCQxGaSgPy8DhGg/woG7Q4cOdju9+hh27dm4caMd6NODtinffvutDTYUEYlWP/74oy2kySAKBtrpMfeHC4Jhw4bZbbR4YvHPxh2DXIPRl11EJBq9+eab1tKhcePGNn8nvWgNcdddd9kalqGetKNgHkVSSLyhmk1ERCSj4xrn5ptvtuSpwFbcI0eOdI888kjQh1VTVf74449bYiwBELprTJs2zRK3mHcRCgz6vvXWW+176t+/f0geQ0Qk2sVkkKJly5YWMae1Ce677z6reuBijsxfjB071l199dWuc+fO6XosspKZdaFqChGJZgRtaXvXsGHDdN0P7Zzof+5hKBwzfjJnzuzatm1rC+gXX3zRdevWzW3ZsiUIz1xEJPqwtmSzgIAsmxPpQbCX1k5LliyxdlEff/yxrS2TQpLN/fff70qVKqVAhYiIZFicD5mHR3vvzz//3BJUV65cGdLHJBmW6omXXnrJzt/sPZH42qhRo5A+LjP/SAwjGENimIhIRhSTQYqrrrrKIuZ58uSxj88//3zXqlUre5/gBbiwJOOXgYOc0NKKLDfK+SZMmOD+/vvvIH0HIiLBQwXZuHHjbMg1gYq0Yv7EbbfdZkPnaKEXeEzlOMimGot1ghRkBNNuT0QkXtDWzkNWJq2e2rdvn+77pdJ39OjRtrny/vvvW5vSpHCMpdUDa86aNWuqzZOIiGQ4VDAykJpg/fTp020wNclSmzdvtmuQUKB6okuXLu6mm26yx8mXL5+bNWuWGz9+vLvoootC8pic8w8ePOj/mGu5RYsWJZvIICISz2IySJEYL9o8c+ZMK6VneFKbNm3stl69eqXrvrlA5UQ5Y8aMoDxXEZFgooKMdk/0ZU2P3r17278EHwh2cNybPHmy3ea11WNeBRto3M4Fg4hIPCAzs3jx4hZQ8ATjGMfx2avqHThwoKtbt26yn//2229bxQXHWi9ILCIiklFQtV26dGk3atQom83ALKf169dbp4z8+fOH5DGXL1/uypUr54YOHWqBg9atW9t8PpK3QoXKdVqLV6lSxQbKenR9JSIZWczuMHHyILJNOf6+ffusuoKMM2732kA99dRTVgmxcOFCu+BLKyL4lStXtv6AIiLRhmMTx6n0VDZ89dVX7qOPPrKFMX3YMWbMGMvm5X6vueaaID5jEZHosnjxYltPkjH5zz//BOU+aUPapEkT22Rhvdq1a9dkP//AgQP+zyHBplChQkF5HiIiItEscK4obY/atWtnA6OXLl1q1yckEYTC8ePH3ZNPPmmzJzhne9UTzKXKnj27C6Vff/3V9qloacXMDRERieEgBSXzZP0yK2LKlCknZfoSZadcr2DBgnaCC0Y1BeWGZMNR+iciEi127dplVV4cozgupreKolmzZtbuieCEF5j1jq20zqOXeuDMChGRePDEE09YFQVtFs4+++x03x/tG+rVq2f/Xnfddf52T8np3r27279/v81Ue+yxx9L9HERERKLd3LlzrYohcKOe6xKqG2i9FCokaJUvX97a2BIkIZmAio1QVk8EoiqELiDMA6xdu3ZYHlNEJNrFbJACbMoxiJDINzih0OaJC8KJEyfabT179rS+wlRScOGZVo0bN7begK+99lrQnr+ISHoRSCDjiHLhtFq1apUtkqmieOaZZ+w2+r8SAGH2j9f7dcCAAVZyzWJeRCSWUS1B5a1XNUEAgdahzDlLL4K8HDdJbCFhhuNp5syZT9tuiiQb0OKCSmAREZF4xTBq9m9q1arl1q1b55577jn//2P/Jj3JV6c7R/fp08cqNXgOuXPntvM07RZDNXsicCj3l19+6f+YIAmzVEVEJA6CFAxPevXVV61nIRiU7c2mGDZsmLV+IkLNTIkcOXKcNJQotbi4vP/++2047Z9//hm070FEJK2oaGAzq2XLlu6CCy5I031wnCSDGGQQFS1a1N73eqFz/ORCgc8juzdv3rzWvkREJJbdfffdtmYMdsUCx0oSaGjhwGwfqs8I9p4OWaQEgvnaG264IajPSUREJFr89ttv7tFHH7U9HKoIqF7s1KmTe/fdd0P+2LRWoo03lRq0YiQRldkTDRs2DPljkzBbvXp1V79+fffTTz+F/PFERGLRGT6upuII/XwJTNBf8LPPPrMqC24jEk+gIj04mVx++eXWDoAqDhGRSGIxTwUFWUBp7dVK0JUWT/PmzXObNm2yHujffPONK1u2rAV+Oe5deuml/s+nHFoD3SQhBv5deOGF7tChQ2kOmImE04cffmgBXtqGNmrUKGj3S/IMmy2sO+mjzWaEiEg0iYdzdjx8DxkR1QokBxCoAMGBQYMGuSJFioT0cbl+4fzco0cP2yeiYoIOGSQshMvhw4etfRWdP7iGI5FBRCSj+COF5+2YrqTwUE5PNjFoyeS1Penfv7//tvQGKMDmHSfSl19+2SLvIiKRQnx56NChlpGTnmFytDZhpgVZRN6QVu/Yeeedd54UoIACFCISqwLzcji+/fjjj0ENUJAR6lVm0BYvJQEKZlD89ddfQXsOIiIi0YpgAQGKUqVKuQULFlibpVAHKH7++WdXo0YNOz8ToKC91LfffhuWAEXguiNbtmz2PZMkoQCFiEicBil+//13V6JECatsoHzPGzxIBjAXi4E9/zhJcCJMTz912qJ8//33dj8iIpFC5QMD35588smg3B9VYiBYMXnyZHv/6aeftn/37NljvWJFRGLVJ598YtW1ZPF46EMdLGx4NG3a1DZgWrdu7R5//PHTfg3rUtrsXXPNNe7rr78O2nMRERGJBtu3b7fuFp4WLVq4999/361Zs8ZVq1Yt5I/PY9FWihaMWbNmtVlU7BElTMIKBbKF69Sp4yZNmuS/jeRZ9qlERCROgxTZs2e3TDUi4l4m2hVXXOGvpmAokoeAxe233+6eeuopa4+SFtddd5275ZZbrG9wnHXKEpEYwjEoPcPWtm3bZvMm9u7de9Ltffv2tWPbHXfcYVlO3oyfMmXKuC5dugTluYuIhNOxY8fcAw884JYtW5auRJWkEMitV6+etXKoUqWKGzlyZIoGfn7wwQdu9uzZVhHM5omISEZax1asWNGyywkYU9VGIqDEh6NHj7pevXpZtfc999zjjhw54q/IpoKBORShxCxSHpeWtiS1MiR77dq1ltgaqoHcCbEWmDNnjs2/Yn0gIiIZIEgReJHHJpqHDGCi1GTOrVq1ym7j5NSgQQP3zz//WL/gtAYZ6GVIBvP8+fOD9j2IiKTUihUr3OLFi+1YlNaFdteuXd2YMWMsUOEheOtl+zz77LP+21ncczwlC1lEJNZkyZLFKmAfeuihk45twQqA0Ap0x44d1rJi6tSp7pxzzjnt19HugrUoSJ656qqrgvq8RESi2ZIlS9wjjzxia1qqg//++29Xs2ZN/2a2xCb2V5jHRKeL5557ztorFS1a1IIG4cI1EtUTEydOtOsXhmR//vnnIW8rldi1FtdZc+fOtWCciIhkwMHZgRiIyHCmunXrupkzZ9ptW7dutZMmVRfTpk1LUy9iXrIKFSrY0A9KB0VEwokNMbLNCCqkZUYEAVZ6s7Jwp8WIVzFBxhELeirO2GgL9Msvv7g8efKEPPNJYpMGWEo0ZnHSZoI1X6jQ2omMUFrkMfuMzbaUboLcf//9buzYsa5YsWKW3XnuueeG7HmKiET7OfvXX3+1igqCFzfffHNMfg8ZHXOeHn30UTdr1iz7uECBAu6ll16yGVDhqF5gf4fqjYEDB9p+zZVXXuneeecdS1QNF9pYlStXLmzVGiIisSJDDc72kHlB30Ev7kI1BRt4nChXr17tbwVFVBu0LiG6n1qcdMhgXrRokVu5cmWQvwsRkaStX7/eBl0ziyItAQoy1bzsXcqPvQAFM32oSkNimcb0blWAQkRiAZtdVatWtTda24XKM888YwGKTJkyWWA3pQEK+nMToMDo0aMVoBCRDI9NCxDwTcyJEydsgyPwTaIH1YRXX3217btwTmSvhGuLu+66Kywb9j/88IOrXLmye+GFF2wvqG3btpaIFc4AxfDhwy2RtV+/fmF7TBGReBM3QQpaOJGNRt9BMjDAxWLz5s3tfcoNPZw02XDjwnXIkCFpejwyjSnNp5+miEi4kB1EZpJ3bEstBsZRgcHgtsCZPSyoWdRTXVa2bFm7bdeuXdaSREQklmTOnNkyKlkbMi8iFN58803Xv39/e5/WecyiSAk22rw2e+3atXM33XRTSJ6fiEisoCqtc+fO7oYbbnAlS5ZM9HO45iYD03tjLSzRo2DBglbpXb16dbdu3To7P5533nkhf1yuXd544w2rXqAd90UXXeSmTJliiQDnn3++CyeSuXg+O3fu1OxSEZGMHqTgpEBbp8svv9z9+eef/tu9aoqPP/7Yyu/ACWvQoEH+BU9aeiTSJoVMZnouUqYvIhKOLCGqxagGS0nP84T27dvnr5Lg4oGFPDZt2mRtnhJWUZAlnD9/fn/Gr4hILKD3M20+ly9f7q6//vqg3z8t8xjE7c2ToL1oSjHjJ1++fNbWJBRDvEVEYg2zKagU9taiiSHJkGoL7+3nn38O63OUU68paFtIO9jA4D3zRUgcDQfOp7SqJeBPi8dq1apZgIT2UpHAUG5agTMwW+2eREQyeJACBB42b97s6tWr57+NQU1UVyAwa5gewtxOeb63UZda9913n90/iyYRkVAj6MrmVuCw69RgcBwXdmQbUQYdWEVBFhsZUPw/8DEVFwyFLV68eNC+BxGRUHjrrbfcpEmT/B9TMcsaLdjYSGMDhCoN1pF9+/ZN1dcz24cgB+1C07r+FBGJFx06dLCgMm2USYxJCnN76GEd+Cbh51UuEIggiemxxx7z/7+sWbOGbXOeJASuWWhVS7IqbZ4IkCT3OxRsu3fvtgAbFZIe2kwqQCEiknZxPTjbw4BZBiey6UYZ4DXXXBO0+6acsHHjxrawuuWWW4J2vyIigZirU7FiRctSat26dZpeHPr3PvHEE5bp47V0ojqDIERix0dODzwu/VW14JbT/W5pgKVEyqeffupuu+0228SiajZUw7LZkKC/NRm8tGliQ0QDr0Uk1kTDOZs1ZseOHd20adPc4sWLUzzTJ5q+h4xmy5YtlijFvge4lhg1apSrVKlS2J7Dv//+a61vqfzm/cKFC1uVeThnT4DrJoIkVG4w42/EiBFhfXwRkViT0vN2XAYp+JaWLl1qJf5eS5R7773Xvfvuu9YnkYvKhBtuDHvittT2t+SxODHT/omIvjbyRCQUatSoYSXVLIaDOcCa+TrTp0939evXt4HcImmhzQKJJDYqqG4gOEFlGG0+g41WosydIAhChcayZctstk9q5gF9++23lu2pDTURyejnbDZ233vvPWudzJxHD88rS5YsMfE9ZBRUDjLHk4rs48eP28+HKsJOnToF9ZrkdJgxRScLqhFBqyfOrfweRALPg1kq/A5fccUVEXkOIiKxIkMHKWrVquXmzp1rUXXaOoEh2WQLU47HhhxtTTyUmFKyTwSer0ttoGHBggXu1ltvtdZRbPiJiAR7EUyQgmwzBlunBof42bNnu9q1a59ybPOOXQRZCX542ce//vqry5Ejh90ukhLaLJBwO3z4sM0Y845rBCpCdcxig4b1HevFXLlyuRUrVqRqQ4LKC46vBDoYsk3/bBGRjHzOTup6e9y4ca5Vq1Yx8T1kFMxPYhYnuG6geoI5oOHENUvz5s3d3r17ra3U8OHD7fck3Ami/N4F/r6Fcu0hIhJPUnrejquZFB4qKDh5/fbbb/7bKAV8/PHH7f0uXbqc1DuQjDguQNkIHD16dKofj+oMNhB79uxp9yMiEsxy4u7du9txLTC4mlIEa2mDwqyewJg0xyqyf7xstsD2KAR3r7zySn85t4hINNm6dau1vwucBxGqTQKOm17P9MyZM1vFWWoCFHz9Qw89ZAGKypUruzZt2oTkeYqIxBKOjYm9pSRAIeHFdQLtYMePH28JneEMUHC9Qmsn9loIUJQsWdKtWrXKWt+GM0DB9Rh7PbR4IpnLowCFiEhwxWWQgtJDKic4oQZiwHXevHndjz/+6F5++eWTghQDBgyw9wlk8LWpRfn+pk2bbHCjiEgw594wK4JjTGoX4/RPZ3MNBDkCv56ALANgqZigfNvDBQC301oq3FlSIiIpsWTJEps3xvBOsnJCiWMvWaMcP2lNwrE0NRjqOWvWLGs/ypDRULSiEhERCZa1a9dacJ2NeVC1yIy6li1bhjUwwHUMlRskJBDAuv/++93KlStDNncqOWT+cj5nH0ntcUVEQicu2z0lZ8KECa5FixZ2smVgLEELcBJm8PVnn33mqlatalUVqb2QpC8iF84EK1R2KiLpdezYMXf11Vdbqzo2uVKDQzuVFx9//LErX768zczJlCmT/T+qzBhQyL8MeksY0D169Kj74osvLGtJJCXUdkHCjWPXHXfc4V/HhcI777xj/a/x6quv2pDX1Ni/f79tppB1+dxzz7lnnnkmRM9URCRjnbPj4XuINn///bd7/vnn7Y0Khtdee82CFZFANTd7KyRPsW9DchUfRxJ7PFRxeOsCERFJuQzd7ilhS4BvvvnG/zG9DBl0Tdk9JXseAhJvvvmmtYnipDhy5MhUP9bAgQPtBedCVEQkGD1gd+7c6YYOHZqmzTUCFGTvUp7tBShA5QQBCkqm27dvf8rXchxUgEJEogVBV45pga06H3nkkZAGKBYuXOhvzdS1a9dUByi8yl4CFASbvX7eIiIi0Wbjxo1WKdinTx8LUNx55532Fm4kjvbr188qKAhQlCpVyqo4IhGg2Lx5s1u6dKn/42LFiilAISISYnEdpKAX+1VXXWVZwl7BCMGIV155xd5n445ouIce7AQa0K1bN7d9+/ZUPV7BggXd008/ba2kvvvuu6B+LyKSsVBOTBs6NsdoSZcatGp69NFH/QEJghGeDRs2WGYUOFadffbZJ2UIiYhEG9ZkZC4SNAhHAfC6detsUDZZpU2bNvWvDVNjz549bs6cObbuZBAsAWMREZFoQlCAvRFmTtBe9qKLLrLWhpMnT3a5c+cO63M5cOCAq1u3rlUd8ryYO7FixQrbzwk39nKuu+4616BBA7t2EhGR8IjrIAXtm8geppSEygkPJxyvTI8st8ALXgIatHsiQ+/SSy9N9WMylJuBivSBz2CdtEQkiBhqzcXBU089leqvZSPv999/t8GybO55OCZxv//++69r1KiRq169uv//0eOVtlIsxr0etCIi0aBOnTru3HPPdddee23I+2H/9NNPrnbt2laSfPPNN1tCS1rmSFxyySW2yUGbUY7FIiIi0Ya9D64Njh8/7mrVquW+/fZbq1oI5+wJUC1Be9rZs2e7LFmyWIcLr8tFJJC8SrtGAiQ5c+aMyHMQEcmI4n4mBa1S8ufPn2imMSedI0eOuHfffdc1a9bM//9oJ8DFcFrNnTvXTvJkIUS6d6KIxJ6ZM2e6+vXrWxbTXXfdleqvJxOqbdu2btKkSSdVYdD+iSAEGb1kBRFQ9dBvnSArM3u4KBBJDfWGlmBjeRq4ScK6LS3JI6nN4rzhhhtsKDcVaMwpy549e0gfU0Qk3OLhnB0P30M04JqBpCWqtx988MGwBycwduxYSxD966+/LDjw4YcfutKlS7tIozVu5syZIxYoERGJJyk9b8d9kCI5DIWiPRMXvbQ5YShTQpwsySggsp8abCwuW7bMLnSzZcsWxGctIhlhWDaLdFqFpPViIeEGH8FXNt22bNlivdFfeOGFRFtMEcBILLArkhxtFkgwrVmzxipdp06d6i6++OKwvLhHjx61jRpaSxQoUMDWcGk5FjLLgsU37aJERKJRPJyz4+F7iAQSNDm/Bc6e47WMxGvINQ/dJ7zkKBKp3nrrrYgkB9De8YEHHrCOG4nN6xMRkfTR4OxELj6pbAhE1vBll11m2Xk9evRINKOuSpUq1jaKYENqvPTSS7Zooh+8iEhqh2UPGzYsVQEKWpSQDeVJ+LUMwiNAQQuSpFpIXX755QpQiEhEeX2oP//8c/fEE0+E5TEZEsrsCQIU9OOm3URaAhSHDx+2537HHXfYHAoREZFosXbtWlehQgWb+/D111/7b49EgGLHjh3upptusgAFLRX79+/vpk2bFrHqRWaZct5mph97QyIiEhlxPZMiMIO4WLFirnnz5m7x4sX+2+l3OHr0aHt/+PDhbsmSJSd9HdkZlPgxz4LKCAIdqRmi/eyzz9pg2i+//DKI342IxKv169dbhRfDslMzJI6Kr8aNG7vKlStb5nFifV4JfoCh2YHVXdu2bXP79u0L0ncgIpI+bFbQqq5Jkya2hgo1qs7InqTNHms+2uLRhzotqFJj46Vw4cL2/EVERCKN8xzrf+Y60T2CCkUqKiKF/Ri6VJBcxbwHKkLfD+MAAHf3SURBVMdJGE3L/KdgYV4p8zm4jgp1a0kREcngQQrmS9SrV88uGikrDESpY7t27ex9ergHBiLOPvtsq77IkyePbR7SKzE1Hn/8cXfNNde4Vq1a2TAqEZHkyow5VhQpUsQCnKlBUGPVqlXuvPPOO6U1HUFaMnsZlk2mcMIWJGQMcWz84IMP9MMRkYjg+MTGiYcgLcckkkVCjY0RL5Nz4sSJNpMiLebPn+9ef/11f39tjsciIiKRbq9x9913+2c+0FJp3bp17sYbb4xIsOSVV15xt956q9u/f78rV66cBSr4OBJYd7D+8CrQR4wY4W677baIPBcREclAQQrQf52WTXXq1Dnl/w0ePNjK+rdu3XpKG5S8efPaRSsXr+PHj0/VQFmCHHwN96u2TyKSHCodKMPmmEE2b0oxXJvWUJgwYYIrVKjQSf+fygyCrGRNeZ/nIYuKoXAEMrhQEBEJN5JDqFYlw/O7774L62MPGTLEDRw40N6nsrZhw4Zp3gQi0QVkYlarVi2oz1NERCS1uK4geYnqRPYlOOdNnz7dqhfCjUTRli1bus6dO1tggA4XtHVMeN0SLh999JElk5LoJSIi0SPDBCnotZgpU6ZE/x+ZemPGjLH3ie5/8cUXJ/1/ZlL07dvX3icL4Ztvvknx4zIAlwDFoEGD3MqVK9P1PYhIfPr2229tZgT91ytWrJjir/vhhx/8G2Pdu3e3HrOB6DdLj1eQHZRwAC2ZvlwgcGwqWrRoUL4XEZHUOOussyyjkmDp5s2bw/bivf322/7NCRJZvGNpWnA/tHliro8X9BAREYmkuXPn2jw62lB/9tlnNo8zNfPugmXXrl0255NkKs75zO7k/axZs7pIocsFgROqKagwERGR6HCGj7q7DObTTz91e/bssRYogdq0aWMDk2i3QiCCmRWBgxzr16/vPvnkE3f99ddbICOlJ3kGMtIrnoGKbBqmJktaROK/zdN1111nG3SUPNOeLiWYlcNxhQDHzTff7BYsWGBZUh4W3JUqVbJjGVnKVFyIhAqZ5AT8Dx06FJEBjBLbDhw4YAEKjoXhwNwJWt+RzUlrThJJ0rpxQ/VHyZIl7X1mm3E8FhGJZvFwzo6H7yHU2L8YMGCAe+ihh1yOHDki8hyYzdmoUSO3e/duew5UdVSvXt1FA2Zh8FwCr59ERCSy5+0MU0nhmTdvnvUapA87J8tARPXz5ctnF8rPPPPMSf+Pdk9k3dEzkX9TczHrtX368ccfXa9evYL2vYhI7CPrlkACx4iUBihA9RcBity5c7v333//lAU2FyXcLyXdVFEEosXTyJEjLYAqIhJuzG8IbJ/JcSpcAYqlS5faUGsCFC1atLBWe+nJLKViluSXfv36KUAhIiIR88svv5w0Y5P9C1pZRypA8e6779p5kT2XEiVKWMAiUgEKkrtISiBp1FOrVi0FKEREokyGq6Qgo4D2TfQ+JhCRMIIza9YsG7LNSZ02KFRNBAvtBFgoLF682N10001Bu18RiU1UTnCM6datm82OSO2xjBZRzNlJuLlHcKJChQoWhCCAwcC8QJ06dXKvvvqqbdRpYLYEgzIaJTU9smlrx/KT9VA4h3dyzK1ataptUrDWmzp1apKtQEVE4lU8nLPj4XsIdgC+cePGbt++fe6BBx6wZKRI4RqFPQ/2PsD5loBFJH9OXC/Nnj3b3XnnnW7KlCkRex4iIhnVHyk8b2e4IAXInqMfYlIY6kS1RLFixZJtz8SJjjJ/ovIpwYYh2QMM0uYiPVeuXGn+HkQk9g/SDGzLnj27tY9LTRXF6dpHEYTl2EU7kw8//PCULOFRo0a5nj17RlXJtcQ2bRZISrHspN0mLemopghXC8yNGzdaRifzL0hWoX1nYFvP1CKRhT7fvImIxJJ4OGfHw/cQLFRXP/zww7bXULp0aQvAX3HFFRF5LlRx3HffffYcvJl5VBomt/cSDszfI2mL5K1wVW6KiMj/UZAiHQ4ePGjl+5Qm0haKYdoJbdiwwRYBBDwSy1ROrgyzTJkydnKkJ3IkhleJSOQ36e655x5rEbJmzZoUX0hwzCATidk5SW2uPf3001aVQWk3QdRLLrkkybLn888/P13fh4hHmwWSHGbu0JLO26QgmMrH4VoDbd++3So2WINRxcEMn2zZsqX5/n799VebQ8HgTe6LyjURkVgRD+fsePge0ot9CKqxhw4dah83bdrUvfHGG+68886LyPNhQHaDBg2savGcc85xY8eOtYBFpBAwCRzOTXIEz0tERMJPMylSYOfOna558+buvffeO+n2iy66yE6qoCUKmcgJ0VeRlilo1aqVZUKnxKWXXmpVGrSV8hYUIpKxcHyhzRKZTykNUBDMIBjK13FcSmoAXP/+/e39119/PckABRSgEJFwDcVmnldg1SktlsIVoCDhhMcnQMHajeBwegIUBJnbt29vLTWoovCGZouIiIRzs4eAgLefQAtYEicjFaCgSwSV3AQomDNFAD+SAYrRo0dbV4wdO3b4b1OAQkQk+mW4wdmBJkyYYAEKyhDJ6gvEcO0nnnjC3m/Tpo21aEqIYYuNGjWyDMGGDRu6LVu2pOhxue+uXbu6J5980gZIiUjGwbBrKrToF8tMiJT4+eefrZ8rGUE1atRwXbp0STToeu+999oG2kMPPXTKfXOcIii7evXqoH0vIiKnQxIHrZHGjx9vx6lworVTzZo1bQ1XuHBhN2/ePNs8SY+33nrLTZ8+3QItrCPD1a5KRETE89tvv7lVq1bZOYgEpmeffTZiHRpIvqRakXM8gQFaK4Vz3lRCXPMMHz7crp9oKykiIrEjQ86k8FCmTwCCgAG94RMicMGARS6w+f/8m/Bi9MiRI9bbmI2/IkWKuOXLl6foAphyQ3oj792713rH05deROIbxwvagrC5xQI+Jf3QyZRioU9wgzZ0HIcobw9ED1qOVWwElitXzi1btuyUY9VLL71kmcx58+a11ifKJpJgUtsFSQ4DPG+66SY7hoXL77//bjN3qELLly+f++yzz9zll1+ervv86aefXKlSpWzwNlVrPXr0CNrzFREJl3g4Z8fD95BerPe5pqCNYSTP74888ogNy6ZqcfLkyVGxr0EFBbP3uPZRe20RkcjTTIogISOATT+y8RhINWLEiFM+Z8+ePVbeyMmQi3Ay9VIyBJeNwrJly7pq1aq5KVOmuDPPzNCFLSJxjXhwy5YtrX0cpdBkGp0OgVJKuWfPnu3y5MljgY1ChQqd8nlUgw0cONAu0pKacUEP9Q4dOrg6depYizqRYNJmgQSaOXOmq1KlSrraKqUHM3eooCBx5OKLL3ZLly5N0TE3OWzAEPRYvHixq1y5st1npAeBiohk1HN2PHwPqcUeA3MoateuHemnYufEnj172vUHWrdu7UaNGmVBk0jgOmfdunV2nhYRkeijmRRpHPaUsO1T/vz5rZwfr732mpVTJkTfd8ocWSCRJZjSk/Nll11mbQOmTZvm+vXrl5anLCIx4uWXX7ZjidcjNSUIjBKgoOKCodmJBSjYDPQuEChpTmrGBRt1HL8IlIiIhMqwYcNc/fr1bYAnVV7hduzYMXt8AhTMGJs/f366AxSgXRUBCvp9M1tMAQoREQmXd955x1pG33XXXW79+vURfeG9FrLe9cdzzz1nA7sjFaBg5tR1111nrXFJ6BIRkdil1P3/j0FTDFRkzkRCZCuQKYB27dq5H3744ZTPYXAifSEJZKSmIoJZFn379nW9evVyU6dOTftPUkSiFgOtaSvHnBsW9SnVtm1bCy5QrpxYKTfVW17QoWPHju7OO+885XOoAgukkmcRCSUqSwmsEhgI9/GGjZM77rjDgglUcXDsLV26dFDuu1mzZu6xxx6zIaVJBYNFRESCjf0JhlAT+CcIT4vpSKGVYq1atdzEiRPd2WefbQmXzzzzTESvL6g2L168uLV2jIZWUyIiknYZeiZFoHfffdeGztKuadGiRadkyLEooM/ikiVL7IJ3xYoVyfaTpyJjzJgxNhz3dNl2/AjIOKQag96SZcqUCdr3JSKRRVCzUqVK7oYbbnAzZsxIdfYtbUvOP//8ROfa0E6FYxEBDPqtJ2wzt2XLFmtX9+CDD7rnn39ecygkZDJi2wVJ2rZt22xQdThxTCTDlKqzrFmzWoAikoM7RUSiVTycs+Phezgd9giefvppm4GELl26uEGDBkWsRTRdJ0jeZE4erzkJltHSXonrJSopSe4SEZHoo3ZPaciQY9DTwoULE91EJFPgvffec7lz57Z+h506dUp2QUG2NEOkCFLQszE5ZB6MGzfOXXXVVVZZQU9FEYl9ZBsxU4Jh1Rw/UhKw5GJk9erV/tsSC1CAga0EKMgYoo1TYnNwuHhg0c6cCo5hIiLBxjDpGjVq2L+ecAcoSAy5++67LUCROXNmCwgHK0BByz16gIuIiIQL1wRU73kBCqophgwZErEAxffff2/zmAhQ0OqauUyRClDw2vTp08cNGDDgpOslBShERGKf2j0FBArIwEtuI48SQjYa+VyqJOgtnxj+f+PGjW0RQX9G2rCcrmCFHsfTp0+3DACeBxmBIhK72NS655573N69e23DjGyv5HCMYAA2FQ9s+B04cCDZ9nQvvfSSv096UhuCtJfisTkOReqiRkTiW/v27W3uA/9GApWuJIYw34tg7UcffRS0jZNPPvnE1alTx91yyy1al4mISNiwvn/llVfs/REjRrhu3bpF7NVnzgMV4SQj0Goq0p0fWHP07t3b2nF/8803EXseIiISfNq1SgSVD6+//rplCiTEhS/DobyhtvPmzUv0hSVIQY9GAhbMqXj88cdPG6goWLCgZT4z7JEqDHXiEoldXExwfGCeREp6x5IR5M3EITMoZ86ciX7eF1984Vq1auUPQlB9lRx611522WVp+h5ERE5n7Nix1v6BYGgkAhT06aYS9pxzzrE1VM2aNYNy3wSYW7dube+XL19e7fJERCRsaEPNrLk333zT9hwihdaJ1apVs+Qp2styHRLuasmESOZi1t/IkSPVJltEJN4wk0JO1qtXL6IJvvLly/v++uuvU16e//77z3fvvffa51xwwQW+9evXJ/kSjhkzxj6Ptx49etjXns748ePt83v37q0fjUgMGjx4sP0NDxs2LEWf379/f/9xYujQoUl+3pYtW3y5cuWyz2vUqJHvn3/+OeVz/v33X7uPP//8M13fg0hqHDp0yH4v+VfiH8eiSOP417x5c/u9y5Qpk2/GjBlBu2/WanXr1rX7LlWqlO/YsWNBu28RkUiLh3N2PHwPiZ3XAvcKUrJvEEqTJ0+28yuvc61atXyHDx+O2HP56aeffCdOnIjY44uISHjO26qkSARzJC699FLXokWLRHvIUx1B5iBDthn+UbduXcu4S0y7du3c8OHD/dnRAwcOPG3gqGXLlva5lDGSISAiseOdd96x7B5KkDt06HDazx88eLB9Ll544QXXuXPnRD/v4MGDdqzZv3+/ZfXyOIkdnygJp4ctfWPVR11EgoljyqOPPupKlixp7R8ihQoK1mjvvvuutelkLg9VY8EybNgwN2vWLGsfRZtP5lyIiIiEspMDbROpmvDmWbLnEClUcDRt2tRmPjVp0sTaxyY1Jy/UWG9UqFDB3X///eo0ISIS5xSkSARDbrdu3WoX4kn1cefClf7HV155pfVnpOUK8yQSQ+smBl1xkUs/x5R48sknbTg3C5UPP/wwNT9TEYkQBqzSHqRNmzauX79+p/18Zkt4PWYJSvJ3nxhm1Nxxxx02tK5AgQI2HJY5NokpXry4fQ7B1tMN6hYRSQ3aUP7444/u+PHjbvXq1RF58bwWTwQPCFDQUu/2228P2v2vWbPGf1wmiExARkREJJTnVq77CQww83LVqlURfbGZe9e2bVsLlhAY4HxLS8VI+f33391vv/1mrbhJEBURkfh1BuUUkX4S0Y4NQt4Syx744Ycf3PXXX28nTgZek82XVGBj+/btqeoNz8KAYZD0WKYfJIMbRSQ6keVDz1bm1vA3y+bZ6bDRd9ttt7lbb73V9ejRI9GMKQ7RBD0YoJctWzb3+eefu9KlSyd7v4cPH7YghoZlS7hw0chw+EOHDrkLLrhAL3wc+/PPP91nn31mA6XDjYxO1kXMoMiUKZP9e7q5PKnB8bZcuXI2iLNRo0Z2LI9kJquISCjEwzk7Hr4H77zTvXt3m0vH+ebtt9+2eRSRei4kTXnzNwnY0wUiGs6Dn376qXWxiFQ1h4iIhOe8rUqK09ixY4e7+eabLTs6sXhO0aJFraKCi+UpU6a4p556Ksn7CgxQrFu3zjVr1swdPXo06R/OmWfaxiSPz0U4F80iEn2ocKAVU9myZd3EiROTDVAQ8PSOJVRXzZ0719o9JXUBQOs3jgMcDwiCJhWg4GDvIZihAIWIBAOZi6+99pr/YzYIIhWgYN3kBShYcwUzQAGOw2SM8v0xCDwaNmZERCR+UXlNgAK0eY5kgIKKbi9A0b9//4gFKKiYJFhCi1sP52UFKERE4p+CFKexe/duK/2fP3++27ZtW6KfQxCBGRVeT3lKNU93oU1rAlq91KhRw3rNJ4W2UmTyFSlSxNWuXdsqN0QketDurWbNmi5PnjzWhilr1qzJVjhQORHY1im5gAZBCS/wSY/0pDYGx40b54oVK+aWLFmSru9FRCTQzp07rU0lbSs/+uijiL04J06ccI0bN7bABC0nWBc1aNAgJI9VokQJ98knn7gcOXKE5P5FRERAa6dnn33W3h86dKjNpIhUgIJ5doMGDbKPX3nllSQrvMOBNUefPn3cnXfeqRkUIiIZjIIUp3HttdfagFoCFZdffnmSn8cAx2eeecbepxc8QY2kkAFIKWf27NndsmXLLMixa9euJD+frGgumC+66CJXtWpVt2XLltP/ZEUk5H7++Wf7m2T2Ay3ZktvUIhuIdlALFixwr7/+ugU3ksOxoWXLlvY+w7SZT5NUWzjub8+ePdYKSkQkWPLnz+/atWvnqlSpYm0WIoGKUyomCJJ488Dq1asX1McgAYRjroiISLiSADp27GjvE6hgrR8JXEdwjUFgwqvmYC5nJDGfgxmhBE5U0SgikrFoJkWQsxDolUyFBNnUbFreeOONybZQqFWrllVr0Apq3rx5Nog7KXwecym4YCdjOrmgiYiE1i+//GIbd5Qk8/dYqFChZNvGUW1BW6hcuXJZ0LFixYpJfv5XX31lsy1o4VS/fn3blEtuCPaRI0fcqFGj7AJHbZ4kEuKlN7T8b8Pi33//tYQK8D7HOQIEkZh/wTFw8eLFtq6aMWOGHRuD6dixYzZbbP369W7ChAnunnvuCer9i4hEm3g4Z8fD90AFNrMWRowYEZHNeM7vVG/QBYLHp80hLa4jVTEZuM7g3JwlS5aIPBcREQk+zaQIEeZCUILIRXxCnNxpu8JmJIEE2rp8+eWXSd5XqVKl3BdffOGuuOIKG6pNS4Wvv/46yc8no2DhwoXWx57s7R9//DFo35eIpC77ib9B5kssWrQo2QDFhg0b7G+bAEWBAgVs4GxyAQrm1XAM4aKLICdBz+QCFGBIdpcuXRSgED8ueAl+c76gIjC5c9GYMWMsS55qPd4Y5J7c50v8YlOATfq2bdv6Wyxw/IlEgIJjIIkcBCioKJ09e3bQAxRexiZrOyrhCDyLiIiEA0F4Zj5FIkDBXgaVkgQoSHCiy0OkAhQkb9HaetOmTf7bFKAQEcmY1O4pFegnz8Ykiwn6wyfGa0VAxQOfzwX22rVrk7zPwoULW6CiTJkybt++fTbTIjmXXnqpbYryOFxMq/WTSHhRFcHfHhk//C3yN5wUsqPI0CWowcwI/tb5NykbN260DeLffvvNNpZnzZplAYikNpaZWSGSEL8XBK169eplrQo5v3Au4hyTGDaB2Zjm93n58uUWTCNQRrWQZCz8vnz44Ydu4sSJtnEfKbTHIyBBCyZaY1JpGop2U1ROcCz1Bmbny5cv6I8hIiIC9gbuvvvuJOdchjNAcf/997vx48dbIgLnv0gO7Gb/gxa63gBxERHJwHySKmPHjvXVqVPHt3///mQ/7/Dhw77KlSuThujLmTOnb/369cl+/u+//+578MEHfYcOHUrR8/jll198V111lS9fvny+TZs2pep7kMgbPny4r1ChQr5zzz3XV6lSJd/KlSsj/ZQkBbZt2+YrXLiw77LLLrP3T2fatGl2DLjxxht9v/76a7Kfu3nzZl/evHnt88uVK+c7ePBgkp+7bt06X6ZMmexzFy5cqJ+dnIRjyiOPPOL/+N9//7VzxYABA1L0Sv3zzz++bNmy+d56660Uv7Kcu/h9TOk5TKLX+PHjfYsWLYrY4+/cudNXokQJ//ppzZo1IXkc1mVZs2a1x+ndu3dIHkNEJBrFwzk71r4H1mINGjSw51ymTBn7OFLP4/7777fnceaZZ/omTpzoi7R9+/b5evTo4Ttx4kSkn4qIiET4vK1KilRq06aNmzlzpsuZM2eyn3f++edb6WKFChXcgQMHLCOQwYxJoacmw2+9nppkFVCxQcZFYsj2I/uVDEOyC1evXp3ab0ViJMtZogP9ymnbRMYtMyhopXM6jRo1suMFw7KZRZEUhmhzjGDuTMmSJd3cuXPtbzspxYsXd926dXN33XWXVW2JeGhBxkwTKnI8lPHzMVUSKUG7wr///jvZQfBUEtFXMvBNYhMVXlQueFq2bBmx4wptLFnT0CaPylHa45UrVy4ksy4aN25sv+v8bTz99NNBfwwRERHPoEGDbK4S3RBGjx4dkRat7C/QtpoKQh6fasKmTZuG/Xlw7qVC0nPxxRe7/v37u3POOSfsz0VERKKLghSpxAZl4KKCEyxDa5MKPDA8u3Tp0m7v3r2uWrVqKZ4j8corr9gionLlykl+zSWXXOIfoE0bqsCTvUSvl156yUps6ftZokQJN3LkSBsISk9QiU6ff/65bZyxiGZDr2DBgol+Hm2d6tWrZy2hPHXr1k120U1LHY4NfM1VV13l5s+fn2xAA2effbZ7/vnnLeAViT62Er3YbGYQYp48eU66nY/37NmTovt48sknLRAeGOhIaMCAAXaO895oESWxZ+rUqXb8IaB6/PjxiAeCmcNDGwxmdXHcJSAbCm+99Za11+P3/N133z3t3B8REZG0IrGwZ8+e9j4toytVqhSRAEXHjh3tupNrB86DzZo1C/vzIKmFfQtmd2rvQkREElKQIh04yZMBzwmeTaHEkInKpiMX2mxGki1Nz8XTue666ywIwUU7Q3bpFZ4YNjPJ0mYDlc1Q+khLfGc5S3iR9VSjRg1XtmxZCwryd5mYFStW2N8qcyQYRJcSBC85JhCIJNjI33LCzeXA/rEEJfjXE4ksLIlv9AXmPMJsJYZuJ6VHjx422Nh7S8l5TaIPM3IYTslxzRuUHQkMamfWj1dNRgVFSqrV0urhhx+2jSJ+13Pnzh2yxxERkYyN8xpzKFi/t2jRIsXXCMHE+b179+5uxIgRFqAYN25cxGZQZMuWzV155ZXWPSK5daaIiGRM2uFKB9r0kCFNS4LkLu7JvmbzkRPy9u3bLWvxdANJCVLQwol2UQzRZZOU9k+JPQ6DdT/66CMbfMobVRgSv1nOEj5jx451t99+u1VHMASbjPHEMHiODTZ+hqVKlXKjRo067X3/+uuvFpz6/vvvrTJj4cKFdixJyrPPPmsXOa1atYroZqJENwLXZIUTAAvEx0kF2DyDBw+2IAXtxqgATA7tCrjADHyT2EM138qVK92kSZMsWBEJVJySVclah+xSgsF58+YN6WOySdOhQ4eQDOMWERHBP//8Y2t31mAE4LmWj0QFNNWv3lBqkixp6xgpfP90D1i1apXOwSIicgoFKdLh+uuvd+vWrbOsBNqvJIcLbjYhyQzcsmWL9bZPbkYF2LBcunSpv1KD9k/t27d3x44dO+VzM2XKZBulTzzxhOvcubNluWojUyRt+NuhnRJtuR588EHLtk0s24fKGP7eaN3F+wQ0li1b5goXLpzs/TODgrYmVEpxbCCIWahQoWS/pkiRIvZ3zmaeWjxJUgicly9f3n6nPGTv8THnrKRw8dq3b183e/ZsC45LfPr999/dnXfeaa2OPLSZi1RV1nvvvWdBYPpT16xZ035Pk5uFkh5sEj3wwAOanyIiImFx8OBBO79RPTBlyhRLLAw3qgafeuopfzIKewnhRmspr92Vl+hCBbmIiMgpQjW5OyP677//fN9//32yn7Nt2zZfkSJFbKr5xRdf7Fu9enWK7nfgwIG+M844w3fWWWf5Vq1aleznDxkyxO6/WbNmvqNHj6b6+5DQOXHihP0Mp02bdtLtLVq08DVo0EAvfZT8jNq1a2d/Q88995z9/SVm165dvvLly9vn8fbss8/6/v3339Pe//r163358uWzrylYsKBv06ZNKX5uHD9ETmfixIm+c8891zd+/Hjfhg0bfO3bt/dlz57dt2fPHvv/9913n6979+7+z3/hhRd855xzjm/KlCm+3bt3+98OHz6c4hf70KFD9jvNvxK9WrVqZT+n0qVLp+h4FUovv/yy//h5zz332LE3VP766y/fzTffbI/VqFGjkD2OiEgsiIdzdqx8D8ePH/d9+eWXEXls1oGB1ymRsG7dOv9zmD17dkSeg4iIxM55W0GKIF4As7GZJUsW38qVK5P93L179/quueYa+wFly5bNt3DhwhQ9xty5c33Dhw9P0edOmjTJnkuFChV8O3fuTNHXSHhUqlTJ16FDB//HbBRdeumlvgEDBuhHEGH8bd544422YTtu3LjTXnSUK1fOlyNHDt/06dNTdP/Lli3zXXTRRfa3X6JEidP+bW7evNl35MiRVH0PIhg2bJgFwfhd5pizYsUK/wtTpUoVX8uWLf0fFypUyH8BGfjWq1evuNssyOj27dtnP/+1a9dG7DkQ+O3Ro4f/9+zRRx8NecCkc+fO/jVXagLDIiLxKB7O2fHwPYTS1KlTfWeeeaa9RpwDk0q6Coe+ffvaeT/SyREiIhL95+0z+M+p9RWSlp6TDRo0sN7Ko0ePdm3btk328//44w/XqFEjG4hNe47333/f3XHHHal6TFrF9O/f3w0fPjzR9ghff/21a9iwofv777/d9OnT3bXXXpvq70uCj+HH9AJlbgH9t19++WXrB75p06YkhyZL6K1du9b+Xk6cOGFDgxNrjXP48GHr2+61d2PgNX+/+fPnP+3900aHNiuUfTNzhgHbybU12bVrl30e7aA+/vhjDXeVqMY5jZktDNHWfIrowlDzAgUKuGhAWzyGhk6YMME+pq0e7SlD2cKO9RVtM8GxnbWXiEhGFg/n7Gj+Hrp06eLOP/98a7NEa6NwY7ZTrVq17JqmTZs2NmMvnK1imTHF9x2J9lYiIhLb523NpAgSNi3ZfCZIcboABfihfPLJJxaY4KK9cePGbsyYMSl+PGJLbHRz8V22bFn32WefnfI55cqVs6FU9HxkqO/bb7+d6u9Lgq9p06bWE5RByPzs2BxnA1sBisiZPHmyzYlhyD0D6xMLUPC3xN8Uffs9/G2lJEBB3/X69etbgKJ27dpu/vz5p+27vnPnTnfkyBE7iDOLQkQkNVgnvPTSS+7KK6908+bNi4qFad26dS1AwXD3N954w3pUh3LjZM2aNf41GcEQBShERCSUuCYfOnSoXS+sWLEi7C828zJJnCRAwTmPpLhwBii2bt1q11H33HOPzdQUERFJDQUpgoihWLfeeqv/YzYYExty7WEQLxn0DOdlsCmDrKiMSElxC4sNKjbYfCBL8pZbbnF9+vSxio5AbHwzsLt58+YW1OjatasWDFGgQ4cONjyZBeTKlStV5RIh/N316tXLNWnSxBb0DKpPGHTgcxgqXLlyZVt4v/POOxZsSKlXX33V/v742ySb96OPPkpRZhFVNsuXL7dg5kUXXZSm709EMjY26UmEiHSQ4pdffnE33XSTBWg5/s2cOdOyO0OJQdlUx7EOIzgcGGAWEREJNs43XmCcf0kSDKft27fb+Y6kAM65JEl51d/h8uuvv7odO3a4b775xqrCRUREUkNBihChLcxtt91mlRLHjx9P8vPIJiTDgWxCUBZKiSgbo6dTvnx524Ag+MDn9+7d21WrVs02vwNRbkmZJ22FyOyoWbOm2717dxC+S5HYtX//fqtueO6556zlCAv5rFmznvQ5LK5Z7D/55JMWZKDi6auvvjrl8xJDsPGZZ55xnTp1so8fffRRyyCmPVRSaM1G0NFTtGhRq9YQEUktkhmoVuDYNnDgwIi9gLSmJKuS7E4SJ2hDwXE11Pbt22fH4auuusqqTllviYiIhAqJT5s3b3b58uWzqvlwBwdo8cQ1fsmSJS0piha14UarWtpMU0USLa0mRUQkdihIESLMF6BtzLJly9yWLVtOu5HAJikBBBBMoESSSoyUVG+MHz/esrt5nxLTq6++2n377benPAabpWQxbtiwwdoMRTqzUiRSqJjgb4AqFmZDJGw5QtCPSqXixYvb3wmLfAJ9tHTLnj37ae/f6wHbr18/+5gMXv6uzzwz6UMuQRAqLljc//DDD0H6TkUkI6FigeqtwCQF1hPhbPUQaO7cudZKj+ArwQKqw0iwCIdSpUrZOoxjfEqO2yIiImn15ZdfuiFDhtj7JCCG87xDhXe9evXs+qFgwYLWRjhcVdgkAwwbNsza1HoIljBTT0REJLUUpAiRihUr2oUxG5xkM6RE586dLdOaskzaQN14442nVEUkhc1NBmVT2snmK4GKxFStWtXKL/kcFhBUbiRsESUSr+iNSuCAv4MrrrjC/haoeEpoz549VtFEuTRtl6ieoGw7JRt9ZDDRfo3gIUGJ119/3T399NOn/VpmTxBAJBOKtlIiIqmt4KRigYQEgqqRxiYNx1ev7cQXX3zhChcuHPLHPXDggP/9Sy65xI71IiIioUJy00MPPWT/ck1OwCCc1zb33nuvBUmYd8d8zEsvvTRsj0+raqrFmTmVXPcIERGRlFCQIoTYqGSDM3DzknYuyWGRwQwJBvgyULlChQrWGiEluBBfvHixlXd6GdtUY1BuSma3J3fu3O7TTz+1RQUtIHiegS1mROIRgQcCcwwsJ2iwYMGCkxbxgS3WKNMmG4rqJqqhqKhICSoz+JulxJkMJjKZHnzwwRR9bc6cOd2iRYssuFmnTp00fIcikpFRTfnAAw/Y8ap69eoRex4cS5l/xbHP2zwhYYNjXDgqNy677DJrcSUiItFd1UzbVdbcJPLQIihW0cGA1oach73OCOFCS9pp06ZZO1n2AIoVKxbWxycow8+QZC7mbYqIiKSHghRhHGTF4F0G556ucoGMQ1oUXHPNNdY3n2HcZGOnZKA2wYnAjQB64nfr1s2VK1fONlsDP6979+4WAGG4FZUVLGxE4hEbV2XKlHHfffedbZYxZD5wkByVEgQXCBB62Oyjuimlfczfeustd/PNN9scixIlSlhGU40aNU67mcdFjYfg5Om+RkQkUOCagtZ1HHvCUbGQmD///NPddddd/pYXHGvffvttazsVahxLmRvEc1A7SxGR6EYiHWvzESNGuFjH98EsinfffdfW8uHC/oB3vqWCmy4M4V53kBjw/fffWzWFiIhIeilIESYsXNi8pDoisBVBUugnyXwJekmzEHj44Ydt0/Svv/5K1eMSGKFyYuPGjbZw6dixo7WE8NArmudEYKRRo0Y2hPvgwYNp+h5Fog2/6/zdUEFBII7f9cAMY3q4PvHEE1bxRLs0AncpCQYG4u/zsccec61atbK/T/6OqKS48sorTxugaN++vbWGI4giIpIaHEPIoLzjjjusYgFko55//vkReSFJeGCd4WV0sllD5Vo45mHs3bvX2mvQWopg8ciRI0P+mCIiknZUDdOC9fbbb4+Ll5FrdypDwoWuCB06dLD3eR3ZMwiHNWvWWMUmCZWeSK07REQk/ihIESZkR3/88cdWuZAnT54UfU3WrFntIv/FF1+0i/wxY8a4atWq2cV4SpHRSICCDVQ2X4cPH27zKqZMmeLfjKV/JZsK48aNs2oKZmjQckYkljEknt9l/oZee+01W8x7f3v87vP3WLp0aTdo0CDb7Lv77rvdzJkzU7WhRsCxdu3aNhQbvXr1ch9++KGVe6ckuLFv3z4LbCgwKCKpReYiQ7I5lnG8iyQqNQm4MueHxAha11E5Gg4Emxs0aGAzvIoUKeKmTp0alsoNEREJH1oXE4gOfIu0Y8eOuVWrVoX9cdetW+eaNGli1y9c41NFGS60i96yZYvr0aNH2B5TREQyEJ9EzMaNG33//PNPij73k08+8V144YVEFXz58+f3rVq1KtWPN2/ePF/hwoXtPnh7/vnnT/mcnTt3+urUqWP/v2XLlr7ffvst1Y8jEkl//PGH78EHH7Tf4WrVqvm2bdt20v//9ttvfdWrV/f/HfD39PHHH6f6cdatW+f/ezrvvPN8H374Yarv48SJE76FCxem+utEos2hQ4fsb4F/JXwmT57se+eddyL6ko8fP953zjnn2M+/TJkyvp9++ilsj/3vv//67rrrLnvsHDly+H744YewPbaISKyKtnM2z2XatGnJfk6vXr38a/fAt0h+D4MHD7bn0LFjx7A95t69e30FCxb0X+dwLRFOvN4dOnTw/f7772F9XBERyRhrD1VSRAitnOiB37RpU3f8+PEUlcTSZ5phWDt37rT2TCmdU+FhtgWDvcj2pl8mmRcJMUiYKoo333zTBpipqkJiCcOwS5Uq5SZMmGA9bulLTq/UQFu3brXPox0JrVKYU0GbkJTib475E9dff73btm2b9X5fvny5tVw5nb///vukwYA8h6pVq6byuxSRjGrTpk22BgislmRoZSRQDcaAbK/VHS07Pv/8c2t5ES6TJk2yytBMmTJZRSiVFCIiEn/I3D906JD/7eeff47o8+E5UFUAZj+GA+dazvu0V6StLOc/riVCiXaSVEd6LrjgAjds2DB34YUXhvRxRUQkY1KQIkJo88KGJQMeUzqYt2jRotbrnn6XBDaYU0H/e4Zrp9R5553nevfubW0R8uXL57/93nvvtQGXtE2g3U3r1q1tCCWDwNjAJZgSuDEiEk1ogcY8FQJxBA0IxvH3wYB4/lYYjO2hLQi/67RBe+GFF2yxnVK///679XxlU46Bf8y3oMybwEhKNvT422Ujb/To0Wn+XkUkY6KlEsHRunXrRrzNhdfqzhvY+fTTT9tmSbj7UtPuolu3bm7s2LE2i0JEROITbfxYswe+RdLgwYPdb7/9ZgmE9913X8gfjyQpZkuS6Mj3PmPGDHfRRReF9DHZq7jzzjut3TTneBERkVBTkCJCOOEvXrzYnwGYUmQtkIk9dOhQy5xggUJf/dT2o86SJYv/fTZZ6dtP8IJACFno9LjMnz+/VVXwMbM0WIQxHyO1w7tFQoWNf3qy83vL7yqb/1RJEKhgMU+WLcPdmAnDhQQIwjHM9fLLL0/VY3FRQNDugw8+sMDi888/7+bMmeNy5syZoq8/++yzbUA3s2b42xIRSQ0qHdmkIdkgkudh5k5QCcqxlucyefJk17dvXwsKhxuPybqkRYsWYX9sERHJuMmGXIuD6wHW+KFGBwWuc7iOef/99+36JtT4vgoUKGBrD/YGREREQk1BiggiIzIw65DB2LSeSclFeefOnd3KlSttgbJ7927bhCWbMC0bF2w2sPFaqFAh98svv9jF/rXXXusPfFBlwYDO+++/3wZzsVHL5oRIJNFWpHz58va3wIBW73eUv4+lS5daSzQqgLZv326BAYa8pTUQQlDjlltusfLqK664wjKa+VtIaRWUhwoOht3ddtttaXouIpJxcY4muYHzb65cuSLyHCZOnGhrF46rBHqp7qT1RDgRMCZrNSWtMkVEJDrRTWDt2rX2Blqo8j5r7Wg3btw4q6i+5pprrEI61BYuXOgeffRRe3/gwIFhu44gIPLyyy9bQiOViyIiIiEXriEZkrzp06fbEJHs2bP7duzYkeKX68iRI76HHnrIPzysXLlyNpA7LY4dO+YbMGCAL1u2bP77q1y58knPh2HBN910k/2/Jk2a+H7++Wf9aCWs9uzZ42vRooX9DlaqVOmkIfKLFi3y3XLLLf7f36xZs/p69+7t+/PPP9P0WFu3bvVdd911/vtjmDyDuVPqyy+/tCHeDHcViWfRNoQzHhw/ftzXvn37k45xkfLXX3/5OnXq5D8W1qpVy3fgwIGwP4/169fbOonnwBBVERGJzXM2a/bEBmGz1o7m7+G///7zFStWzB57zJgxIX+87du3+3LmzGmPd++999rjh9L8+fPtfB/qxxERkYzlUArP2wpSRAku9gkIpHVRQJDDW8BkyZLFN3r06DQvLtgE7tixo+/cc8/1FSxY0HfixImT/j/3O2HCBF+ePHlsE7hnz56+33//PU2PJZJShw8f9j333HMWRON3nQuDwM1/AmZnnXWW/Q1kypTJ98ADD6QriMbvuBewu/DCC30TJ05M1ddz8L3ooovs6wcNGpTm5yESC6JhwyPePPXUU/aa5s+f35IIImXnzp2+G264wb+B1L17d98///wT9udBwgSvhZdAcfTo0bA/BxGReBAP5+xIfQ9cW+TKlcuut0P92Jz7y5cvb99nhQoVQn7e27Vrly9z5sz2eOPHjw/pY4mISMZyKIXn7TP4T+jrNSQlaF3AnAmvrzM/GsosU2rXrl02PNhr03THHXdY/8rcuXOn6QdAG6kff/zR3XDDDf62N7TVoR2UN7iTklPKQJlxQfubRx55xGXOnDlNjyeSGFqY0YOVnucMru7QoYN76qmnbFgcA7KZyeJ58MEHrQVT9+7drYdqWjC7gpJq5rTgxhtvdO+88461WklLa5S33nrLZmNky5ZNP2CJW5wPmJl06NChiA+zjKfXtF69ejaUumbNmhF5DosWLXJ333239d/m5/r222+7hg0bhv15/Prrr9bCj7Z+zMf64osvXI4cOcL+PERE4kE8nLMj+T1wbcI1CG1nQ6l9+/bWDprz3Zo1a9J0LZJar732mp1j33zzTZtFISIiEs7ztoIUUYoABf31L774YhvIldKBlAy1eumllyxg8Pfff9uiZtCgQa5169apCngkhs3WVq1a2fssyujTX79+fQtmPPfcc27s2LEub9681nefQEY4hohJ/OJ3mcFwzzzzjPvpp5/sd4rfLYIPc+fOtffph75+/XpXokSJNAX2AvG1PN5jjz1mG3IEO3r16uV69OiR4t9l7oNACgGUwNvS+7cnEu3iYcMjGuzdu9flyZMn4scPjr8MpCYgzPsEgz/88EN35ZVXhv258DtVtWpV9/XXX9vxn3lEBQsWDPvzEBGJF/Fwzo6H7+F0cy/atGlja4DZs2eHLFmBJEnesmfP7r9N1y4iIhKp87YGZ0cpBv++8cYbFmBg0G5KEczo2rWrDdUuW7asZYW3bdvWLvDJQEyPOnXq2HBuhhB/9dVXlk3JxsWsWbMsMLJhwwYbqMnjcfvUqVNtc0MkNVgY8zvFMDqGtpcqVcr+BqgKYnAcg95r167tli9fbhk+ZBZ50rqZR8UQv9/Nmze3AAVBDzbCCJCkNEBx+PBh17hxY/tbO3r0aLqfk4hkLFOmTLFh1PwbyeMHVQtUSxKg5RzOkGqOt5EIUKBp06YWoKAqlEpRBShERCQSOD+G49qWc97DDz9s75OUFaoAxYEDB9ytt95q1/QnTpzw365rFxERiRQFKaJUlSpVrK0CrZQINqRWuXLl3KpVqyzIQVBhyZIlFjhgoRO4CEkNNgjIrNy+fbu10zn//PMti51SVLIbycCgrQ2Pmy9fPnfnnXfaBvOECROsqkMkOf/++6/74IMPLDhBixPaIxEooNUSFQ78jlERRFCC9mJUPBBcIJCRVvxe0rKsZMmSbs6cORb0oK0UFwfXXXddqiPDn332mQXr2NATEUkNjh8EODmPRvI5sH4ga5PWjbSZoIqSdUSk0O7qsssuswq6okWLRux5iIhIxkYyU+HChS1pKlQOHjxo19BUN5AwQEVjqNANgUSwb775Jt3JjCIiIsGgdk8xhAxvgg1ka6cGQQWyMT799FP7mH7Oo0aNcjfffHO6ng9tbehXOXz4cNtA5rl5Nm3a5Pbv3+9eeOEFy4qnhyZVGJStssEs4iFoRkCOANiWLVtcjRo1LIP3lltusUwe/j+Zs/z+83vG3JN27dq5nDlzputFpNqIAJtXqUQFxMiRI9O1CcYGH5UXVBSJZCTx3nYhXIFaKiipRqTdXDiRGUrAluoxngfrBIIlJBpEA/p/M7NLRETSLx7O2eH+Hnbs2GEBcyq+SZIiWBFs3HejRo3cjBkz7P7pXBDYQjYUCLhccskl/ta5IiIioaB2T3GGodW0PGjSpIlVR6QGCyoCBQzxpdc1AQQqNdjopR1UWlE50aVLF7d582bLgA8shaX646GHHnINGjSwuQGVK1e2YcQ8lwEDBtiCUjK2P//809qE0d7kgQcecGXKlLHKCYazDxs2zBbqoLqBAAb90LkoePLJJ9MVoODgyPBtAgkEKLiv8ePHuwULFqQqQMFGHtlNBDs8DHZVgEJEUnosevXVV/3HOgITBE7DHaD45ZdfLDjMLCuOa7R3oiIyUgEK1jusH6ho8yhAISIikURnAM7XJFGFIkABrn8IUHDOo/VjKAIUkydPdj/88IP/42rVqilAISIiUUPtnmIEGeU33nijZYowrDotX0+QY+PGjbYJAjI2ixcvbq0c0tNfkw0VMjA8ZH2QTU4rKDafmR9ApgtBErJDevfubRnxBC0CF0mSMRBoYG4KvwMEHOizSpCLFmH0RKWl07Rp0/yVP2jZsqW744470jWMnQsLHodMoREjRtjHbMbxN8H9p7b/KsG2/v37u7vuussdOXIkzc9LRDIeNuKp3urUqZNVMEQKmyEEiMmkPO+886w6kjUB7RwjgSAJx2Oq2pgTpGOriIhEGtcMJDShVatWIXkM2tnSdQCDBw+29rfBRuU6CY+0kaKtlIiISLRRu6cYQ8sbZkMEZmKmpcSVjHWCFWzQgk0KKjTIpgwGWkGNGzfOWkGxKe254oorLEvkiy++cKNHj7aqi1q1alkLn9tuuy3sGaQSHgTB6CdOcICqHqpw7r77btsIY5MssA8qrZ1oTxaMlk6Bg+gJjJAd7P0esgnGsLi04m+P7CPul+9FJCOLh9YR4fb666/bnKiZM2e6ChUqhPWxjx07Zseu1157zT5mM4TZP5Gc+UCAgs0f5hARkCaLlMC1iIgEVzycs8P5PXDdTLU01y179uyxoH4wHT582M7DtL0loW/q1KkhGV69d+9em7lH4hdV6rruFhGRqDtv+yRmbdy40ZcjRw7fq6++6vvvv/9S/fXHjx/3DRw40HfhhRfSa8LeatWq5Vu7dm3QnuM///zjmzdvnq9Fixa+8847z5cpUybf/v377f8dO3bMN2DAAF+5cuXssfPnz+/r06ePb+fOnUF7fImsPXv2+Pr37++77LLL7GdcpkwZ39ixY31HjhzxrVy50v97lyVLFt8999zj++STT3x///130B5/w4YNvgYNGvgfh99BfseOHj2apvv76quvTvr433//DdIzFYlthw4dsr8x/pWkJTxm/Pbbb2F/udasWeMrUaKE/7j4+OOP23og0q9Ly5Yt7fmcddZZvg8//DCiz0dEJJ7Fwzk7nN9D27Zt7bFat24d9PvmGr558+Z2/wUKFPAdOHAg7tYdIiIih1J43la7pxhGSwZmSpCJ7vW0Tg16/T/xxBOWtdG5c2eXKVMmN2fOHFeuXDnLZty5c2e6nyMZGmSr81zJ3iCb3suOz5w5s/v444/dd999Z20vGNRJ2wsy6WkRRTYlcwskthw9etTaKtGWLH/+/O65556zuRP8Htxwww02FDZr1qyuYsWK1taD9iJkJb333nvW3iM9LZ083N+DDz5oPdX5++D3kB7nW7dudc8++2yqh7dTCcKsjPLly7vp06f7bz/zTB1CRSRlqFrgOHjixAn/baEeiJmwUoE2dddee63bsGGDtWmcPXu2tZVgPRDJ1lesOVgncKymooMsTxERkWiwePFi+zcUldOc+959913/+S9HjhxBu++ffvrJKjVp6RiJdYeIiEhqqd1TDCMwQTul22+/3TaDvdvSWh7KBi6DgL0h2AQRHnvsMZsbQFlOsNFrmk3rb775xn8bg8JoN0ErCp4Pm9mUvd57773WiioYG9gSms0vFsAsshlwTXCJwAQ/P4Jgx48ft8+jrIsAQmqDBCnF4w4ZMsRal3m9zGkX8sILL1gQLD3oEzt06FC7b/4uRCS+WkeEEse9q666yl4n5kG1adMmrI9P28UWLVpYq0UQBBg1apTLlSuXizSOzz169LANGs4hzM8SEZHQiYdzdji/B9oPsuHPefTiiy8O2v0ym5HkQBK8nn/+edezZ08XTLRTJkHi6quvtutttXcSEZFoP28rSBFnyJKkv/8rr7yS5sDCypUrbUP2s88+s4/ZxCD7nBkWoci2XLdunZswYYL13wycX0E1xc0332z/j9kZPI977rnHNjDop6mFVmRRXcCMh0mTJlnmz+7du12RIkWsT+vatWtP+txChQpZsIlB1fRcDXaf1b///ttmoPTq1cs2A0G2MAEFesimNeBBhi/zM0Cghd9DLiZEJP42PEKNSsLVq1fbhnwoek0nhsQF5j8xf4JjWrZs2WwuFBst4XoOp8PmDMFkNlM4T4iISGjFwzk71r8Hrl1I1uNaihl38+bNC3qFNtcuHTt2tOv4AgUKBPW+RUREUkNBigw6VJvNYBYkZCPSniY9Gxu0YqKKYtOmTXZbvnz5XJcuXdwDDzxgg8OCjcdkE5j2PLwxPJkKCm6nxQ6tgcjYZ0ODUljaCfFWs2ZN23iR0KM6Yf78+fa7wc+IwedURfCzITOYkmIGwfJWqVIl+/k0aNDA2i6FYkOM3wXaRRGM2LFjh38oNsG6u+66K82P+eWXX9rfDwEVKouiZTNPJFrF+mZBKNDikGGYV155ZUQen2Mi7fU4ZoOA7dtvv+0uu+wyF2m0vKJy0ju2pqcKVEREMt45O9a/BxKraIlLMtS3337r74qQXsuWLXOVK1cOyn2JiIgEi4IUGRStHCZPnmxtaYJxwU8mOa0p+vbt63755Re7jQABWRm8efMlQo2e2VR3BKIdFQEZWkBVqVLF2lewKa5MkeDatWuXmzlzprVxWrRokWX+UFHz119/+WehUAVD2zFvY44qi7x587pQ+f333618+eWXX7ZACfLkyWMZysyeYPMrPch2plqHwBzv586dO0jPXCQ+xfpmQbBR0VirVi07P61YsSKsbZU4LhO8pS0dQRLOlQRuH3300aiYo3Pw4EE7V5M5ygaNiIiEVzycs8P1PXD9u2bNGmtjS9ukYGBdcOONN1ryHdXowZh1wbm/U6dOVi05ZswY165du6A8VxERkXCetyN/tSpBRdkoG7degIJsRdojsdGaFmywUDlBGyaCFbTzYVg3mfJUbVBZ4QUvQok2EGyUMwyZvt7w5hwQSDlw4IAtzBi6zSKSCgwGMbPBLqlDkIEWTrzWxYsXd5deeqm9T6sSAhTe7xWLYTKEWQTzunsIFoQqQMFzIxDB7x7zUwhQkBVMwGL79u32O5DWAAVVGR4qQgjK0IpMAQoRSS0GUxIQ4FzMgixcOA7SKpHjMgEKsinpQ925c+eoCFDs3LnTKjpIqHj11Vd1jhYRkahGghZV4ZxHg4HWi7S/JUBB1XawhnGz3vCGbrMBJCIiEos0kyLO9evXzz3zzDO2aUyggYzK9GBBRdZ8//79/XMHMmXKZO1+nnjiCQtihANzB5YuXeqWLFlimx28sYFOP08GjwUO46bao3z58q5evXq2yGRDXW0l/odAA0Em2jfxRl/U/fv32/9jc57qiFtuucVaKNG+iQARH1O5wrwQAhjh2nijpRPZwV5wimwmAhYE4dIzUJ0gF/dN5tFXX30V0goQkXgVD1mZwbZ582YLVoSjioJzM8Fajom05aPajXMhmyrRMr+Jdo5Ul/z8889WpTZ79mxrBSgiIuEVD+fscH0PL774orU/JrBAy8T0IvmPWVG0d6LNkzf7LljXdcyWpBpcREQkmqjdkxgqDJjtQJaG144nWIugOXPmWAsJggUgS5M5ALSUIHszUoEAr4rCa0WUEEELMuUJXBBU4bnyb7wHLng9yGKlZJnNeAI7n3/+ubVtSgzts6gm8L6WmSdUSYTz+RI0GT58uP082YTzBmL37NnTgk7ByAymNdX1119vcygI6lGhISIZb8Mjvcer3r17WwCXNkbh3vxn9sTy5cvtYyoVxo4d64oWLeqiBc+NYzaVmAS7WT9QESciIuEXD+fscH0PBPtfeeUVC1S88MIL6bovugLQ7hALFixI93qBIMeoUaPs+UVLQoKIiEhiFKSQJC1evNgNGTLEFjS0RkovNrsJVsyaNct/G1nuZIqQdRLMDJHUzCygmuLrr7+2ig/+3bBhgw38JojCJj1vVGSAzW4WulQG8JqQ3UkWSp06dWJu0Udgig13+p3Srmjr1q1WLcFrwoY8Lr74Yle2bFkbqMrmGt8jrZ24rVy5cvYvb17ZcDjRooTB7yy6vWod1KhRw7KEqeRIb0CJ1+OSSy7x/2z5XeD3gwBXvAerREIhHjY80uP111+3hADOd1RQhKN6gqoyNkw4/xJszpYtmxs4cKCde6OhtZNnxowZlihx7NgxCzKzSRPOGR0iIhJ/5+xwfQ9NmjSxeY9cN3MNmVYE6bk+5tqTdslci6e3bRTXrLS+ZS1AEEVERCRaKUghiWJDmgoCNu2Z80CWerAQFKDHNAPA2IxAlixZrB0PMw1oFxTJDWDmKNBmgjkKHiootmzZkuTXZM2a1T6HjFS+ltePagKGc5MFyuKQthW0RqIlUijwmGzcswjlbffu3RZ4+Omnn6wygrYefE98H5s2bTqp1VVCBCKYLUEwhp8Fsye8QXD8rCKJCg8CE1RNsPAG31vjxo3toqBixYpBeRx+51nIM1y+ffv2QblPkYwuHjY80hswIKhNYL5NmzZh6ZHNefWHH36wj2+77TY3cuRIOzdFG9YE9N3m9WGj57zzzov0UxIRydDi4Zwdru+B6kQqz7lm4pokrVq0aOEmTJjgihUrZtfh6W3B7J1faR1F5XskEstERERSSkEKSbY1xLPPPmsbwt6ChixMZksEI4hAxv4777xj979+/Xr/7WXKlLEMz+bNm0fNgpgAABv/bPozs4N/2eznNSITlexPNoF4o60V8wsSw0wEZjSQycob1Sr8EfKa8sZmu/c+Pcqp0pg+fbp9LrM0eONn4L2R5c/9EZzgjUzUpNpXgb6mBCoIprCI5udYuHBhC0pwOwEUghG8H86WTadD73QW2PyuBA53px0Ivyss6GnPFUwE0hiw3bBhQ/sZiEj6xcOGR1qyIgM3BahUC3UFA+erbt26ubfeess+5lzBMY1Wi9FcBUalZc2aNe0cKCIikRUP5+xwfQ9cQ3GNSKDihhtuSFebJ9YIdCBI68wI1hl834FdCsKx9hAREUkvBSkkVe6//363a9cuGx4cjBZQYFOdPtRkd7JxTiUDyKK85557bNg28yBiZWFFhr9XvUBVBeW6DJkmKEMQgl7kXlCBWQregOe0oKqBjXqCGLSoImuWAAnvszClVQabUwQnaM8UWBFAkOOcc85x0cob6kY2EcEsDlZg8+rOO++04ASvZbA23Hbs2GG/e95Qd15HhoQ3atQoqjf1RGJJPGx4pMayZcss0Nm/f387f4YaM3nGjBlj83gOHjxox66HHnrIHp/XPZpwTiQQTPuJvHnzRvrpiIhIHJ6zw/E9cM1CVT3XdAQqSABLLc7ZJUuWtOvsrl27ukGDBqXpudCloFWrVpZMt2TJErsmFBERiRUKUkiKsWgiMMFGbnqyRE6Xcfr2229bxjwtiTy0SqJ0ln6fZJXESsDidPge+SPkNSVowMKSFkYsctmMZ67CxIkTLWBDuS9vvM9CmAAD1QMlSpTw3x/3QyAkVnlDsAlW0e6DwIGH6g6CLCy8mZURTJ988on9fl1zzTVWCaOghEhoxMOGR2r07dvXKhKvv/5699lnn4V0dhEzhph3wewcryqRcynzHaLN999/bwFgzoGc5xYuXKjjrohIlImHc3Y4vgcSBLh2odUubZLTci3WunVrN378eKt2Z9ZeWtvrkihH61sSAT799FNXvXr1NN2PiIhIJChIIaneWGBD97HHHvPfRrY7swqCmanBZjUbOm+88Ya12vGy6EFVgBewYPNFG8qxjZ81m2peYGL79u3+/0dApkGDBta7vVq1aiELTjEgmyAIi3p+39SvVSQ04mHDI7XHtxEjRtjmQ6hmLNDa6amnnnJjx461x+N17devn1VQ0GIw2tCWkJkc/C4w94iKNSr9REQkusTDOTsWvgeurevWrWvXtCQC0kEgPVasWGEJbyQBiIiIxBIFKSRdaFlESSubx8xXCMzqDxaqAxjizCb2Rx99ZI/pKViwoD9gwQazAhaxgY00hsHxM+Vt27Zt/v/HRh79WPmZ1q5dO+iDuqlYYTOPjb1evXr5b2eQeOnSpfU7JJLBNwvSg7lBBNdp7RTKqgnvsV577TXXu3dvy5gE83lefPHFqJopFNgPm+dKdYk3ZJTAdDQ+VxERiY9zdrR/Dzwvkv1ImCIJ8KWXXkr1fVAFTutfBfxFRCTWKUgh6bJu3Tp3xx132GbMd999F/KsTbJC5syZYxvbZGPSGimwwqJGjRo2dJPS1mC3BJL0t/JasGCBmzdvnr0FVkzQvqpevXoWmKhTp459HCos5JllQTutzZs3u0KFCoXssUQktjYL0ht8Zf4ElQGPP/64Gzx4cMgei8B9586d3caNG+3jsmXL2mBsNv6j9eferFkzG4yNjh07uiFDhmhAtohIFIuHc3Y4vocffvjB2hcyJ5C31KBN4+uvv24V3SRMpfYaiGsqrqFoAUzbR66HRUREYlVKz9vR1y9AogKZ52ySMCDaC1CwUdO0aVPbbKalQzADF8xkYBOIN+Y3BAYsGFQ9btw4ewPzBbygBfMzYnlWQyyiYoGB6Cye2VBbvXq1/W54qJCgtJnAxG233RayVij0iSUgcsUVV9jHN998s7v33nutRzxDxUVEgoFKPjbiFy1aFLIWC2yCdOvWzc2cOdM+zpUrl3v++edd27ZtQ165kR4EhZkxxDl89OjRtjYQERGJB1OnTnU9evRwLVu2tLkSKfXFF19YgAKcG9OSpFWpUiVXpEgRV6xYMQtUiIiIZARn+AJ3F0WSweYJ7XpYaNHGJ3fu3CF/vQhYMMPC2xCnwiMQG+JsThOwIHBBWW28DN+OFhwi2EDzfga0/zpy5MhJn8Pr7gWO+HmEKjDh2bJliw1nJQpL1QQbZCISOfGQlZnYsS+w1eC+ffuCft7bv3+/69Onj21mEHgl+N+hQwcbyn3RRRe5aER7J3jnWs4PR48etQQCERGJfvFwzg7H99CpUyerZiRQ0b9//xQnc9GeacOGDTa36s0330zzuoM1AvP0dG0rIiKxTpUUEnQMOKbNBRspgRs1zJMgu5SFYrARhGDjm7dBgwa5PXv2uPnz5/s3zPmYqgvewHOoUKGCZZ8wy4J/GeApKbd79263atUqe6O8mH8PHjx40ufw87/11lvt58K/4X6NKXnmooS2YN9++639rEVEgoXWTpxzGHp5/vnn223BDFAwk2nYsGE2CJtjGWjrwNyJ4sWLu2jFzB8ySqliZKg3yPIUERGJN7t27bJ/8+bNm+Kv4TxOgIL2xKwjUooZVMxjfOSRRywRy6uqFBERyUhUSSHp8uOPP1qvTYZ68X44y1HJNmFehhewWLJkiVVeJMTCkmCFF7ggiBGtGarhxubYV1995Q9G8C/ttRKipRY90b2KFdqBhSurh00xFvz8rNkw9PB8KYPOnj17WJ6HiMR3VqaHSjHOawTBGQjdq1evoFYhvPvuu+6ZZ55xP/30k3/uBHMcSASIZrNnz3Zt2rSxQDaBGyoqtYEiIhJ74uGcHY7vgYD8smXL3JQpU9ydd96ZohkWXCORiMC5nlaRKdW3b1+roiQhgvNrKOf4iYiIhJsGZ0tYsHBr166dDSn+9NNP/bczK4DbAktWQ+3vv/+2jezADXc+pvIjITagyP4sWrSobXTzL29UBITzOYcrmMOmEgtn3miP5A2C49+E+P5p3+RVovBvqVKl3DnnnBOR589GIb9LlE/zM1XVhEj0iYcNj0ArV650EyZMcEOHDg3KEGiOwwTTn3zySRugiXz58lklRYsWLaJ67gQVa127dnWjRo2yjzl3MjOK84KIiMSeeDhnh+N7KFy4sF3Tcr3LzLvTnedJNqAtbq1atey6ODXXlP/884974IEH3KOPPurKlCkThGcvIiISPRSkkLBhUUY7IHpmelmoBQoUcAULFnSzZs2KaLslnsvXX399UuuirVu3Jvn5ZK0EBi14u/zyy12ePHnsjYqRaAti8PqzibR37157I/vGC0h4bwlnSAS67LLLTmqPRV9xr71JuBGIIFuJC4KePXv6byfL+KqrrrJB3OrLKhJ9Yn3Dg2Pkzz//HJLWRQQ8OJ4tXLjQPuZ16t69u21ERHumJMM/CaJQKen15x4wYIC1YhQRkdgU6+fscHwPXF8x845rE66tuF5Kzrhx46zakPMjSXIEOE5nzZo1Nr8i2q4tRUREgk1BComYpUuXujp16libJTbIvU1lNoFCPVA5JQ4cOGCZrIGb+FQXsAlDFktyWKxShusFLRK+T6CG1kh8XmL/eu8zHBU8HiXBx48fP+nfhLfRp9QLQvDGANfA9xNrcxWILF0Wy4EBGN6nzQg9U6MFQSQCJVRt0ArlkksuifRTEpE43/DgOFq3bl3rPU1AgSB7MDAvh7ZOzG0CxzWGYhOwCGdrxPT8TJn/c/jwYUs6YAMm2ltSiYhIfJ+zw/U9/Pbbb/5zNddiXMMlhQHXJFPxNbSo7dat22nv/+WXX3ZdunRxL7zwgnviiSeC+txFRESijQZnS8TcfPPNlpFK1okXoCAbpXz58ta/+s0337RN8khhwclGS8LNFtpFkcGfsAqB2wgEUK3AInXHjh32Fm0IABEooTVSYCUIbwQoItWuKbmDFFUT9GinZRio5rj99tutmoNgjohIqHGs4djOOYD2cukNUhD07tOnj3vvvffs3Md5sFWrVtZrmuNzrGDTh5kcBFvYTGEzSEREJKOsDd5//30LQCQXoECPHj0sQME8isceeyxF98/agDUC15T8q2oKERERDc6WMGH+AXMOWOSRtUrbJDArgaBBtG2gJ+bo0aOnVDAkfJ+Kh8BKiMBqiMRmYyRcrLIg9t4CKzDYHEqsciPw32ioUkmNqVOn2hA62oFRNRHNPdlFJL6zMtkkIEhxxRVXpPk+qMZjxsTbb7/tP943btzYPffccyFpIxVsnMM6d+7s2rdv76pWrWq3aeNERCT+xPo5O5q+h+XLl7vKlSvb+59//rkN206pRYsWuVtuuUUBChERiXt/pPC8/b+eMyIhxgYN1RVfffWVP0ABNkNY3FFd0aBBg6j+OdA7nH6kp+tJmhSvtRNvGzZssNsoDeZ+CUR4LaDi0fr1693YsWNt6FzTpk3tNtqr8HG9evWs36t6nItIuLzxxhsWIG/UqJF9TDujtCLI+vzzz1s7JK9lIMc3ghNUhUU7gjOvv/6669WrlwXaaYfIMZvAuTI7RUREEsc5/+GHH7b3W7dunWyA4pdffrHWTszZ85LzvIQAERER+Z/43RWVqJMvXz5787BZz2YIMyIC2z8x2Joe2GXKlImrDRKCELxR8XDjjTe6eEb2rdfmBDNnznSvvPKKDTH3ghQEZpYtWxbhZyoiGc2MGTOsxRyBUc5BzOdJC1oaMkR6/PjxttGPWrVqWauna6+91sWCefPmWfWEFzgnqDJ69Gj/sVtERCQj4pqFKstSpUq5yy+/PNHPee2119zatWvdRRdd5AYOHJjkfVFdWaNGDbdx40a7Fhw6dGgIn7mIiEjs0lWoRAyb1GzyUBob2Apj+PDhrly5cinu6SnRhR7mVJuw+eVp3ry5a9KkiQbDiUjE3Xbbba5OnTqua9eu7sorr0zTzAkyJglujBkzxgIU1atXt3PZ7NmzYyJAQTIAVSQ1a9a0AAXzokaNGuW+/PJLmx8lIiKSkVHlz3mSSvDE0LL46aeftvdJWLj44ouTvC9a2lJBQcDj0UcfDdlzFhERiXUKUkhEsWhLWBpL6SwtkBjA7dm1a5erXbu2GzFihGXoS3Qgw4ihcgnLmbn9k08+8d/GINoPPvjAWqCIiIQb1XneuYMsRqopaMeUmmq97777zt17770WVKd6gsxIKicITsyfPz9Vfagjje/lo48+snNwp06d3A8//GDtFzUbSERE5H/XngjsAhCIRAfWFhUrVrTqzMTw/z0kR6xZs8YVLlxYL6+IiEgSFKSQqDNs2DBrAcWsAs+sWbPcnDlz3IQJE07aVGIgN0OpJTwCA0QHDx60hXazZs1s3oiH3qwff/yxZRWJiEQalQ+0MXrxxRf9t6VmBtDq1avdHXfc4UqWLOneffdd999//1k1xooVK6xyIhaCE7/99ptbsmSJ/+P69eu7bt26uXXr1rmXX37ZWlWIiIjI/1VKJBWkWLx4sXvvvffsmpSZTgkD/KwTOMdWqFDBrmk98Tx/UEREJBgUpJColDlzZv9QMdDHc9CgQa5Dhw7+28hiZbYDk+G///77k26X4GJzi9eagISHTS0253jbv3+//3ZadRFgohpGRCTSFixY4LZs2WLtjI4cOZLigOyiRYusUoIsyWnTptlmxJ133um++uorC5zHQlunX3/91dpR0E/79ttvt8HY4HshaFOiRIlIP0UREZGYqaSg4r9jx472/oMPPphoi0QSuSZNmmRVioGV5SIiIpI8hfMlJjDjgLLaQNu3b7eMFIIZV1xxxUkzEai46N69uy0eJXXIDPr0009tAV6pUiW7LVOmTO6LL75wuXPnts07r5pl4cKFygoSkajGeeCvv/5yTZs2deedd16yn0v24/Tp020AJvMZQIYkAVrOKbGyqU/bvcGDB9sQ7KNHj9pt9MJm0yV79uyRfnoiIiJRi2udpCopGJa9fv16lzNnTtevX79Ev57/R3Dim2++OSnBS0RERJKnSgqJWQQmWEDSyiOwfHb58uXup59+OqktFNmkVatW9Q84k//1SX3rrbdc//79T3o56FP+zjvvWPaxh1Yp9GCn93oglS2LSDRuLhCoJjDhYVBlnjx5kvwa2gYyHJMgBNUSBCio6KN9HeeYt99+OyYCFJz72rRpY5UTtHEiQEGW59SpU93atWtj4nsQERGJdItEbw1xySWX+G/ft2+fe/bZZ+39559/3uXIkeOkFsQkdHmuvvpqBShERERSSZUUEtMIROTNm/ek22jLwQZT4GYMgQv6hxKsCMx6YdDZjz/+6Hr16uWqVKniz6TlflMzUDXazZ071+ZE3HLLLbYBBxbfrVq18m/gnX/++fb+Pffc44oXL+5q1qzp/3o261q2bBmhZy8iknKdO3d2r776qgVax40bl+yxnJYMI0eOdK+88orbu3ev3UalwSOPPGLHRarHYgnnLwLKBGpuuukm99RTT9mxPJ7OZyIiIuFo9ZQrV66T2g/37NnTHTp0yJK3AodlUzHBNRbnWq45r7rqKv2ARERE0kBBCok72bJlc9WrVz/pNgaXjRkz5pSNmqVLl1qWbGCFBX3IGZJK/242ezz0IOfrr7vuupMyZyKF3uK0vKJ9SZEiRfzzOAgy0OqDDTqCC6ACYvjw4e7YsWP+IAWlyHyPlDGTRewFKRo1amRvIiKxqHbt2hZ4oF1dUpvzHDsJTHBe8OZU5M+f3wIc7du3t/NILFRNcI4is3PEiBF2W+HChW1+0/XXX+8qV64c6acoIiISc1gPTJw40Z04ccJ/26pVq9ybb75p7w8bNuykYdlFixa1wMSZZ54ZFdeIIiIiseoMH+l2IhkUC84NGza4Bg0a2CBosJnPPAaGP1N94ClWrJgN6KYiw6u6mDdvnuvWrZt9zIaXZ+jQoRZEaN26tc3TwM8//2xlwFR+3HzzzVbdQHCADbVzzz3XPmflypUWJClZsqQ9vuf++++3wAPzIrx+4pQZE1xp27attSnx8P/J8lm3bp31IPeCMTNnzrQsn9tuuy3Er6qIZDR//PGHu/DCC+3Yc8EFF4T98QNn5WDnzp22yZAQVXZDhgxxU6ZMsaoDcJx84oknbGYF83eiGecMWvK98cYbbv78+f7ve+vWrRagEBERifZzdqx9D6wXCP6zhrjvvvusBWTCdceBAwdc1qxZXZYsWUL6XEREROL5vK1KCsnQKlasaG+BCAiwmR/YzxxlypSxhWfgxte2bdusxLdAgQKnDFXbsmWLtdnwghQrVqywKgcCFEuWLHF169a1ygeCD95QNm7v0aOHtVYKDFJMnjzZ/piZweEFKfgaeqx71RIegiUskgOfJ4/Jm4hIPAabu3btanMXqBBD4PGP4+yMGTPcSy+9dNJcnRo1arjHH388JtohEcCmWoIABQs8T7Vq1SxQHdgzW0RERIKHGX4EKKiyHDhwoFVYUHXJ9SIJafDWHyIiIpJ2ClKIJEBVA5UMCX3wwQen3Fa/fn1XsGDBU1qDtGjRwu3Zs8ddeuml/ttYvDK8u3Tp0vYxrUgSFjLx/1js3nDDDSfdTtUEAZLA/uh8nrcwDqTZESKSURCA4Ji3ceNG6xU9atQo//87fPiwzaQgcMvsIVAp0axZM9elSxf/sTga0YKK783LMvnuu+9sGDgIijNPiOO/qidERESC67PPPrPKiPLly9t5uHv37nY7Q7OpiKeCnWoKksioUCdpTERERNJP7Z5EREQkZltHsIHft29fmy9BwJhZDfSLZhOB5wN6RD/wwAOuQ4cO/sq1aELAmsq82bNnW2u+hQsXun79+lmFiDfgu1evXu7uu++2uUj0vRYREUkLtXtKHrMJp02bZhWMJDnQJpK5E99++60N0qb900MPPWRz/qjGFBERkeSp3ZOIiIjEnX/++cfa6TEnCFdffbV7//33bebPyy+/bBsL3rwJBlkyDJvqNtrgRdt8CWZjEJDgjeBKoNWrV/vfZ2bSq6++GoFnKSIikrHs2rXLn0DgzRykAvPss//XhIJEgcDKTREREQkOtXsSERGRmEAbJKoJCEgsX77cZv5MmjTJNhG++uor/+fdeuutFpyoU6dOVFQdEJBgfhGVHV7WJc+LntbHjh2zj9n8YDAn84h4K168eISftYiISMYNUrz33nuWGFGuXDlbU1BVwUwKERERCQ0FKURERCQmsLG/f/9+29jv06ePVSDs3bvX/l/mzJndfffd5x599NFE5wqFy86dO92GDRssKOG9MTODGRNUf/A+aBlx//332/NmADaziM4///yIPW8REZGMjkpM5gpi2bJlNsvqnnvucU888YSdvwlaeBUVIiIiElw6w4qIiMQZ+igPGjTILrTLlCljMxoqVaqU5Ocz/PGZZ55x27dvd0WKFLFMQYZBRhs2CBgcTSskWjyBGROPPPKIVSXkypUrLFURO3bssNeKFk1UR3izI9CoUaOTqjo8PDde28ANDq+NhIiISEaU2vVKqDEw+++///Z/3KlTJ9etWzdLfqAS8qyzzorYcxMREYl3ClKIiIjEkQ8++MB6J48cOdJde+21NqehVq1a7vvvv3e5c+c+5fPJFCRLcMCAAdZmiPYGbLSvWbMmohUJnk8++cR9+umnbu3ate7zzz/3384AaTYPGFxJpmNasiVpH3X48GH/GwGH3377zf3555+udevW/s/l9Zw7d67bvXu3/f9APDb/32srxWvG15cuXdo2XHgrW7asu/TSS90ZZ5yRrtdCREQko65XwtnqCRdffLF7+umn7X3aR4qIiEhoneFjIpSIiIjEBS70K1as6IYPH+7fjKf6oGPHjq579+6nfH7Tpk1ts37mzJknBQDYWGfjIDEnTpywN88ff/xhj8Em/wUXXBCU7+Ovv/5yjz32mHvttdf8t7HJT+VE4cKFXfbs2a09kldRAbIdqbKgtRKZkIFvVC+sW7fO/7lsOMyePTvRxybgwNd4gYcmTZpYtYnnvPPOs3kYhQoVsjeyQLkNLKsUjBARkWjFOfvCCy8M6jk71OuVcKw7wJqiWbNm/ue3YsWKoN23iIhIRvVHCtceqqQQERGJE2zs02qoR48e/tvYaGeQNIOmE8PtZDIGIpNx+vTpST4OVRfMhAglggrz588/6TYCAL/88ou9gUBFIKotFi9enOj90aIhMICQJUsW/+vDQilbtmz2b44cOVzOnDltM8T7HIIftJPKmzevvV100UVJBiIUoBAREQnueiUc6w6vutRbg7z66qshfzwRERH5PwpSiIiIxAmGSlNFkCdPnpNu5+NNmzYl+jX0gU7s873BkYlhUyEwsOFlNAYTmxX9+/d3ixYtcsWLF7dB04GBAN7OPffcU55X27ZtLSBBGybe+Drv30Djx49377zzjgUiThdYINNTREREIrNeCce6A4MHD3YFCxa0KopIzsYQERHJiBSkEBERkVQhOJAwQBAKzJvgLaWqVauW4s+NZIsLERERib51B49B9aSIiIiE3/+aLYuIiEjMy5Url1UR7N2796Tb+fiSSy5J9Gu4PTWfLyIiIhLu9YqIiIjENwUpRERE4gQtjcqXL+8WLFjgv41BlHx8/fXXJ/o13B74+Zg3b16Sny8iIiIS7vWKiIiIxDe1exIREYkj9Gxu2bKlq1ChgvVTfvnll92RI0dc69at7f+3aNHCXXrppTaEEp06dXJVqlRxQ4YMcXXr1nUTJ050q1evdqNHj47wdyIiIiIZdb0iIiIiGYuCFCIiInGkadOm7tdff3XPPvusDb8uW7asmz17tn845Y4dO2wotady5cruvffec08//bTr2bOnK1KkiJs+fborWbJkBL8LERERycjrFREREclYzvD5fL5IPwkRERGJXX/88Ye78MIL3aFDhzSQWkREJIrFwzk7Hr4HERGRjOKPFJ63NZNCREREREREREREREQiQkEKERERERERERERERGJCAUpREREREREREREREQkIhSkEBERERERERERERGRiFCQQkREREREREREREREIkJBChERERERERERERERiQgFKUREREREREREREREJCIUpBARERERERERERERkYhQkEJERERERERERERERCJCQQoREREREREREREREYkIBSlERERERERERERERCQizo7Mw4qIiEi88Pl89u8ff/wR6aciIiIiyfDO1d65OxZp3SEiIhJ/aw8FKURERCRdDh8+bP8WKFBAr6SIiEiMnLsvvPBCF4u07hAREYm/tccZvlhOoRAREZGI+++//9yuXbtctmzZ3BlnnBHUjAsCHz///LO74IILgna/8UKvj14b/d7o70rHnOgRK8dkLv/ZJMiXL58788zY7P4cqnVHLP0cY5leY73O8US/z3qd48UfITz/pXTtoUoKERERSRcWGvnz5w/Zq8giSRsFen30u6O/q3DScUevTTz/7sRqBUW41h2x8nOMdXqN9TrHE/0+63WOFxeE6PyXkrVHbKZOiIiIiIiIiIiIiIhIzFOQQkREREREREREREREIkJBChEREYlK5557ruvVq5f9K3p99LujvysddyJLx2S9PhmBfs/1GscL/S7rdY4n+n3OGK+xBmeLiIiIiIiIiIiIiEhEqJJCREREREREREREREQiQkEKERERERERERERERGJCAUpREREREREREREREQkIhSkEBERERERERERERGRiFCQQkRERKLSiBEj3GWXXeYyZ87srr32Wvfll19G+ilFhaVLl7r69eu7fPnyuTPOOMNNnz490k8pagwYMMBVrFjRZcuWzeXOnds1atTIff/995F+WlHh9ddfd6VLl3YXXHCBvV1//fXu008/jfTTikovvPCC/W117tw50k8lKvTu3dtej8C3YsWKRfppRY1ffvnF3XvvvS5nzpwuS5YsrlSpUm716tWRfloSxPXF5MmT7Xeez+fn+8knn+j1DeJrPGbMGHfTTTe5iy66yN5uvfVWrflCvFaeOHGiHctZJ0nwX+fff//dPfLIIy5v3rzu3HPPdUWLFtVxIwSv88svv+yuuuoqO/cWKFDAPfbYY+748eP6lQ7iNeTixYvdNddcY7/HV155pRs/frwLJQUpREREJOp88MEHrkuXLq5Xr15uzZo1rkyZMq5WrVpu3759LqM7cuSIvR4s5OVkS5YssYvCFStWuHnz5rm///7b1axZ016zjC5//vy2+f7VV1/ZBmq1atVcw4YN3XfffRfppxZVVq1a5UaNGmUBHfk/V199tdu9e7f/7fPPP9fL45w7ePCgu+GGG1ymTJks6LdhwwY3ZMgQ22iV+FhfLFu2zN1zzz2ubdu27uuvv7ZNXd7Wr18f9ucer68xm2C8xosWLXLLly+3zUbO3QQAJXivs2f79u2ua9euFhiS4P8+//XXX65GjRr2Ok+ZMsWSZQjEXXrppXq5g/g6v/fee6579+72+Rs3bnRvvPGG3UfPnj31OgfpGnLbtm2ubt26rmrVqm7t2rWWvNOuXTs3Z84cFzI+ERERkShTqVIl3yOPPOL/+N9///Xly5fPN2DAgIg+r2jDUm7atGmRfhpRa9++ffYaLVmyJNJPJSpddNFFvrFjx0b6aUSNw4cP+4oUKeKbN2+er0qVKr5OnTpF+ilFhV69evnKlCkT6acRlZ588knfjTfeGOmnISFcXzRp0sRXt27dk2679tprfQ888IBe9yC9xgn9888/vmzZsvneeustvcZB/F32XtvKlSvbub9ly5a+hg0b6jUO8uv8+uuv+y6//HLfX3/9pdc2hK8zn1utWrWTbuvSpYvvhhtu0OsepGvIJ554wnf11VefdFvTpk19tWrV8oWKKilEREQkqpCBRLY35f6eM8880z4mw04kpQ4dOmT/5siRQy9agH///ddaPZBRRdsn+R+qcMgYCzz2yP9s3rzZ2gNcfvnlrnnz5m7Hjh16aZxzM2bMcBUqVHCNGze2FnPlypWzjFmJn/UFtyc8JpDdq/VI8F7jhI4ePWqVkDp3B/d3Gc8995wdq6gMktC8zpwXWFuxpsiTJ48rWbKk69+/v629JHivc+XKle1rvJZQP/74o7XUuu222/QyB0kkzn9nh+yeRURERNJg//79tpBnYR+Ijzdt2qTXVFLkv//+s7JkWrFwgSjOffvtt3bhTL/e888/302bNs2VKFFCL83/789NewHaPcnJ6AtND2L6PtPqqU+fPtYmhHY3zH/JyNgUYd4LLSpoMcHvz6OPPurOOecc17Jly0g/PQnC+mLPnj2Jfj63S2jWcE8++aQFRRUwDu7rTJs+WuLQtkVC9zpzXli4cKEF9Nk037Jli3v44Yct8EZrIgnO69ysWTP7uhtvvJEOQe6ff/5xDz74oNo9BVFS578//vjDHTt2zGaBBJuCFCIiIiISd8hgYxNVvfP/D5vMbE5QYUKfZDZRmeOR0QMVP//8s+vUqZPNMWFYo5ysTp06/veZ1UHQolChQm7SpEkZPhuXYCiVFGTJgkoKjjsjR45UkEIkDZidRNCYORU6HgfP4cOH3X333WeVXrly5dLvZojPC1SrjB492p111lmufPnyNl9l0KBBClIEEccIzr2vvfaarUsIBrGW69u3r3vmmWeC+VASRgpSiIiISFTh4olF/d69e0+6nY8vueSSiD0viR0dOnRwM2fOdEuXLrWB0fI/ZHdfeeWV9j4XzWR9v/LKKzYoOiOjXQCDGa+55hr/bWT08fszfPhwd+LECTsmyf9kz57dFS1a1DYEMrq8efOeEuQrXry4+/DDDyP2nCS46wtu13okPGu4wYMHW5Bi/vz5FhCV4L3OW7dutUHO9evXP2kzHWeffbYNd77iiiv0kgfh95nzQqZMmU5aN3BeICudtkasxST9rzOBCAJvDHJGqVKlrI1p+/bt3VNPPWXtoiR9kjr/XXDBBSGpooB+aiIiIhJVWLyzgbpgwYKTLqT4WP3zJTmUexOgoI0RpfaFCxfWC5YM/q7YgM/oqlevbq2wqDLx3siOp1UD7ytAcbI///zTNrzYiMnoaCfH5l6gH374wSpNJD7WF9we+Pmg6krrkeC9xnjxxRctA3r27Nl2/JXg/i4XK1bslPNcgwYNXNWqVe39AgUK6CUP0u8z5wWC+F4QyDsvcM5UgCI4v8/e7JqEgQhvvfa/udCSXhE5/4VsJLeIiIhIGk2cONF37rnn+saPH+/bsGGDr3379r7s2bP79uzZk+Ff08OHD/u+/vpre2Mp99JLL9n7P/30U4Z/bR566CHfhRde6Fu8eLFv9+7d/rejR49m+Neme/fuviVLlvi2bdvmW7dunX18xhln+ObOnZvhX5vEVKlSxdepUye9Nj6f7/HHH7e/KX53vvjiC9+tt97qy5Url2/fvn0Z/vX58ssvfWeffbbv+eef923evNn37rvv+rJmzep75513MvxrE6vri/vuu8+Ojx5+5/kZDx482Ldx40Zfr169fJkyZfJ9++23Efwu4us1fuGFF3znnHOOb8qUKSedu1nvSPBe54Ratmzpa9iwoV7iIP8+79ixw5ctWzZfhw4dfN9//71v5syZvty5c/v69eun1zqIrzPHYl7n999/3/fjjz/aevaKK67wNWnSRK9zGq8heX15nT28rqxpunXrZue/ESNG+M466yzf7NmzfaGiIIWIiIhEpWHDhvkKFixoF66VKlXyrVixItJPKSosWrTIFpYJ37jYzOgSe114GzdunC+ja9Omja9QoUL293TxxRf7qlevrgBFMhSk+D9Nmzb15c2b1353Lr30Uvt4y5Ytof+ljREff/yxr2TJkra5UqxYMd/o0aMj/ZQkHesL/vYTnk8nTZrkK1q0qH3+1Vdf7Zs1a5Ze4yC+xpybEjt3swkpwf1dDqQgRehe52XLlvmuvfZaOy9cfvnlFsj+559/9OscxNf577//9vXu3dsCE5kzZ/YVKFDA9/DDD/sOHjyo1zmN15D8y+uc8GvKli1rPxN+l0N9TXUG/wldnYaIiIiIiIiIiIiIiEjiNJNCREREREREREREREQiQkEKERERERERERERERGJCAUpREREREREREREREQkIhSkEBERERERERERERGRiFCQQkREREREREREREREIkJBChERERERERERERERiQgFKUREREREREREREREJCIUpBARERERERERERERkYhQkEJEREREJMa9/vrrrmDBgu68885zd9xxh/v1119P+zXff/+9u+SSS9zhw4dD8pzOOOMMN336dHt///79Lnfu3G7nzp0heSwRERGJnMBzfjTcTyRs377dnv/atWsj/VREYpKCFCIiIiIiMWzq1KmuW7dubtiwYW716tUWdLjrrrtO+3U9evRwHTt2dNmyZQv5c8yVK5dr0aKF69WrV8gfS0REJN7s2bPHztmXX365O/fcc12BAgVc/fr13YIFC1ws6t27tytbtuwpt+/evdvVqVMnIs9JRCJLQQoRERERkRj2/PPPuw4dOriGDRu64sWLu7feest9/vnn9paUHTt2uJkzZ7pWrVol+Tn//vuv+++//4L2PFu3bu3effdd99tvvwXtPkVEROIdGfrly5d3CxcudIMGDXLffvutmz17tqtatap75JFHXDyhwpMgTLT5+++/I/0UROKeghQiIiIiIjHq4MGDbs2aNa5u3br+2/Lly+dKlizp5s+fn+TXTZo0yZUpU8Zdeuml/tvGjx/vsmfP7mbMmOFKlChhmwQEM1atWuVq1Khh1RAXXnihq1Klij1moM2bN7ubb77ZZc6c2b523rx5pzzm1Vdfbc9t2rRpQfv+RURE4t3DDz9sbYS+/PJLd+edd7qiRYvaObVLly5uxYoVSbYa+v333+22xYsX28f8y8dz5sxx5cqVc1myZHHVqlVz+/btc59++qklOlxwwQWuWbNm7ujRo/77ueyyy9zLL7980nOiCoJqiKQ8+eST9jyzZs1q1R/PPPOMf6Of9UafPn3cN998Y8+HN25L2O6pcuXKdj+BaGeZKVMmt3TpUvv4xIkTrmvXrraeoeXltdde6/9+k8Jj0CaTig1eA57flClT/P/fey0/+OADW/OwtiHJgsSN5557zuXPn9/WSLwGBIsS2rRpkz13vo712JIlS5J9PiLyPwpSiIiIiIjEqB9//NH+vfLKK0+6vUiRIv7/l5jPPvvMVahQ4ZTb2ZQYOHCgGzt2rPvuu+9sjgTto1q2bGmVGWyGcN+33Xabf5YFF+3MwTjnnHPcypUr3ciRI0/ZVPBUqlTJHltEREROj+pDNsKpmGATPiGSC1KL4MLw4cPdsmXL3M8//+yaNGliQYj33nvPzZo1y82dO9daSKYHrSQJPGzYsMG98sorbsyYMW7o0KH2/5o2beoef/xxC7TQ3ok3bkuoefPmbuLEic7n8/lvI3BAwsNNN91kH1NJunz5cvu8devWucaNG7vatWtb8kRyCJoQ8CFQwuPcfffdbuPGjSd9Tvfu3V2nTp3s9lq1atn3MWTIEDd48GB7LG5r0KDBKY9FC06+v6+//tpdf/311pbrwIED6Xo9RTICBSlERERERGKUl+lI4OD888/3v3300UcnZUEm9NNPP9lFfkJkOb722muWAXjVVVdZBiRZlvfee68rVqyYZVmOHj3a7tvLDKRig6zBt99+26ozqKjo379/oo/LY/LYIiIicnpbtmyxTXrOwcHSr18/d8MNN1g1Rdu2be18TmUBH7P5z1yrRYsWpesxnn76aVtLUIXBJj3VDlRxguoF1ipnn322tXfijdsSIniya9euk9pXEki55557rNKBas9x48a5yZMn2/O+4oor7HFuvPFGuz05BDPatWtn1R59+/a1xI2EgZnOnTtbEkbhwoVd3rx5LThBEgYBDdZIJHVQTZGwyoTACQEQ1ky8rlShvvHGG+l6PUUygrMj/QRERERERCRtCCKA1gaB2ZRk/nn/LzHHjh2zNgQJUQ1RunTpk27bu3evbTbwGLSEYFYFQQo2B0CGIQM8A4MeZA4mhk2I5IInIiIi8n8CqwiCJfA8nydPHn9LpsDbaC2VHlQ8vPrqq27r1q3uzz//dP/884+1kkqNiy++2NWsWdNaLRGE2LZtm1VNjBo1yv4/szlYkxBoCEQLqJw5cyZ73wnXKXwc2CoLgRWnf/zxhwVMCO4E4mOqMZK6bwIx3E/CKg0ROZWCFCIiIiIiMcrbVODCP7Dl0/Hjx0/acEiI+RLMs0gsiEB2YiBaPdGmgDYHhQoVsj7MXID/9ddfaWpbwaaDiIiInB6VkpyXqVhMzplnnnlKUCOpYc/MdPBw34Efe7fRyjHwvhMGS5IbJE0ggRZKzJ2gJRKVBLRjolVSanE/jz76qFU5UEVRqlQpewPBj7POOst99dVX9m8gKjXSK7H2WiISOmr3JCIiIiISoy666CJXvnz5k+Y8cNHOBgHDrpNCSwf6RKfEF198YRsEzKGgfzRBiv379/v/P+0M6GlNT2mPN8gzofXr19tji4iIyOnlyJHDNvpHjBjhjhw5csr/Zzg2vASAwHNxwsqAtOK+A++XqgKqGpLCrAuSGp566imrIiDQkrDVI5WbVEGcTsOGDS3xgrkcBCkIWnhYT3AfVHmSqBH4Rgup5CRcp/Ax65mkkAxCxShrokB8XKJEiSTvmwoSgijJ3beI/I+CFCIiIiIiMYxNAN4YdMnwRvpLX3vttae0JAjEhgeBjJRsELC5MGHCBGtVwGBsNggCe0ffeuut1mqBigtaHhAw4fkkRJsnLtRp3SAiIiIpQ4CC83WlSpXchx9+aOd6zsm0U/JaC3Fevu6669wLL7xg/485E7RqDAZmU7EO4PxOiyXO9wkrFxKuG2gJSfUE7Z54ntOmTTvpc5hVQaCDQAqJD7RoSqqaoVGjRjbomu+LeRQe1h6sSVq0aOGmTp1q90ebqgEDBtgA8OQwx+LNN990P/zwg+vVq5d9HbMkksNAbOZQ0Mrq+++/t8HaPH9abCb8efH9Uv3CwHMqV9u0aeP//8wXSfh6iIiCFCIiIiIiMe322293vXv3tuAEg6tpweANp0xKnTp1rE8yQ69Ph2GPXGBfc8017r777rOqity5c5/UBoKLbeZcsIHCIMrnn3/+lPthmHfBggWtr7SIiIikDO0b16xZ46pWreoef/xxV7JkSauWXLBggQ1m9rDpTuY+FZYMfWZAdjD06NHDValSxdWrV8/VrVvXggYMqU5KgwYN3GOPPWab/gyWprKCIEMgBkvXrl3bvicqNd5///0k749ABEkQrB9YRwRiQDZBCl4Xhlnz3FatWnXK5yVEKyqCKMznePvtt+3xE1ZEJMT6p0uXLvZYtJyiumPGjBkWlAlEoIg31mQM/eZzaLPpIcBx6NChZB9LJCM6wxeKKTwiIiIiIhLVyPTjwnnOnDlheTwyPLnAb9asWVgeT0RERCQhZm6QXEFAQ0SihwZni4iIiIhkQA888ID1sj58+LDLli1bSB+LVg533HHHSW0aREREREREoEoKERERERERERERiXuqpBCJTqqkEBERERERERERkbinrvci0enMSD8BERERERERERERERHJmBSkEBERERERERERERGRiFCQQkREREREREREREREIkJBChERERERERERERERiQgFKUREREREREREREREJCIUpBARERERERERERERkYhQkEJERERERERERERERCJCQQoREREREREREREREXGR8P8A9qmW3ZMjvD8AAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 1600x500 with 3 Axes>"
       ]
@@ -1812,7 +1812,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1500x500 with 3 Axes>"
       ]
@@ -1822,7 +1822,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1500x500 with 3 Axes>"
       ]
@@ -2069,10 +2069,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: 2026-06-08 17:38:50 CEST\n",
+      "Last updated: 2026-06-11 00:03:28 CEST\n",
       "\n",
       "Python implementation: CPython\n",
-      "Python version       : 3.13.11\n",
+      "Python version       : 3.13.9\n",
       "IPython version      : 9.13.0\n",
       "\n",
       "pycircstat2: 0.1.16\n",
@@ -2093,7 +2093,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pycircstat2 (3.13.11)",
+   "display_name": "pycircstat2 (3.13.9)",
    "language": "python",
    "name": "python3"
   },
@@ -2107,7 +2107,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.11"
+   "version": "3.13.9"
   },
   "orig_nbformat": 4
  },
diff --git a/examples/T4-regression.ipynb b/examples/T4-regression.ipynb
index 786a0b7..e99e782 100644
--- a/examples/T4-regression.ipynb
+++ b/examples/T4-regression.ipynb
@@ -7,7 +7,7 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "import pandas as pd\n",
+    "import polars as pl\n",
     "import matplotlib.pyplot as plt\n",
     "from pycircstat2 import load_data\n"
    ]
@@ -27,10 +27,12 @@
    "source": [
     "from pycircstat2.regression import LCRegression\n",
     "\n",
-    "df = load_data(\"lung_deaths\", source=\"pewsey\")\n",
-    "df[\"θ\"] = (np.pi / 6) * df[\"month\"].to_numpy()\n",
-    "df = df.rename(columns={\"deaths\": \"y\"})\n",
-    "df = df[~((df[\"month\"] == 2) & df[\"year\"].isin([1976, 1979]))]"
+    "df = (\n",
+    "    load_data(\"lung_deaths\", source=\"pewsey\")\n",
+    "        .mutate(θ=(np.pi / 6) * pl.col(\"month\"))\n",
+    "        .rename({\"deaths\": \"y\"})\n",
+    "        .filter(~((pl.col(\"month\") == 2) & pl.col(\"year\").is_in([1976, 1979])))\n",
+    ")"
    ]
   },
   {
@@ -123,9 +125,9 @@
       "\n",
       "Linear-Circular Regression\n",
       "User formula:     y ~ harmonic(θ, k=2)\n",
-      "Expanded formula: y ~ cos(θ) + sin(θ) + cos(2*θ) + sin(2*θ)\n",
+      "Expanded formula: y ~ cos(θ) + sin(θ) + cos(2 * θ) + sin(2 * θ)\n",
       "\n",
-      "Formula: y ~ cos(θ) + sin(θ) + cos(2*θ) + sin(2*θ)\n",
+      "Formula: y ~ cos(θ) + sin(θ) + cos(2 * θ) + sin(2 * θ)\n",
       "\n",
       "Residuals:\n",
       "     Min      1Q Median     3Q     Max\n",
@@ -173,6 +175,129 @@
     "lc_ext.summary()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Linear-Circular Regression\n",
+      "\n",
+      "\n",
+      "Family: gaussian\n",
+      "Link function: identity\n",
+      "\n",
+      "Formula: y ~ s(θ, bs='cc')\n",
+      "\n",
+      "Parametric coefficients:\n",
+      "             Estimate  Std. Error  t value  Pr(>|t|)\n",
+      "(Intercept)   2101.04       19.66    106.9    <2e-16  ***\n",
+      "---\n",
+      "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
+      "\n",
+      "Approximate significance of smooth terms:\n",
+      "        edf  Ref.df      F  p-value\n",
+      "s(θ)  6.258   8.000  90.11   <2e-16  ***\n",
+      "---\n",
+      "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
+      "\n",
+      "R-sq.(adj) = 0.913  Deviance explained = 92.1%\n",
+      "-REML = 462.42  Scale est. = 27060  n = 70\n"
+     ]
+    }
+   ],
+   "source": [
+    "lc_gam = LCRegression(\"y ~ s(θ, bs='cc')\", df)\n",
+    "lc_gam.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(2, 2, figsize=(12, 8))\n",
+    "lc.plot(axes=axes[0])          # harmonic: fit overlay + residuals\n",
+    "lc_gam.plot(axes=axes[1])      # GAM:      fit overlay + residuals\n",
+    "axes[0, 0].set_title(\"Harmonic LC — fit overlay\")\n",
+    "axes[1, 0].set_title(\"GAM LC — fit overlay\")\n",
+    "\n",
+    "months = np.arange(1, 13)\n",
+    "for a in (axes[0, 0], axes[1, 0]):\n",
+    "    a.set_xticks((np.pi / 6) * months)\n",
+    "    a.set_xticklabels(months)\n",
+    "    a.set_xlabel(\"Month\")\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x550 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import calendar\n",
+    "\n",
+    "xg   = np.linspace(0, 2 * np.pi, 200)\n",
+    "grid = pl.DataFrame({\"θ\": xg})\n",
+    "\n",
+    "# Fit + 95% CI straight from each model's own backend — no standalone gam():\n",
+    "# the harmonic fit lives on lc.lm_fit, the smooth fit on lc_gam.gam_fit.\n",
+    "yhat_df = lc.lm_fit.predict(grid)\n",
+    "lc_fit  = yhat_df.to_numpy().ravel()\n",
+    "lc_ci   = lc.lm_fit.compute_ci_yhat(yhat=yhat_df, Xnew=grid, alpha=0.05).to_numpy()\n",
+    "\n",
+    "pr      = lc_gam.gam_fit.predict(grid, type=\"response\", se_fit=True)\n",
+    "gf, gse = pr[\"fit\"].to_numpy(), pr[\"se.fit\"].to_numpy()\n",
+    "\n",
+    "th, y  = df[\"θ\"].to_numpy(), df[\"y\"].to_numpy()\n",
+    "months = np.arange(1, 13)\n",
+    "rmax   = max(y.max(), lc_ci[:, 1].max(), (gf + 1.96 * gse).max()) * 1.05\n",
+    "\n",
+    "# (For a quick one-model dial, lc_gam.plot(polar=True) is built in.)\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 5.5), subplot_kw={\"projection\": \"polar\"})\n",
+    "for a, fit, lo, hi, c, lab in [\n",
+    "    (ax[0], lc_fit, lc_ci[:, 0], lc_ci[:, 1], \"C1\", \"cos(θ)+sin(θ)\"),\n",
+    "    (ax[1], gf, gf - 1.96 * gse, gf + 1.96 * gse, \"C3\", \"s(θ, bs='cc')\")]:\n",
+    "    a.scatter(th, y, s=16, alpha=0.4, color=\"C0\", edgecolors=\"none\")\n",
+    "    a.plot(xg, fit, color=c, lw=2)\n",
+    "    a.fill_between(xg, lo, hi, color=c, alpha=0.2)\n",
+    "    a.set_theta_zero_location(\"N\")     # θ=0 (Dec/Jan boundary) at top\n",
+    "    a.set_theta_direction(-1)          # clockwise, like a calendar\n",
+    "    a.set_xticks((np.pi / 6) * months)\n",
+    "    a.set_xticklabels([calendar.month_abbr[m] for m in months])\n",
+    "    a.set_ylim(0, rmax)                # shared radial scale -> comparable panels\n",
+    "    a.set_title(lab, va=\"bottom\")\n",
+    "fig.tight_layout()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -184,7 +309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -193,20 +318,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
     "df = load_data(\"B20\", source=\"fisher\")\n",
-    "X = df[\"x\"].values[:]\n",
-    "θ = np.deg2rad(df[\"θ\"].values[:])\n",
+    "X = df[\"x\"].to_numpy()\n",
+    "θ = np.deg2rad(df[\"θ\"].to_numpy())\n",
     "\n",
-    "data_cl = pd.DataFrame({\"X\": X, \"θ\": θ})"
+    "data_cl = pl.DataFrame({\"X\": X, \"θ\": θ})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -245,7 +370,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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       "text/plain": [
        "<Figure size 1100x500 with 2 Axes>"
       ]
@@ -255,16 +380,76 @@
     }
    ],
    "source": [
-    "# Fit the mean direction only model\n",
+    "# Fit the mean direction only model in the Fisher-Lee style (μ = β₀ + 2·atan(βX))\n",
     "# This implmentation is ported from R package circular's lm.circular.cl and we arrive at the same results\n",
-    "cl_reg_mean = CLRegression(formula=\"θ ~ X\", model_type=\"mean\", data=data_cl, tol=1e-10)\n",
+    "# The API is standardized to the mgcv's gam general family style\n",
+    "cl_reg_mean = CLRegression(\n",
+    "    formula=\"θ ~ X\", # which can also be written as `formula=[\"θ ~ X\", \" ~ 1\"]``, \n",
+    "    backend=\"fisher-lee\", \n",
+    "    data=data_cl, tol=1e-10)\n",
     "cl_reg_mean.plot()\n",
     "cl_reg_mean.summary()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Circular-Linear Regression — distributional gam backend\n",
+      "Family: CircularLL(vonmises) (links: 'tanhalf', 'log')\n",
+      "  LP[mu]: θ ~ X\n",
+      "  LP[kappa]: ~ 1\n",
+      "\n",
+      "\n",
+      "Family: CircularLL(vonmises)\n",
+      "Link function: tanhalf log\n",
+      "\n",
+      "Formula: θ ~ X\n",
+      " ~ 1\n",
+      "\n",
+      "Parametric coefficients:\n",
+      "                Estimate  Std. Error  z value  Pr(>|z|)\n",
+      "(Intercept)     1.625870    0.271014    5.999  1.98e-09  ***\n",
+      "X              -0.010164    0.003558   -2.857   0.00428  **\n",
+      "(Intercept).1   1.122029    0.220746    5.083  3.72e-07  ***\n",
+      "---\n",
+      "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
+      "\n",
+      "Deviance explained = 2.77%\n",
+      "-REML = 36.862  Scale est. = 1  n = 31\n",
+      "\n",
+      "Fitted parameters (per observation):\n",
+      "  mu     min  0.7365   median  1.7172   max  2.0332\n",
+      "  kappa  min  3.0711   median  3.0711   max  3.0711\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# the default backend is actually GAM, which uses a different parameterization of the mean (μ  = 2·atan(β₀ + βX))\n",
+    "cl_gam_mean = CLRegression(formula=[\"θ ~ X\", \" ~ 1\"], backend=\"gam\", data=data_cl, tol=1e-10)\n",
+    "cl_gam_mean.plot()\n",
+    "cl_gam_mean.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -337,7 +522,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1100x500 with 2 Axes>"
       ]
@@ -349,12 +534,70 @@
    "source": [
     "# Fit the concentration only model\n",
     "cl_reg_kappa = CLRegression(\n",
-    "    formula=\"θ ~ X\", model_type=\"kappa\", data=data_cl, tol=1e-10\n",
+    "    formula=[\"θ ~ 1\", \"~ X\"], backend=\"fisher-lee\", data=data_cl, tol=1e-10\n",
     ")\n",
     "cl_reg_kappa.plot()\n",
     "cl_reg_kappa.summary()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Circular-Linear Regression — distributional gam backend\n",
+      "Family: CircularLL(vonmises) (links: 'tanhalf', 'log')\n",
+      "  LP[mu]: θ ~ 1\n",
+      "  LP[kappa]: ~ X\n",
+      "\n",
+      "\n",
+      "Family: CircularLL(vonmises)\n",
+      "Link function: tanhalf log\n",
+      "\n",
+      "Formula: θ ~ 1\n",
+      "~ X\n",
+      "\n",
+      "Parametric coefficients:\n",
+      "                Estimate  Std. Error  z value  Pr(>|z|)\n",
+      "(Intercept)     0.783972    0.080158   9.7803   < 2e-16  ***\n",
+      "(Intercept).1  -0.254752    0.493047  -0.5167  0.605373\n",
+      "X.1             0.033264    0.008747   3.8030  0.000143  ***\n",
+      "---\n",
+      "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
+      "\n",
+      "Deviance explained = 6.03%\n",
+      "-REML = 33.011  Scale est. = 1  n = 31\n",
+      "\n",
+      "Fitted parameters (per observation):\n",
+      "  mu     min  1.3298   median  1.3298   max  1.3298\n",
+      "  kappa  min  0.8013   median  3.5801   max  44.8543\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Fit the concentration only model with gam backend\n",
+    "cl_gam_kappa = CLRegression(\n",
+    "    formula=[\"θ ~ 1\", \"~ X\"], backend=\"gam\", data=data_cl, tol=1e-10\n",
+    ")\n",
+    "cl_gam_kappa.plot()\n",
+    "cl_gam_kappa.summary()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -364,7 +607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -443,13 +686,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/ziweih/Works/pycircstat2/pycircstat2/regression.py:165: RuntimeWarning: CLRegression did not converge in 100 iterations (last diff=3.04e-05, tol=1.00e-10).\n",
+      "/Users/ziweih/Works/pycircstat2/pycircstat2/regression.py:637: RuntimeWarning: CLRegression did not converge in 100 iterations (last diff=3.04e-05, tol=1.00e-10).\n",
       "  self.result = self._fit()\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1100x500 with 2 Axes>"
       ]
@@ -463,8 +706,8 @@
     "gamma0, alpha0 = cl_reg_kappa.result[\"gamma\"], cl_reg_kappa.result[\"alpha\"]\n",
     "\n",
     "cl_reg_mixed = CLRegression(\n",
-    "    formula=\"θ ~ X\",\n",
-    "    model_type=\"mixed\",\n",
+    "    formula=[\"θ ~ X\", \"~ X\"],\n",
+    "    backend=\"fisher-lee\",\n",
     "    data=data_cl,\n",
     "    tol=1e-10,\n",
     "    beta0=beta0,\n",
@@ -477,23 +720,141 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Circular-Linear Regression — distributional gam backend\n",
+      "Family: CircularLL(vonmises) (links: 'tanhalf', 'log')\n",
+      "  LP[mu]: θ ~ X\n",
+      "  LP[kappa]: ~ X\n",
+      "\n",
+      "\n",
+      "Family: CircularLL(vonmises)\n",
+      "Link function: tanhalf log\n",
+      "\n",
+      "Formula: θ ~ X\n",
+      "~ X\n",
+      "\n",
+      "Parametric coefficients:\n",
+      "                Estimate  Std. Error  z value  Pr(>|z|)\n",
+      "(Intercept)     1.290961    0.139975   9.2228   < 2e-16  ***\n",
+      "X              -0.005997    0.001320  -4.5435  5.53e-06  ***\n",
+      "(Intercept).1  -0.520744    0.562024  -0.9266     0.354\n",
+      "X.1             0.047977    0.010145   4.7292  2.25e-06  ***\n",
+      "---\n",
+      "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
+      "\n",
+      "Deviance explained = 9.04%\n",
+      "-REML = 31.591  Scale est. = 1  n = 31\n",
+      "\n",
+      "Fitted parameters (per observation):\n",
+      "  mu     min  1.0200   median  1.5858   max  1.8189\n",
+      "  kappa  min  0.6233   median  5.3989   max  206.9394\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cl_gam_mixed = CLRegression(\n",
+    "    formula=[\"θ ~ X\", \"~ X\"],\n",
+    "    backend=\"gam\",\n",
+    "    data=data_cl,\n",
+    ")\n",
+    "cl_gam_mixed.plot()\n",
+    "cl_gam_mixed.summary()"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Circular-Linear Regression — distributional gam backend\n",
+      "Family: CircularLL(vonmises) (links: 'tanhalf', 'log')\n",
+      "  LP[mu]: θ ~ s(X)\n",
+      "  LP[kappa]: ~ X\n",
+      "\n",
+      "\n",
+      "Family: CircularLL(vonmises)\n",
+      "Link function: tanhalf log\n",
+      "\n",
+      "Formula: θ ~ s(X)\n",
+      "~ X\n",
+      "\n",
+      "Parametric coefficients:\n",
+      "               Estimate  Std. Error  z value  Pr(>|z|)\n",
+      "(Intercept)     1.00525     0.08014  12.5432   < 2e-16  ***\n",
+      "(Intercept).1  -0.52074     0.56203  -0.9265     0.354\n",
+      "X.1             0.04798     0.01014   4.7292  2.25e-06  ***\n",
+      "---\n",
+      "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
+      "\n",
+      "Approximate significance of smooth terms:\n",
+      "        edf  Ref.df  Chi.sq   p-value\n",
+      "s(X)  1.000   1.000   20.64  6.40e-06  ***\n",
+      "---\n",
+      "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
+      "\n",
+      "Deviance explained = 9.04%\n",
+      "-REML = 28.222  Scale est. = 1  n = 31\n",
+      "\n",
+      "Fitted parameters (per observation):\n",
+      "  mu     min  1.0200   median  1.5858   max  1.8189\n",
+      "  kappa  min  0.6233   median  5.3989   max  206.9389\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cl_gam_mixed_smooth = CLRegression(\n",
+    "    formula=[\"θ ~ s(X)\", \"~ X\"],\n",
+    "    backend=\"gam\",\n",
+    "    data=data_cl,\n",
+    ")\n",
+    "cl_gam_mixed_smooth.plot()\n",
+    "cl_gam_mixed_smooth.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x124274b90>"
+       "<matplotlib.legend.Legend at 0x11fe68410>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -644,6 +1005,56 @@
     "The results from our implementation of the Mixed Circular-Linear model are quite close to those reported in Fisher (1993), though not exactly the same. We have reason to believe that the numbers reported in the book may not be entirely correct, as the results for the mean direction model were significantly different from both our implementation and the results obtained using R/circular."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X_   = np.linspace(0, X.max(), 200)\n",
+    "grid = pl.DataFrame({\"X\": X_})\n",
+    "\n",
+    "fits = [\n",
+    "    (\"Fisher–Lee (offset outside)\",   cl_reg_mixed,        \"C0\"),\n",
+    "    (\"gam linear (intercept inside)\", cl_gam_mixed,        \"C1\"),\n",
+    "    (\"gam smooth μ\",                  cl_gam_mixed_smooth, \"C2\"),\n",
+    "]\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
+    "# left: μ̂(X) over the data (replicated at +2π so the wrap is visible)\n",
+    "for yoff in (0, 2 * np.pi):\n",
+    "    ax[0].scatter(X, np.mod(θ, 2*np.pi) + yoff, color=\"black\", alpha=0.4, s=18,\n",
+    "                label=\"data\" if yoff == 0 else None)\n",
+    "for label, m, c in fits:\n",
+    "    mu = m.predict(grid)                       # predict now takes an array OR a DataFrame\n",
+    "    mp = mu.copy(); mp[np.where(np.abs(np.diff(mu)) > np.pi)[0]] = np.nan  # break wraps\n",
+    "    ax[0].plot(X_, mp, color=c, lw=2, label=label)\n",
+    "    ax[0].plot(X_, mp + 2*np.pi, color=c, lw=2)\n",
+    "ax[0].set_ylim(0, 4*np.pi)\n",
+    "ax[0].set_yticks([0, np.pi, 2*np.pi, 3*np.pi, 4*np.pi])\n",
+    "ax[0].set_yticklabels([\"0\", \"π\", \"2π\", \"3π\", \"4π\"])\n",
+    "ax[0].set_xlabel(\"X\"); ax[0].set_ylabel(\"θ\"); ax[0].set_title(\"Fitted mean direction μ̂(X)\")\n",
+    "ax[0].legend(frameon=False, fontsize=8)\n",
+    "# right: κ̂(X), log scale (κ spans ~0.7 to ~200)\n",
+    "for label, m, c in fits:\n",
+    "    ax[1].plot(X_, m.predict_kappa(grid), color=c, lw=2, label=label)\n",
+    "ax[1].set_yscale(\"log\")\n",
+    "ax[1].set_xlabel(\"X\"); ax[1].set_ylabel(\"κ̂(X)\"); ax[1].set_title(\"Fitted concentration κ̂(X)\")\n",
+    "ax[1].legend(frameon=False, fontsize=8)\n",
+    "fig.tight_layout()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -653,7 +1064,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -683,19 +1094,25 @@
     }
    ],
    "source": [
-    "from pycircstat2.utils import load_data\n",
     "from pycircstat2.regression import CCRegression\n",
     "\n",
     "df = load_data(\"milwaukee\", source=\"jammalamadaka\", print_meta=True)\n",
-    "ctheta = np.deg2rad(df[\"theta\"].values[:])\n",
-    "cpsi = np.deg2rad(df[\"psi\"].values[:])\n",
     "\n",
-    "df_rad = pd.DataFrame({\"theta\": ctheta, \"psi\": cpsi})"
+    "# The csv keeps Jammalamadaka & SenGupta's labels: `theta` = wind direction at\n",
+    "# 6am, `psi` = at noon. Pewsey §8.6 swaps the letters and regresses noon on\n",
+    "# 6am (morning wind predicts noon wind), so we rename to plain English to keep\n",
+    "# the roles unambiguous.\n",
+    "df_wind = pl.DataFrame(\n",
+    "    {\n",
+    "        \"six_am\": np.deg2rad(df[\"theta\"].to_numpy()),\n",
+    "        \"noon\":   np.deg2rad(df[\"psi\"].to_numpy()),\n",
+    "    }\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -705,29 +1122,29 @@
       "\n",
       "Circular-Circular Regression\n",
       "\n",
-      "Circular Correlation Coefficient (rho): 0.51234\n",
-      "Mean Residual Cosine (A_k):             0.45009\n",
-      "Residual Concentration (kappa):         1.00676\n",
+      "Circular Correlation Coefficient (rho): 0.50229\n",
+      "Mean Residual Cosine (A_k):             0.50637\n",
+      "Residual Concentration (kappa):         1.17031\n",
       "\n",
       "Coefficients (Cosine Model):\n",
       "\n",
       "             Estimate     Std. Error   t value    Pr(>|t|)    \n",
-      "(Intercept)  0.03765      0.14388      0.26       0.79654     \n",
-      "cos(x1,k=1)  0.47746      0.25193      1.90       0.07424     .\n",
-      "sin(x1,k=1)  0.26616      0.17887      1.49       0.15406     \n",
+      "(Intercept)  0.01441      0.13024      0.11       0.91311     \n",
+      "cos(x1,k=1)  0.33465      0.17773      1.88       0.07597     .\n",
+      "sin(x1,k=1)  -0.07479     0.19627      -0.38      0.70763     \n",
       "\n",
       "Coefficients (Sine Model):\n",
       "\n",
       "             Estimate     Std. Error   t value    Pr(>|t|)    \n",
-      "(Intercept)  -0.23775     0.14393      -1.65      0.11590     \n",
-      "cos(x1,k=1)  -0.28901     0.25201      -1.15      0.26647     \n",
-      "sin(x1,k=1)  0.23117      0.17893      1.29       0.21270     \n",
+      "(Intercept)  0.11451      0.17478      0.66       0.52064     \n",
+      "cos(x1,k=1)  0.54941      0.23850      2.30       0.03338     *\n",
+      "sin(x1,k=1)  0.48210      0.26338      1.83       0.08380     .\n",
       "\n",
       "Higher-Order Terms Test:\n",
       "\n",
       "             Pr(>χ²)     \n",
-      "cosine model 0.15187     \n",
-      "sine model   0.20656     \n",
+      "cosine model 0.09971     .\n",
+      "sine model   0.94748     \n",
       "\n",
       "Higher-order terms are not significant at the 0.05 level.\n",
       "\n",
@@ -738,7 +1155,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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pRdKePJ5Lzw4ZkyrXFzJGVR7vZRlH2wblMoZVAohTTjnF/UKEjL6VQMdbkNOWfK4sX5VrBVnWunnzZuWaQa4B4uLiDnnu5JpErlvk55HrHwlG2urNOWpLKlNkiY0EGq5rJfndyLVTd36vUh1z8sknY8GCBcoxSMCwfv165RjkhR1ZuiPnTX5+qQSSfXLM7733Xpe9VFxc12cyulaasEoVjVTPSqPZAQMGKJVGQr63/O4efPBB5XPkdyO93OR7SmN5CXXkdyznSF70kspgqcjtzvJlon7Xr7NoiKhfuMayrVy50uvHvY3IlVFqV199tTJqT6fTdWtcrozEHTlypNNoNDozMjKcV155pbOystLrfe+44w7law4dOrTTryfj02QcnozFlZG1Q4YMcS5YsMC5atUqj3F7Mi7OG2+j+F544QVlHKCMD5RjlZ/Z22i+9qPrHn744UP+/ERERMHw2C5jSOUxUzZ5PBc7d+50Xnjhhc7MzEzlcVpG1p988snKWF2XBx980Dl16lRlTL3ZbFYeJx966CGPka7eHjMbGhqUMfIpKSnKY/Ipp5yijOhtPyJXfP7558poepPJ5BwxYoTz9ddf9/o1P/zwQ+f48eOVx38ZrfrII484X3zxReV+ct3S2YhcGXU7c+ZM5VjksV7Owc033+ysrq7u1nm1WCzKzy7fR46tve6co65+X998843zsssucyYlJTljY2OdF1xwgbO8vNzjvnLt0tkYXhk3u3DhQuX6Sc5hamqqMur4scce8zgG+Zq//e1vnfHx8cp1lLy/Zs2aQ47IdZFzPXHiROUcyrHKOV6yZIn748XFxcoxxsXFKZ/f9ncg/9bOPvts5RzJ70/O10cffeTx9V0jct9+++0uzxtRf9DJf/o/eiEiCk7Su+T6669XXklpuy6ZiIiIqLukKkd6iEhFhauRLREFB9YnERG1kExY+prMmjWLAQgRERERUQhiTxAiCnuyvlbW3EoXellnK2PwiIiIiIgo9DAEIaKwJ53ypXGqNEO7/fbbu9WsjIiIiIiItCdoeoL86U9/UqZNXHvttV5HbBERERERERERab4niDQMklnm48ePD/ShEBEREREREVGICngIInPAL7jgAmUWeFJSknu/zOPW6XRet3vvvTegx0xERERERERE2hPwniB//OMfcdJJJ2Hu3Ll48MEH3fvfe+89zJw5EyeccAJuuukmZd+ZZ56JsWPHum+3Z7Valc3F4XCgoqICKSkpSnhCRERE3SOrZWtqajBgwADo9QF/zSRkyLVJYWEh4uLieG1CREQUgOuSgIYgb775Jn7++WdlOUx7ycnJiIiIQGxsLDIzM5V9JpMJ0dHRyj5vFi1ahPvuu8/vx01ERBQu9u3bh5ycnEAfRsiQACQ3NzfQh0FERBS21yURgTx4aYK6ZMkSREVF+eRrSmPVG264wX27uroaeXl5yveKj4/3yfcgIqI2bE1A9T7A1lqFF5TWvQWsekF9PyIKOOUpIGkQQobBCMSmA1EJPvuSFotFebIuFQvkO67zyWsTIiKiwFyXBCwEWb16NUpKSjBp0iT3Prvdjm+//RZPP/20x7KW7oqMjFS29iQAYQhCRORjTXVAVRlgNgKQLUgVrADWvwREtiyLnHcfkDcWIUGWekanADHpgJ+WrHA5qX/OJ69NiIiIAnNdErAQZM6cOVi/fr3HvosvvhgjR47ErbfeCoPB0OFzeCFGRBQk6iuAmiJZoImgVlMMLL1fVpKqtw//PTD4aIQEoxmIH6C+JSIiIqLgDkGkjEWanLYVExOjNDFtv7/t52zZskWpIElPT++nIyUiIjcJPSwHgIaq4D8pdhvwxT2A1aLeHnQ0MOkiaJ5Ory59kQoQNv0mIiIi6hFNtXs/55xz8N133+GSSy4J9KEQEYUf6ftRsUsbAYj46Vng4Ab1/bgs4Jjb1QBBy0zRQMpQICaVAQgRERFRL+icMmsmhJunJCQkKA1S2ROEiKgPJPiwFAJOhzZO455lwOJb1ff1EcDpzwLpo6Dt6o8MICal374lH0N5XomIiIKFL69LAjoil4iIgpzDrvb+0Er1h6sPyFcPtt6efpW2AxDp+ZGQA0R0bPxNRERERD3DEISIiDqf/lJ9ALA3aecMKX1A7gasNertwTOBsWdDk6TfR0yaurH3BxEREZFPMAQhIiJPskqy9iBQV6a9M6P0Admovh83AJgtfUD6Pkqt3xlMavWH9AAhIiIiIp9hCEJERK2a6tXpL9IEVWv2Lgd++U9rH5Dj7gci46A5UQnq6Ft9x1HxRERERNQ3DEGIiAhwONTqj/pybZ4NOfYvH2i9fcQftdcHRCpWZIpNdHKgj4SIiIgoZGl8ViCtWbMGjz76KBzyBIaIqDcaq4Hy7doNQJQ+IPcCVot6e9BMYNw50Nzyl+R8BiBEREREfsYQRMMaGxtx0UUXYcmSJXjyySehNbNnz8Z1113n9++j0+nwwQcf+P37EGmOLHmp3ANU7QPszdCsVf8Eitep78dmAscs1FYfkMhYIGWIOgWGiIiIiPyKIYiG3XXXXbjkkkvw3//+F2+88QZ27Njht++1ceNGnHXWWRg0aJASKjzxxBN9/prvvfceHnigTfm6Bv3zn//EYYcdhtjYWCQmJmLixIlYtGiR++P33nuvcr5ki4iIQGpqKmbOnKmcP6tVgz0XKITG3hYD5TsAay00reAHYM3r6vvSQ0PpA9K32fH9KjYdSBrE/h9ERERE/YQ9Qbwp3oB+lTm2V5/25z//2f3+ypUre/S5CxYsUAINeZLeHfX19cjPz8c555yD66+/Hr6QnNz1uvempiaYTCYEWmfH8eKLLyqVLE899RRmzZqlhBrr1q3Dhg2e/37GjBmDL774QlmyVF5ejq+//hoPPvggXnvtNeX9uDgNNm4k7U59aagEaksAhw2aV1vq2Qdk2pVAxhhogk4PJGSrTVCJKGQ5nU7sKKmFpdGGoemxSDAbA31IRERhj5UgGiThRftKjAkTJnQ70OiNww8/XAldzj33XERGRnb7877//ntl2Ut0dDSSkpIwb948VFZWel0OIz+XVIZceOGFiI+Px2WXXXbIr9Gbc3Hrrbdi+PDhyteTYEcqapqbW5cCyOfK1/jXv/6FwYMHIyoqyuvX+fDDD/GrX/0Kv/vd7zB06FAl7DjvvPPw0EMPedxPKkAyMzMxYMAAjBs3DldffTW++eYbJSx55JFHun0uifre92MHYCkMjQBEfoal9wKNVertgUcC48+FJhiMav8PBiBEIa3OasPfvtyBF7/fg3dW78eji7fgh10a7b1ERBRCGIKQ36xduxZz5szB6NGjsWLFCixbtgynnHIK7HZ7p5/z2GOPKctLpOGrhBO9+RqHIpUXL7/8MjZt2qT0UpElLX/961897iNLi959911lyY4cgzcSbPzwww/Yu3dvj49h5MiROPHEE5WvT+RXstylfKfa90OLY287s+pFoKjl/83YDOCYO7TRB0T6fiRL/w/v4SoRhY4vt5SgqLrRfdvhBD5eV4SaRg33YCIir2x2B1bsLMdrK/bggzUHcNDS+v8+BR8uhwkT//73v3H55Ze7b8vSDelTIaGDy6effoqjjz7aZ99TptZMmTIF//jHP9z7pFqiK8ceeyxuvPFG9+3zzz+/x1/jUO688073+1JJctNNN+HNN9/ELbfc4rEE5tVXX0VaWlqnX+eee+7BmWeeqXwNqSyZPn065s+fj7PPPht6vb5bQcjnn3/ep5+FqMvwo64EaKoPvZNUsAL4+VX1fZ0BmHufNqoqIuOAhFygG38fiEj7ZBlMezaHE3vK6jEuRwN/s4io2/7zUwE2FdW4b/9cUInLZuYjJymaZzEIMQQJE6eeeiqmTZvmsSQkOzsb11xzjXuf3PYlqaCQHiI9IYFHX7/Gobz11ltKH4+dO3eitrYWNptNWX7T1sCBA7sMQERWVpZSnSLLWr799lssX75cmdYjy2gWL158yCBE1glLEEXkU9YaoK40NMMPUbVXHYcLp3p76mVA5jgEvehkIH5AoI+CiPpRYrQRJTUdK/DYF4QotOyvrPcIQESz3Ymvt5biN0cMDNhxUecYgoSIQy0PkSUgbRtwyvvSmFR6WfiL2dzzcY8xMTE9+hoSNEiY0Fbb/h7tSWhxwQUX4L777lN6iyQkJChVIH/5y1+6PI6ujB07Vtn+8Ic/4IorrlCqaaTnxzHHHNPl523evFnpOULUZ/L/gPT8qC8HmhtCu7pl8W1AU8urq4NnAhPOR9CT5TqxXYeqRBR6jh6WplSDyDIYl/zUGOSl8JVholBSXtvkdX9ZbQgtQ+7npUWbiiywNNgwLCMWGfG+X0LMmlyNOnjwoMeT/n379iHYjB8/HkuXLvXr15BqjaKiIvdti8WC3bt3d3p/qdaQKo877rhDqToZNmxYr3p6dEZ6l4i6urou77dlyxalWkTGDhP1msMB1JUDZduB6v2hHYDIWF9phFpVoN6WxqLH3qVOWQlmUv3BACRoSRWf9JmSxtVSmffBBx8c8nNkqtekSZOUJuHyQoL0mCLyRqbB/O6owRidFYecJDOOHZmOC2fwVWGiUJObHO21LdlABp49Jj2Tnlq6Hf/5aR8+Xl+EJ77YjqWbW5/3+gorQTRKxrNKw1B5Qi/NPaurq5XlHRKOZGRkdLh/Q0ODch+XP/3pT8rb4uJi9z6pDOlsJK30yJBGoq73Dxw4oCxViY2N7bSaZOHChco0FFeFhHztr776Slnekpqa2q2f81BfQ3qIyAWoXMQmJibi7rvvhsFg6PTrSehRUFCgVH/IxJuPP/4Y77//PnrjyiuvVC6c5RhycnKUMEZG30owI/1BXGS5jZzn9iNyZQLNzTff3KvvTWFOGpzWV6iTUSQcCAcrn1d7gYjIeOCEPwHGIH41Va6G4rMBc2Kgj4S6IIG1NOO+5JJLlB5PhyIh+0knnaQ8HkmvLQnpf//73yvLI6W6kKi9/LRYZSOi0JUcY8KxI9KxdEuJe19StBHHjuj4nIy6JkuISttV1sh5nZiX5NPggiGIRsmTfunnsWvXLuXCTZ5UP/zwwzjhhBOU5R7e+mBcfPHFXX5NCRdkFK03hYWFmDhxovu2NFSVbdasWcqTem+kWag0/rz99tsxdepUZWmL9CWRMbLddaivISGJXJSefPLJytIWGbHbVSWI9Ea5/vrrcdVVVynNYeViVqbQ9Ga88Ny5c5Uw6plnnlHCDQllJPyQi+KUlBT3/TZu3KhcIEs4I8co1SJy3BKi9GTcMIU5WfJitajhR1PXlUYhZ/sSYM3rrY1Qj3tADRh6qKHZgW3lzTAZdBieYoRBr/NfACINUKM8ew1R8JEpXbJ117PPPqssY3QtoRw1apQytUwmjDEEISIKX3NHZ2DUgHhsO1iD+CgjxmUnwBQR5NWqQWhPWZ3XS+A95XUYmtj5C909pXO2b6gQQmRphDzplAqI9o0vtUymkVx33XXKRkRhUPXRUAk0SNWHDWGn6Bfgo+sAe8urAkdeB4zrebPkrWVN+M+GWlht6kNeklmP302MR0q07x5Q3QFIYp46CUbjQvUxtDOyHEYqA08//fRO7zNz5kxlKcwTTzzh3vfSSy8pj8dtqy3bksBdtrbnNTc3N2zOKxERUXf9+8e92HDA0mH/FbPykWS0++y6hPEUEVGwsdvUio/ynWq/j7qy8AxAKnYDi29tDUBGngyMPbvHX8bucOLdzXXuAERUNjjw8XYfT9AJoQCEvJOlje2XnMptCTZk2ak3ixYtUi7aXJsEIERERKHC6XSiur4ZzXZHn7/WzGFpMBp0HforDUzp/tCK7uByGCKiYCC9PWS5i0x5keUuoVuk1z21JcAnN6ojf0XOVODom9WgoYeKa+2osXZ8YN5e7r2be5+WwDAAoXZk+eMNN9zQoRKEKBwctDQqW3aiGSmxXAJMFGq2H6zBB2sPoKKuGZEReswanoZjRqb3qcnslbOHYPmOclQ3NCvTYY7Ib20z4CsMQTRoz549gT4EIvJVxYcEH7Ix+GglVTD/uwaobekGnjoCOP5BwNC7h6xYk17JKNrnSglReh8GIDnsARIGMjMzPaazCbktZbmdjXSX3k/s/0ThxuFw4p3V+7FmX5X7z+RRQ1Mxf1xWoA+NKOw5Wy6IZBloX9RabXjth71otqtfz2pz4PNNB5ESa8L4nN43hs9KMOOsyTl+/T0xBCEi6k/NjWp1gwQfoTzStrekEuaja4HqlrHf8TnA/D8Dpt6XQUrYMTEzEj8XtfZlEDMHen/S2qsxuFEJvvlaFNSk+fUnn3zisW/JkiUeE8GICPhlf5U7ABHynOu77WUYmRnHaTlEAWK12fHJ+iKsLaiCxBYTchNx0vgsREb0rj/axgPV7gCkrV/2VfUpBOkPDEGIiPxd7dFU27LVAfZmnu/OSANYaYJasUu9HZsBnPIkEN33MsgzR8UgPcaADSVNiIzQYWp2JMZn+KA0Oy4LMCf1/etQQNTW1mLHjh3u2zJdTMa/y8j4vLw8ZSmLjIR/9dVXlY/LaNynn34at9xyizJW98svv8T//d//KePWiajV9pLaTvdzZDBRYPx3bSHWFLSGkyv3VCp9PH59eF6vvl5nlSR9rTDpDwxBiIh8SUIOCTua64GmesDWyPPbHXWlwP+uBar2qrcl+DjlKSAu0yfnT8bhzhpkVjafiUkDYny/TpX6z6pVq3DMMce4b7t6d1x00UV4+eWXUVRUhIKCAvfHZTyuBB4yav3JJ59ETk4O/vWvf3E8LlE78VHen2LERfKpB1GgqkDW7W8NQFzW7a/GaRPsiDL2vBpkbHa8Ulkiy2DampQX/C8O8S8REVFvORyArUFd4iKhh2ys9Oi5qgLg4xuBmsLWCpCTn1T7bAQrcyIQ5zklhLRn9uzZ7rXR3kgQ4u1z1qxZ4+cjI9K2qYNT8MOuCo8nR3FREZiQF9wl8kShSh7qHF4e7hzK/t414482ReCiGYPw4dpCFFsaERtpwOwR6Rg9IPjHvzMEISLqDgk3pKrDZlXfSvBht3KKS18VbwAW36L2AhFxA9QKkPggbp4n/UniswN9FEREQSs5xoTLZ+Xjyy0lKLFYkZNkxrEj05UnTUTU/6TSY1h6LLYd9FyqJuNn+/L/5eDUGFw7dxgamuzKdBi9PviXwgj+JSIiatu/wyFhh1UNPSTkUN5vUkfYkm/t+AL46iH1/IqUocCJjwGxacF7piMigcS8Xo3qJSIKJzLh4YJpAwN9GETUQiauvPlTAXaX1Su3B6VE42wfTWExm3rXXDVQGIJokJTuXn755XjnnXdQWVmJhIQELFiwAE888USgD40oeJetOGwdNwk6JPSQ8EOeiDs91zSSv34fdmDl88Ca11v3ZU8Bjn8IiIwN3tOujwASBwJ6bT3QExEREcVHGXHZzCGorFNffEqKMYXtSWEIokGLFy9W1il//fXXyM/Ph16vh9nc2uxv0KBBuO6665SNSFNkTaKySRjR/n1H62331va2XX0rT7DlfQk+lLct+yk41FcAS+8DDqxq3TfyZODomwCD0a/fem9VszIdxmTQYVJWJFKiexBmSOVHYi4QEb4XDERERKR9SWEcfrgwBNGgnTt3IisrCzNmzAj0oVC4a19h4S186LA5Ow84KLTtXwV8eT9QX67e1hmAI68Bxpzl9+Ulywoa8PE2tfxTfFfQiAUT4pCfZOz+KFzpBUJEREREmsYQRGNk2csrr7zinsE8cOBApfJjwoQJynIY6Vq/d+9eZXyfbKKrzvdEh2RrUhuBynIR9yZLSJpZYUHdI31VfnwGWP926z4ZgTvnXiB7kt/PYqPNiS92NXjsa7Y7sXhHPf5weMKhv0B0sroRERERkeYxBNGYJ598EkOGDMHzzz+PlStXwmAw4JxzznF//L333sNhhx2Gyy67DJdeemlAj5U0SIKNpjqguaF19CuXklBfHFgNfPeYOgbXJedwYM49gLl/5siX1dthtXUMg4tqbIf+ZKNZrQIhIiIiopDAEKS952YBtSX9/5uITQcu/+aQd5MmqHFxcUr4kZmZ2eHjycnJysfkPt4+TuRBqoSaagFrrfpWXrEn8oW6UmDF0+oEGBeDCZh2JTDubECn77fznGLWK31AmuyeQUhmbEQ3GqFyEgwRERFRKGEI0p4EIDWFAfllEPUbCT0aqwBrDUe/km/JpJ0N7wCr/qVWFLlkjAFm3w4kDer3M2426nHsYLOy/MUlQg/MGxrd+SdJj5KEHL83ayUiIiKi/sUQxFtFRjh9Xwof0qi0oVKdziF9PYh8SZZN7fwSWPWC59KXqAS1+mPkSf1a/dHerEFm5CZEeEyHSY/pYjpMTFpwj+slIiIiol5hCNJeN5akBDuTyQS73R7ow6BgemW+vkwNQCQIIfL1kqo93wIr/wVU7GrzAR0w6lRg2uVqEBIEZBJMt6bByBQYBtNEREREIYkhSAiSaTHffvstzj33XERGRiI1NTXQh0SBIKNqJfyoK2NzU/LDvy8bsPtbYO3rQOlWz49lHQYccRWQMVp7Z176gCTkBvooiIiIiMhPGIKEoPvvvx+XX365MkXGarVyRG44aqgCag+q016IfN1PZstH6rjb2mLPj6WPBg6/VJ3+Ij01tCghGzDwoZGIiIgoVOmcTqllDk0Wi0WZplJdXY34+PhAHw6R/0noYTmgPlEl8qXyncDm/wFbPwaaWxuMKlKHq+FH3nTthh8iOgWI5zhcFz6G+gfPKxERUWAfP/lyF1EoVX/UFLHvB/mOTA+SEbdS+VG6pePHJfQY/2sge4q2ww8REQXEcaw4ERERUahjCEKkdVLMJeGHTH0hTbLanDhYZ0NSlAFxkR0nqFQ3OlBttSMzNkKZbOJXNiuw7wdgx1Jgz3cdJwlFRALDTwDG/RpIGoiQ4BqHq8Egx2Z3oKi6EbGREUiKMQX6cIiIiIiCHkMQIq0vf5FxpM0NgT4S6qWVBxrx8fZ6JQjR64AjcqJw8vBo6HQ6pZ/PB1vqsKrQCocTMBt1OGV4DCZmRfo++DiwGti5VG122n65i0gdoY65HXYcEBliywtjMwFjFLRm+8EavL16P2oabcrtMQPi8evDc2E0BG4UMREREVGwYwhCpFUSfEgAwuanmlVaZ8f7W+qUYh4hQcfyfY3IiY9Qgo6fDliVzaWh2Yl3NtUiPykCCVGGvn1zGZm8dzmwdxmwbyVg8xKkSdghocfIk9W+H6FIxuHGpEBrrDY73vipAI3NDve+jYUWfL21FMeNzgjosREREREFM4YgRFrUaAGq93P0rcZtKm1yByBtbSxtUkIQedueBCWbSpsxPbeHIYiEZSUbgf0r1e3gJllL1fF+plhg8ExgyBy110coT0rRG9RlMBq0s6TOIwBx2VhYzRCEiIiIqAshfHVLFMINUGUCTOgOdgobURG6LvdHdtL/o7PP8yD/Pip3t4Qeq4DCNd6rPZQvmAgMPFINP3KnAoYw6S0hjVANRmhRlNH7kpfIiD5WCBERERGFOIYgRFoizU8thYE+CvKR8RkmfL6zHvXNrYGW9AWZmq32/JiWE6VUg7TNu6Rx6ug0LyGF3QaUbweKfgEOrgeK1gENXTTLTRoMDDoSGHg0kD5KrYoIJ1HxgDkJWjU4NQbpcZEoqWldLiWm5ScH7JiIiIiItIAhCJFWMAAJOWajHr+fFI9Pd9Rjd2Uz0mIMmJsfjbwEtTphaLIR542NxVd7GlDR4MDgxAicOCwakVIJYq1Vl7dI6FG8HijZBNgaO/9m0Snq8pacKUD24UBsGsKWPgKIGwAtk8a5lxw1GJ+sL8LW4hrERUXgqKGpmJSn3WCHiIiIqD8wBCHSAmliyQqQkNRgc6K2yQGbA6hrcqKuybPPw7iMSIxL1QEVu4DSLcDKLUDpZqB8p/eeHm0bfmaMA3IOV7fkfE2OgPWL+KyQ6HWSYDbivKl5gT4MIiIiIk3R/lUguc2ePRsTJkzAE088wbMSShqrgeoDgT4K8oMaqwOv/lKjjMcVFqsDH2yqRkbjLuQ17VBDD9kk8HCoY1A7FZsBZI5Xt6zx6nKXcFvi0h1RCepGRERERGGJIUiY+vrrr3HMMcegsrISiYmJgT4c6owseZApMBSSNhZZkFK7HVlNu5Ft3Ynsxh3K+8adzV1/ok4PJA9Rww4l+BinhiDUNWmCGq/tZTBERERE1DcMQYiCVXMDUFXAKTChQDqb1pUC5Ttat4qdmFZZgCPQccypJx2QNBBIG9m6pQwFjOZ+OvgQEpfF6hgiIiKiMMcQRKPq6upw5ZVX4r333kNcXBxuuukmj4+/9tprePLJJ7F161bExMTg2GOPVZbJpKenY8+ePUoViEhKUpvoXXTRRXj55ZexePFiPPjgg9iwYQMMBgOmT5+ufJ0hQ4YE5OcMW/bmlgDkUE+QKejYrOpo2raBhyxnsVo63NVbh45SYzZMmSORkDNaDTxSh6n9PahvZBKMTIQhIiIiorDGEMQH9lfWY0tRDWIiIzAhNxFmk//X4d9888345ptv8N///lcJNm6//Xb8/PPPSk8Q0dzcjAceeAAjRoxASUkJbrjhBixYsACffPIJcnNz8e677+Kss85SQpL4+HiYzWZ3uCL3HT9+PGpra3H33XfjjDPOwNq1a6HX6/3+cxEAhwOo3KsGIaS56o5uh1d6I5A8GFUxg7Hamotd+oEoixmG6UNSMXsQqzx8ymBSq0CIiIiIKOwxBOmjr7aU4PNNB923v9xyEJfNHIK0uEi//eOScOKFF17A66+/jjlz5ij7XnnlFeTk5Ljvc8kll7jfz8/Px1NPPYXDDz9c+dzY2FgkJycrH5MApW1PEAlG2nrxxReRlpaGTZs2YezYsX77maiN6oKuR516IQ01l+ysx67KZiRGGTBzYBRGpJp4WgNQ3dHpeNqUYUDKEHUpi2wJecqEEvm/7xinE5MaHYg16WE0cIKLz0kfEIa4RERERMRKkL6xNDbji82tAYiotdqVff4cW7hz5040NTVh2rRp7n0SakjVh8vq1atx77334pdfflGanzqkugBAQUEBRo8e3enX3r59u1L98eOPP6KsrMzj8xiC9IOaYrUZag84nE7862cLSuvsyu2KBgd2VzXjkonxGJps9NOBhnJ1RxlQvr21skPeVu0DnOr5PWR1R9Kg1qBD2YaoSzG6+jSdDklmTnLxCwmgImP987WJiIiISHNYCdIHByob4FAnW3ooqKhHIMmSlnnz5inbv//9b6WSQ0IMuS3hSVdOOeUUDBw4EP/85z8xYMAAJQSR8ONQn0c+0FCpPgHvoa1lze4ApO1z+RX7GhmCHLK6Y0/H5Swykrjb1R1tw47W6g4KEhGRQFxmoI+CiIiIiIIIr9b7QJa86HQdh3ek+3EpjJAmpUajUanWyMtTK06k2mPbtm2YNWsWtmzZgvLycvzpT39S+n+IVatWeXwNk0ldKmG3tz55ls+RHiESgBx99NHKvmXLlvn1Z6EWTfWApbBXp6O+2Xv/idomNlV1k+qasm1A2VagdKta6dHj6o4hnktaDlHdQQEmf5wTctS3REREREQtGIL0QWpsJCbnJWHV3kr3PlnPf+zIdPiT9PT43e9+pzRHTUlJUfp63HHHHe7GpRKMSMjxt7/9DVdccYUy6UWapLYl1R46nQ4fffQR5s+frzRGlUkx8vWef/55ZGVlKdUjt912m19/FmqZBFMtT8i9lBV1w9BkE/S6ug5VSSPDtSeIEni0hB2ut3J+e1rdkTxEnczC6g5tiknnGGEiIiIi6oAhSB+dOSkb+Wkx2Fpcg+jICBwxOBnp8VHwtz//+c9Kk1NZviIjcm+88UZUV6tl/LL8RcbdysQYaYg6adIkPPbYYzj11FPdn5+dnY377rtPCTkuvvhiXHjhhcrnvPnmm7jmmmuUJTDSY0Q+f/bs2X7/ecKWBB8yTaQPk2ASovQ4fWQM/retHs12NQkZkWrEUXn+/3cYcM2NQOlmoGRzzwIPVneENhkpHJsW6KMgIiIioiCkczp7+fKzBlgsFiQkJCjhgIyBJQo6sgSmvsInX0qWxeytsiExSo+suBDMN+VPVe1B4OAGoHgDcHC9uqzFYT/0eFSp7EgbCaSNAFJHAEmD2bsjVOkj1CVLBjYF7is+hvoHzysREVFgHz9D8JkSkUZI+OGjAEREG/UYlRZCS2AcNqB0mxp2uEKPutKuP4eBB8k4XAYgRERERNQJhiBEgdDcANQU8dy35XSoE1oO/AwcWA0UrQWaDzFpSSo6MsYCGWPUSg9WeIS3mFQgilV/RERERNQ5hiBE/c1uU/uAhO5KtO6r3g/s+0ENPQrXAlZL5/c1moH0MUCmhB7jgIzRQCSf8FLbPiAZPB1ERERE1CWGIET9SYIPadzZh0aomiY/d/E6YO9yoGC5GgZ1xpwMZE8CMg8DMscByfmA3tCfR0taIctfEnI5DpeIiIiIDokhCFF/ksaeTXXhdc4bq4E9y9TQY99PnS9xiYwDBkxSg48Bk4GkQXxSS4em0wOJeWx0S73297//XZm4VlxcjMMOO0wZLz916lSv95UpajJRzeNPV2QkGhsb+RsgIiLSCIYgRP0ZBtSVhc/PuvsbYOdX6lIXp937k1fp5zFwBpAzFUgdpu4j6mkjVFkqRdQLb731Fm644QY8++yzmDZtGp544gnMmzcPW7duRXp6uvd/cvHxysfdf8p0Op57IiIiDWEIQtQfbFag+kBon+vmRmDPt8C2xcD+Vd6Dj6gEIHcakDdDfcsmltQXsemAOZHnkHrt8ccfx6WXXuqu7pAw5OOPP8aLL76I2267zevnSOiRmZnJs05ERKRRDEGI/M3haGmE6gjNHifS42PrJ8Cur7wv9YnNBIYcAwyeBaSPZl8P8o3oFDUEIeqlpqYmrF69GgsXLnTv0+v1mDt3LlasWNHp59XW1mLgwIFwOByYNGkSHn74YYwZM6bT+1utVmVzsVi6aABNREREfscQhMjfpBGqVIIEIafTiTXFTdhY0oTICB2mDIhEfpLx0J8oYYcEHxvfB6r2dhJ8HKtuMrpWY+XitU0OLN/XiMIaGzJjIzA9JwoJUVyqEzSkoig+K9BHQRpXVlYGu92OjAzPqUJye8uWLV4/Z8SIEUqVyPjx41FdXY3HHnsMM2bMwMaNG5GTk+P1cxYtWoT77rvPLz8DERER9RxDECJ/qi0BrDVBe44/3l6P7wtaG/qtLbbivLGxGJcR6f0TKnYBG94Dti8Gmhs8PyZ9GfKPBUacCGQdptn+Ho02J55dVY3yerVyZ2tZs3Jerp6agBiTNn+mkAtAErw/2STyt+nTpyubiwQgo0aNwnPPPYcHHnjA6+dIpYn0HWlbCZKbm8tfFhERUYAwBCHyl0aLGoIEcbXDD/sbO6xuWbq7oWMIUrQOWPMqUOClRDxrIjDqFGDwzJBoUPlzkdUdgLhUNzqwstCK2YO0//NpmvT/YABCPpKamgqDwYCDBw967Jfb3e35YTQaMXHiROzYsaPT+8j0GNmIiIgoODAEIfJXk9Dq/UF9bqsaHbB7aVNSXm9vTUT2/wT8/BpQtMbzThFmYPgJwNgzgeR8hJKKBi8NXdueFwqMmFQgjs0oyXdMJhMmT56MpUuX4vTTT1f2SZ8PuX3VVVd162vIcpr169dj/vz5/NUQERFpBEMQIl9z2DXRCDUjxgCzUYeGZqfH/kGJRuDAz8CPzwAlmzr2+jjsXGD4iUBkLELRoMQIfF/gfT8FgPSTicsCopN5+snnZJnKRRddhClTpmDq1KnKiNy6ujr3tJgLL7wQ2dnZSl8Pcf/99+OII47A0KFDUVVVhT//+c/Yu3cvfv/73/O3Q0REpBG8qifyJamekADE3hT059Vo0OGU4TF4Z1MtHC05SJ5jHy7Y/zrwU7tlL4l5wMTfAkOPBwyh/WdjdJpJ2TaVtv4OhyYbcVgmy9n7ncEIxGeHbOBGgffrX/8apaWluPvuu1FcXIwJEyZg8eLF7mapBQUFysQYl8rKSmWkrtw3KSlJqSRZvnw5Ro8eHcCfgoiIiHpC55TxECFKmo8lJCQoHdzj4+MDfTgUDiyFQH0FtLb8Y/v+Ygze/grS9n0KXdsKluQhwOQF6nhbvQHhQv4s7qy04YBFpsMYMDzFCJ3GJtxongQf8TkhH7oFMz6G8rwSERGF4nUJry6JfKWuXHMBiCzZSd79P0z74RmgqbZ1f0w6MPVSYNi8sAo/XCTwkOoP2aifyb836f1hTuKpJyIiIiKfYwhC5AsyBre2WFvnsnIP8M0jQPG61n3GaHXZy/hfAxFc/kH9SCptJPiQAI7VH0RERETkJwxBiPqquQGo2qf2A9ECezOw5jXg51cBR3Pr/hHzgSP+wFfgqf/Dj8h4IDYDiDDx7BMRERGRXzEEIeproKCBSTBuMrb3i7uB0q2t+6Tx5MxbgJwpgTwyCjf6CMCcCESnqA1QiYiIiIj6AUMQor6Mwq3cqwYhWrD9c+DbR9XKFVfvhcPOByZfzKUv1D/k35wpFohKACLj1CoQIiIiIqJ+xBCEqNejcPcCtsbgP38Seiz7K7D149Z9CbnAcfcDqcMDeWQU6nR6wGgGTDHqJj1nGHwQERERUQAxBCHqVQBSADTVB/+5k+NcfJsa2LgMPxE4+gb1CSlRX0moIUtbZDOY1KoieSvhB5vrEhEREVGQYQhC1FOWA+o0mGB3YDXw+R2txxphBmbeDAyfF+gjI29Bgs6gVk4om87zfejavVU+qfVzvenQqNfZ+X08vkbbr9vy/TyOy6Aua1HeSvDBhxEiIiIi0g5evRL1hKUIaKgK/nO26UNg2WNq3xKRnA8c/xCQmBfoIwsf0uxTCQla3urlbUtw4HrrDhTYG4OIiIiIqD8wBCHqrtoSoL48uM+XhB4//ANY92brvrzpwNz71J4M5DsSXBgi1bGuhvabkcEGEREREVEQYghC1B21pWoIEsxsVuCLe4E937buG/crYPpVarUB9Z6EGhFRrX0u5H0JO1jBQURERESkKQxBiA6lrgyoPRj8E2A+WwjsX6nelmUWR10PjDkj0EemPRIYScghlTNK8BHNvhdERERERCGCIQjRoQKQmuLgPkdNdcAnNwHF69Tb8sR93sNA7rRAH5l2Qg9lfKuMcY1Wqz2IiIiIiCgkMQQh0nIA0mgBPr4BKN2s3pYn8yc+BmSND/SRBS9ZwiLVHZFxLeEHQw8iIiIionDBEITIG+n/Eew9QBoqgY+uA8p3qLcj44GT/wqkjQz0kQUfmcQSGdsSfMQBen2gj4iIiIiIiAIgoM8EnnnmGYwfPx7x8fHKNn36dHz66aeBPCQitfoj2AMQq8UzADEnA6c+zQCkfTPT6BQgeTCQPhJIyAGiEhiAEBERERGFsYBWguTk5OBPf/oThg0bBqfTiVdeeQWnnXYa1qxZgzFjxgTy0ChcVR9QKyyCvQfIxze2BiAxacApTwGJeYE+suCo+IiKV8MOjgQmIiIiIqJgqgQ55ZRTMH/+fCUEGT58OB566CHExsbihx9+wOzZs6HT6bxu9957byAPm0KR0wlUFQR/ACJjcGUKTMkm9bY5iQGI9PiQ0ENCoLQRQPwABiBERERERBTcPUHsdjvefvtt1NXVKctizjjjDMycORMnnHACbrrpJuU+Z555JsaOHeu+3Z7ValU2F4vF0m/HTxrmsKsBiFRYBDO7DVhyF3BgtXpbeluc/ET4VoDIFBwJgcyJ6oQXIiIiIuqzmsZmrNxTgYq6ZgxKicaE3EREGNhPjUJHwEOQ9evXK6FHY2OjUgXy/vvvY/To0erBRUQo+zIzM5XbJpMJ0dHRyj5vFi1ahPvuu69fj580zt4MVO4FbI0Iak4H8NWDwN7v1dsRZuCkx4CUoQjLqg/pgSLjbImIiIjIZ6rrm/GPr3fA0mhTbq/eW4l1+6tx8ZGDlIp8olAQ8EhvxIgRWLt2LX788UdceeWVuOiii7BpU0upfw8tXLgQ1dXV7m3fvn0+P14KIc0NQMWu4A9AxIq/AzuWqO8bTMCJjwAZYxFWTU5jM4DUEWqDUwYgRERERD63bEeZOwBx2V5Si52ltTzbFDICXgki1R1Dh6qvZk+ePBkrV67Ek08+ieeee67HXysyMlLZiA6p0QJU71crLILd+reBdW+q7+sMwHEPANmTETZLXmJSgKhEtQqEiIiIiPym2OL9xcHiaiuGpsfxzFNICHgI0p7D4fDo69EWS7DIJ+rK1DG4WrD7W+D7J1tvH30TMOgohDyp9JCpN5F8sCUiIiLqL9mJUdhR0rHqIzvJzF8ChYyAhiCyfOXEE09EXl4eampq8MYbb+Drr7/GZ5995vX+cXFx2LJlC0pKSpCent7vx0shMAFGqj8aq6EJBzcAX9wjB67ennQRMPpUhLTIWDX84HhbIiIion535NBUrD9QrTRFdRmbHY/BqTHu26v3VuCn3ZVotjswLjsBRw9LZeNU0pSAhiASZlx44YUoKipCQkICxo8frwQgxx13nNf7n3POObjiiitwySWX4KOPPur34yUNszUB1fvUPiBaIGHNp7cC9ib19rB5wOGXImRJ6CE9P9jrg4iIKOztLqvDnvI6pMSYMDornk+w+1FclBFXHzsMawqqUFHXpIQfo7JaK3O/31GGj9YVuW8XVTeitNaKX03JDft/t6QdOqdTXh4PTTIiV8IVaZIaHx8f6MOhQLHWqgGIjMLVgoYq4IMr1GMWAyYBJz2uNgcNNUazGn5IBQgRBRU+hvK8EgXCez/vx8o9le7bWQlRuPTofJhNBv5CgsCfPt2C6obWKhGh1wG3nThSCVCItHBdEnQ9QYh6q8nmwNp9VSisasCARLMy09zUWA7UHtTOSbVZgcW3tQYgSYOAeQ+HXgASEQnEpqvjbomIiIgA7C2v8whAXJUGK3aV4diRGTxHQaCm0TMAEQ4nUGe1MwQhzWAIQiETgPzzu13YX9my3MXpwE+bduKyCVEwGTQyVUQm1Xz5AHBwvXo7OgWY/5fQag6qNwAx6UB0Mqe9EBERkYe95fU92k/9b3hGHLYU13jsSzAbkR7HCZ1aYLM7lJHHYlh6bNguNWMIQiFBKkDcAYi9GWiuw4EGB9YW6zA1Owqa8MM/gF1fqe9HmIET/wzEZSIkyHhbc5K69EWCECIiIqJ2UmO9P5FO6WQ/9b9TDhuAstrdKKtV+9aZjQb8akoO9LImhoKaVMu/snwPLI025XZcVAQumjEI2YnhN/mHIQiFzP/UyhSV5kaP5qdFNRrpA7L+beCX/6jv6wzA8Q8AaSMQMn0/4rMBo0bCKCIiIgqIkZlxyEkyt76wJYWxJgOOHJLC30iQSI4x4fq5w7GrrBZNNieGpsfCFBGe1QRa8/6aA+4ARNQ02vDe6v24es4whBuGIBQSBsRFAI01gKP1f2yRFaeBqoPd3wHfP9l6++gbgbzp0Dyp+JDKD1n6QkRERHSoSwe9Dr8/ejB+3FWhTIeRJ9wzhqQqbym4fk9D00NouXYYqG+yeYSLLoXVjai12hAbGV6xQHj9tBSa6iswIboMP8UCByytuwfEGTAhM8jLJ4s3AF/crVaxiIm/BUafBs2ThqdxWYCBf2KIiIio+yIjDJg5PA0zkcbTRuQjJoMekRF6WG2Odv+/qfvDDZ+hkHbZbYDlAGCtgUkPXDY5HmuLrcoSmMxYAyZmRQZ3U9SqvcCnNwN2dU0lhh4HTL0MmqaPAOIHAFEcSU1EREREFAykAer0ISn4emupx/4j8pNhDMPmqAxBSJsaKoGaYsDR2vNDAg/NNEGtLwc+vhGwtpSuZE8BjrkD0Om1Xf0hAQgbnxIRERERBZXjR2cgPsqInwvUMdQTcxOVYCQcMQQhbZHJL5ZCpfpDs5rqgE9uAmqK1NspQ4F5DwMGIzRJQg+l+iMh0EdCRERERERe6HQ6JfSYHqbBR1sMQUg76iuA2oMe1R+aDHGW3AWUbVNvS+PQ+Y8Bphhokhx3Qo52AxwiIiIiIgorDEEo+MnYW+n90Wb0rSbJ5Jql9wP7flRvR8YBJz0OxGiw8ZdOB8SkA7EaPHYiIiIiIgpbDEEoeDkcQF0pUF8GOFump2iV0wF8/Sdg15fqbYMJOOFPQNIgaI5UfUj1h1arV4iIiIioU812B9btr0JFXTMGp0ZjSFqsspSCKFQwBKHg1FitNj6V5SNaJwHOsr8C2z5tnaAybxGQNQHaXP6Sy9G3RERERCGovsmG577ZhZIaq3vfxLxE/GpKbkCPi8iXNDyKgkKSzQpU7Aaq9oVOAPLDP4CN76m3dQbguPuBvCOgOTGpauWKgdkpEYWOv//97xg0aBCioqIwbdo0/PTTT13e/+2338bIkSOV+48bNw6ffPJJvx0rEZG/Ldte5hGAiDUFVdhbXseTTyGDIQgFB2l2aikCyneo01NCgQQgq14EfnmjZYdOHYM7eBY0Rcb2yvKXuEy1FwgRUYh46623cMMNN+Cee+7Bzz//jMMOOwzz5s1DSUmJ1/svX74c5513Hn73u99hzZo1OP3005Vtw4YN/X7sRET+sK/Sew++fRUa781H1AZDEAp8UCBTX2RaSn259nt/eFSA/B1Y/WLrvpk3A8PnQXP9P5IHA+bEQB8JEZHPPf7447j00ktx8cUXY/To0Xj22WcRHR2NF19s87e7jSeffBInnHACbr75ZowaNQoPPPAAJk2ahKeffpq/HSIKCWlxkT3aT6RFDEEocBotauWHpVDbY2/bk5/luz8Dv/yndd+Ma4DRp0FTjGYgOV99S0QUYpqamrB69WrMnTvXvU+v1yu3V6xY4fVzZH/b+wupHOns/sJqtcJisXhsRETB6qihqYgxGTz2SXPU4RmxATsmIl/j4n7qf031QG2x+jbU2G3AVw8CO5a07NCpFSBaC0BkfK80QNUzJyWi0FRWVga73Y6MjAyP/XJ7y5YtXj+nuLjY6/1lf2cWLVqE++67r8P+X//61zAajb0+fiIif06HqaxvRrPNAbPJgPXRRnz0GJdEU2A1N/uuXyRDEOo/zY1A7UHAWhO6TV2X3AXs/V69rTcAx9wFDDsOmhKdDMRlsf8HEZEPLFy4UOk74iKVILm5uUo/kvj4eJ5jIiKibpDHz4SEBPgCQxDqn3CgtkQdexuqpJ/JZwuBgxvV2wYTcPyDwMAjoSmx6epGRBTiUlNTYTAYcPDgQY/9cjszM9Pr58j+ntxfREZGKhsREREFB9a6k3/Dj+r9at+PUA5ASrcC7/6+NQCRHhrzH9NeABI/gAEIEYUNk8mEyZMnY+nSpe59DodDuT19+nSvnyP7295fLFmypNP7ExERUfBhJQj5Z9lLXWloBx8uO78CvnpADXxEbAZwwp+A1OHQDBl7KwGIOSnQR0JE1K9kmcpFF12EKVOmYOrUqXjiiSdQV1enTIsRF154IbKzs5W+HuLaa6/FrFmz8Je//AUnnXQS3nzzTaxatQrPP/88f3NEREQawRCEfKe5oSX8CIPO904HsPplYNULrfsyxgDzFgHRKdBUAJKQA0T5Zn0dEZGWSHPS0tJS3H333Upz0wkTJmDx4sXu5qcFBQXKxBiXGTNm4I033sCdd96J22+/HcOGDcMHH3yAsWPHBvCnICIiop7QOZ1OJ0K8eUp1dTWbj/mTtVYNP5rqEBZqS9XqjwOrW/cNPwGYeQsQEamxACQXiGJjPiLqiI+h/sHzSkREFNjHT1aCUO9IdtZYBdSVA7bG8DmLu78DvlnUZqmPDph2JTDhfG1NU2EAEtZqrTZsPFANnU6HsdnxiDbxoSCc7SqtRWFVIzITIjEkLVb5d0FEREQUqnjlSz1jtwENlUBDBWD33axmTfQ5WfE0sOn91n0xacCxdwHZk6EpDEDC2o6SGry2Yi+a7GoR4Cfri3DRjEEYnBoT6EOjfiaFoG/8VIANB1qXMA7PiMWF0wfBoGcQQkRERKGJ02Go+/0+ZNJL2Vag9mB4BSD7VwHvLPAMQAbPBM55RaMBiPQA4RKYcH3S+9+1he4ARFhtDny4tjCgx0WBsbHQ4hGAiG0Ha7F2XxV/JURERBSyWAlCnXM41CUvUvkhIUi4qa8AVvwN2P556z7p+THjWmDUqdpa/uIiU2DYBDVsWRptKKtt6rC/2NKIhiY7zCZDQI6LAmNPufc+TnvK6jB5IKdFERERUWhiCEIdNdWrwYf0vZApKOFGfubN/wN+eAZoqmndnzkemHUrkDQImhSXxTG4YS7aZECUUY/GZs//r2MjDYiMYGFguEmOMfVoPxEREVEoYAhCKlneIqFHQ1V4NTpt3+y1YAXw03NA+Y7W/ZFxwBF/AEaeDOg0+kQxNh2I0dDoXvILo0GPmcPT8PnGgx77Z49Ih549IMLOpLwkfL+jDBV1rcsb480ROHxwckCPi4iIiMifGIKEM4cdsFrU4CNcxtt25uAGtfKjaK3nfhl9O/0qbVdQRCerIQgRgGNGpCM1JhJr91UqS7om5SVizIAEnhtfLyV02ACnXf07q1TUtZ9Gr1NDVdn0hpb3DYC+66DV7nCioKIeRoMOOUnRfTrMKKMBV8waogQhB2Q6THwUjhqaithIXhoQERFRq4q6JqzcU4H6JhtGZsZjVJa2+wvySidcg49GC9BUq1Y/hLODm4A1rwJ7vvPcnzYKmP5HYMBEaJpUscgyGKI2xuUkKBv5YFpWc71aPadsTYCjWf0721uuUEQf0boZjMrbfRYbXl9VAovVoQRYOUlm/Hb6QMRHGXv97eKijDhhLP9GEBERkXf7KurxwrLdSjN98dPuSuVFk5PGa/f6gSFIOJALcwk8ZLmLXLCHe/AhP/++H4G1rwOFazw/lpALTL0cyJ+tzcanbRnNQGKe9n8OomD62yFVcxIky1ub1Q/fwyHlHh0mcMlknzeXV8HS0NLPRafD/kYLPvqhFudPzgQMJjUsMUSqb/n/PREREfnAF5sPugMQl+93luHIoSlIjNZmHzGGICF7oV7bcrFeG749PtqTJyw7vwTWvenZ80NEpwBTLgFGnAwYQuB/C3lClDiQT4SIfKG5saVZdFXfqjz6oLjWjgpXAOL6O++0YUuRBahr9zdLAhC9EYiQYKTdJhOuGJAQERFRN8kUwfbkMqSkxsoQhAK8/lwqPGST4IPVHp6q9gKb/gts/QSwtpn24qr8mHC+2vtDniCEAimlTxoYGmEOUSDJ39O6UjVMDrBoo07JLtoX8pkjvFR6yZ3sTermjVIx0hKItA9JDtGThIiIiMJLdqIZlgbP51DSTz8jPgpaFR7PkmQZiLnlok+eIGqZXNxKRYOtAWhu2aTSI9yXuHh78rL7WzX4KPy548fTRwMTLgAGHa39fxNtybMkWQIjT26IqPeVHzVFQdUwOiHKgFGpJmwq9Qw2puf24gJEltrI5u3nUwKSlsfLOj8s9yEiIiJNmTsqA7vL6tDY3FqROmt4GhLk+bVGhUcIUn0AcFSr7ysN51wXecZ270sTOmNwvBIm1R2uV/JkU5ruSfhhbZk0QF6bFO7/Edj+udrotP16fbmoH3IsMOo0IHNcaJaExw8ATDE9/jTp9GzQ6xAZEUKBEFFv/u7WHgQaKoIyWP7VmFh8sase60uaYDLoMDU7Ekf2JgTpTkCCeqAu8BUwREREFFgDEs24bu5w/FxQiXqrHaOy4pCfFqvpX0t4hCBtyXpu2brqkyHd+ZVQpKVDvzK2MKJ1jKHy1jXS0LXpWkYetrx1a7mQluBCWcPtaNnajE6UUYqyyYVn27d0aBJ07P9JrfrYs0xtWNieLHkZfTow4kQgKoQnYsSk9XiUb2VdE95ZvR+7yupg0AMTcpNw2oQBMMoNonDSVA9YDvin2amPREbocNLwGGUjIiIi6i8JZiOOGZEeMic8/EKQ7pBgIogvhMNefTmw7ydg73KgYIW6NKg9CTuGzAGGzQMyxoRm1UeHUbgZPf60f/+4Fweq1EBQBlKs3luJKKMeJ48f4IeDJApSdeVAbbHPqz8cTie2ljWjpsmBoclGJJtZaUVEREQUaAxBKPhJZUzxenWsrWzl273fL8IMDJwBDDseyJ2mVvOEg4gotdqlh0osje4ApK01BVUMQSg8SOgh1R8NVT7/0rVNDvzrZwsO1qrTZCSHPWlYNI7MM/v8exERERFR9zEEoeAjy4QqdgKFa4HC1cCB1WoDWG+k4mPgUUD+LCB7Svg1BJVlWtIINRj62BBprf+HTI7yU/PTL3c3uAMQV97yyfZ6jE2PREIU/38lIiIiChSGIBQclR6lW4AiCT3WAgfXd/3EJHUEkDtVrfaQBqcSBIQjZRJMLhDRu9G+6fFRyEkyY3+lZ8A0MS/RRwdIFMRNlCUA6Sxc9YFdldJc1JPDCeypasZhmWEW1hIREREFkTB99kgBLz8v2QyUblbflm3tugdLVKIaeMiWczgQndyfRxy84rJ6NQmmrQum5SmNUXeW1iFCr8OE3ETMG5Pps0MkCspKs8o9XTfH9oGkKL1HJYh7v5lVIERERESBxBCE/KuuFCjZ0hp4yFtrTdefIxNOsiYAWYepW8pQdQIPtZIgyAdhUGK0Cb8/Oh8NTXZlRQ1H5FJI66cARMwcaMa28mal+sNlSLIReQlh0quIiIiIKEgxBCHflZdXFwBl29V+HuU71E0muRxK3AAga3xL6DFBbfIZ6tNc+sIUrVaB+JDZxKkVFAZVaH5eAtPW4CQjLp8Sj+8LGmGxOjA8xYQj86L65XsTERERUecYglDPn0jUlQGVuz3DDnl11WE79OdLlUfaKCC9ZZP3zexB0W0y8SYhjyERUU9V7wOa6vv1vEnVR944Vn4QERERBROGIOSdBBqWIqBqD1C5Vw05qlreNnfziURkHJAyrDXskLexGXwC31uyJEgmwRj4vy1Rj9QUA40WnjQiIiIiYgiCcK/qaKwCLIVqs9KqAjXwkOCjah/g6DjdwCudQZ1SIr07koeob2WLSWPg4UsJ2YDR7NMvSRTyGirV6jUiIiIiIlaChEkjwNoSNeRou1UfAGoKux5F24EOiMsEEgcCSYOA5Hw17JD3Izjy0a8kUIpK8O/3IAo10v9DQl4Kaps3b8abb76J7777Dnv37kV9fT3S0tIwceJEzJs3D2eddRYiI/kYQ0RERL7BuvpQIONl24Ybyvst1R01Rd3r1dGWPkJtTpo0sDXwkE16URjZ2K/fRcUDcRn9/32JtN6sWarbpOKNgtLPP/+MW265BcuWLcORRx6JadOm4YwzzoDZbEZFRQU2bNiAO+64A1dffbVyv+uuu45hCBEREfUZQxDNLFup7ljJ4Xq/OxNYvC1hkaqO+AFAfLa6uYIP2SdBCAWeLH+Jzwn0URBpsxGqvZtL+iggpMLj5ptvxjvvvIPExM4bZK9YsQJPPvkk/vKXv+D222/v12MkIiKi0MNnusG0bKWutGPA4dp6tGylRYRZ7SPRNuhQwo5stUEpg47gnwQjjVD1+kAfCZG2yBLA3vzNpH61bds2GI2Hnp4zffp0ZWtuZqhFRETkLzWNzSirbUJGfCSiTaEdE4T2Txfqy1ZcI2fbBxyu9+VjOp0/fhLqt0kwHK9J1CPWWjUEoaDXnQCkL/cnIiKi7lm8oQjLdpTB7gCMBh3mjsrAzOFpIXv6GIL4etmK1eK9kkP21Zf1ctlKRsegI06qOwYAphif/ggUBCS4SsjhJBii3vQBkb+3pAlPPfVUt+97zTXX+PVYiIiIwtXmIgu+2db6PLXZ7sSnG4oxODUGucnRCEUMQXo7VrZ6f8u2ryXkkPdl2UpNL34LUV4qOVqWsMRmAgb+msJKXJbaDJWIekb+FrMPiGb89a9/9bhdWlqqTIZx9QepqqpCdHQ00tPTGYIQERH5yaZCi9f9GwstDEHCLuiQZqOWlmDDFXgot/cDzfV9WLbipT+HOZnLVkgVmw5EJ/NsEPVUfQVg7UUITQGze/du9/tvvPEG/vGPf+CFF17AiBEjlH1bt27FpZdeissvv5y/JSIiIj+JMhq87jebvO8PBeFdYtDcoFZyVO5RNxmnKLcl6LA19ryHgzQbbd+XwxV8cNkKHYqEHxKCEFHP+y3VFPOsadhdd92lTIlxBSBC3pdqkbPPPhsXXHBBQI+PiIgoVB0+KAk/7CqHzeF074uM0GNCbueT27QuPEKQhiqgZgdQJWHHXqBqr/q2trh3Y2WlX4MSduSoY2WVHh1ZgMHkr5+AQl1UghqWEVHPK/ckuHY6eOY0rKioCDZbx+bgdrsdBw8eDMgxERERhYP0+ChcfOQgLNl0EActVmQnmTFvTAYSzKHbkDw8QpD//AqI7OaUFL0EHQNagg4JObJbgo4c9ucg/5D+H/Lvi4h6TibBSFUf9Y30UpEXCMp3AhW7gIqdQOH2fjurc+bMUZa9/Otf/8KkSZOUfatXr8aVV16JuXPn9ttxEBERhaP8tFhcPisW4SI8QhBvTLFA0kAgUbZBQFKe+jY+C9CH72mhQAQguewJQ9QbTfW9m7oV7prqgNKtQNlW9a0EHhKAOOye97O2lsX624svvoiLLroIU6ZMcY/ClcqQefPmKcEIERERka+Ex7P9rInAgKFq4OEKPqJT+MSTAsucqC6rkpG4RNQzDkfLMpj+e6IeEoFH6Ra191V3GGUEe/80m01LS8Mnn3yCbdu2YcuWLcq+kSNHYvjw4f3y/YmIiCh8hEcIcuIjQFz4lPeQRpqgsgcIUe/VFAH2Jp7BtqQvijT5Ll4PFK8DDm7sXuAhy0ClEjI5H0gZAiTLlg84Y4AHZvTrOZbQg8EHERER+VN4hCBEwUSmCMWmBfooiLSr0QI0VAb6KIJjKk7JZqD4FzX4OLjh0GOCpYF3ylAgdQSQ1rIlDQYMXpqf1dSiP+3fvx8ffvghCgoK0NTkGXA9/vjjPv9+FRUVuPrqq/G///0Per0eZ511Fp588knExnb+osns2bPxzTffeOyTXibPPvusz4+PiKitkppG/LCrApaGZgxNj8WUgUmIMOh5koh6gSEIUX+RMcpS/SHLYIiod+w2wFIYvpUe0rh0/0p1K1rbdTWM18AjHzAE30P/0qVLceqppyI/P19ZDjN27Fjs2bMHTqfT3SjV12TsrkylWbJkCZqbm3HxxRfjsssuwxtvvNHl51166aW4//773bejo6P9cnxERC6FVQ14/ttdsNrUSWgbCy3YdrAGF04fxJNE1AvBdyVEFIrkyUhiHmCMCvSREGmb5QDg6DhKNZg5nE7oe9v7p74cKPhBDT0OrOq6AiYqEcgcD2SOU9+mDdfM6PaFCxfipptuwn333Ye4uDi8++67SE9PV4KKE044weffb/PmzVi8eDFWrlypNGMVf/vb3zB//nw89thjGDCg85HlEnpkZmb6/JiIiDrzzbZSdwDisrmoBvsq6pGbzCCWqKcYghD1xwQYaYAq6+6JqPfqKw693COIfLu3Ad/tbURtkwNDko04bUQM0mK68Xegci+w5ztgz7fAwU1SAuL9fjHpQM4UIGuCGnxoeNKUhBL/+c9/lPcjIiLQ0NCgLEuRiovTTjtNGZXrSytWrEBiYqI7ABEyileWxfz4448444wzOv3cf//733j99deVIOSUU07BXXfdxWoQIvKr0hqr9/21VoYgRL3AEISC6tXSb/Y0Yk2xVXl/QmYkjhlkhkGvjYv6GqsDn++sx/aKZsSZ9Dh6UAzGDxsImJMCfWhEodH/oqYYWrGqsBGfbq93395Z0YwX11hw04xE73/TZETt9iXAzqVAVYH3L2o0AwMmATmHAzlT1eoyjYYe7cXExLj7gGRlZWHnzp0YM2aMcruszPdjkIuLi5VKk7YkfElOTlY+1pnzzz8fAwcOVCpF1q1bh1tvvRVbt27Fe++91+nnWK1WZXOxWCw++imIKFwMTIlGUXWjxz7585/HKhCiXmEIQkHjsx31+HZv6x/4pbsalFdQTx8Z/JN9ZN36S2stKKqxK7erbRH4z3YdDKl6jDEH+uiINE7G4CrjcD1LgYPZqsKOr9pVNTqwo6IZI1JblqjUlQI7lgI7PlfH13ojk1oGHQXkTgPSxwRlPw9fOOKII7Bs2TKMGjVKWZJy4403Yv369Uq4IB/rrttuuw2PPPLIIatOekt6hriMGzdOCWzmzJmjhDZDhgzx+jmLFi1SlvkQEfXW7BHp2Fpcg8r6Zve+mcNSkRobyZNK1AuheTVFnq8OyhOIIGdzOPHjgY5PGlYXWnHC0BhERQT3q507K21qACJLXozR7kkLK3aWY8yAhEAfHpG21R4EmhugJfZO8ppm+YD0+Nj0AbB3OeBUg9NWOiBrPByDjkZD9lGITsmBLkSqPboi019qa9VpNBIYyPtvvfUWhg0b1qPJMBKeLFiwoMv7SPNVWcpSUlLisd9msykTY3rS72PatGnK2x07dnQagki/kxtuuMGjEiQ3N7fb34OIKMFsxLVzh2HDgWpYGmzKdBj2AiHqPYYgwUwfoT6ZbvtW2QyAztD6Vi6QZfKIsnVysSxBiBKGyFuH5+awqxfi8nFpOCi35a3sc72v3PZfmCK9nqw2Z6f7gz0EaXAYAVMMEOGZyNc3tX+CQ0Q9Yq0F6ny/HMLfxmWYsN/S2sA1xlaF6XVfYMySJUBtUcdPSBsJDD0OGDoHK6visGRXA2pKHUgyV+GkYTEYk66NBqe9YbfblfG448ePdy+N6e3I2bS0NGU7lOnTp6OqqgqrV6/G5MmTlX1ffvklHA6HO9jojrVr1ypvpSKkM5GRkcpGRNQXkREGTB6YzJNI5AMMQQJNQgtDpPrkOSIKiDCpt6Wjv96Hs7+VoMQVJBh6P5pSCUSa1bfK7WbA7rrd8rYXJOTITzJiV2VrmZ/IiY9AQlQQz0CPjAWiUzA0ORrGnZvRbPcMckZmxgXs0IhCYxzuAWjRUXlRqGiwY8feAhxZ8T6mWL6A0dlunG10KjDyJGD4PCBxoLJrd2Uz3tvc2jOissGB/2yowXVHJCI1OjSbKxsMBhx//PHKMhVpVtofZNmNTJ2RcbcSuMiI3KuuugrnnnuuezLMgQMHlKUur776KqZOnaoseZHxubJcJyUlRekJcv3112PmzJnuAIeIiIiCH0OQQAQe0tzOtUnwoZVSZ1mLrqxH72LMq1SLSBhib2oNSJRwpM37nazrP2NUDF79pQaldWr1RJJZj7NHxyDoyO8tMh4wJ7qXvUjbj19NycU7q/e7R5gNz4jFrBGHfkWSiDph2a/+zdAgfcUunH7gdTj3LIXOY8mLTu3vMfp0YOB0tbqvjbXFHZcFygqadQetOHZw6I5BHDt2LHbt2oXBgwf32/eUKS8SfEjQIVNhzjrrLDz11FPuj0swIk1P6+vVBrcmkwlffPEFnnjiCdTV1SlLWuRz7rzzzn47ZiIiIuo7hiD+JOGGhBymWMAUDRhjfFvdEbQ/s0ndOiNLbCQkcVeQqO+nmppx/ZFRKKhsgMPhxMDECOiDISCSJyny+1N+j7Gd/mxjsxMwLCMWBeX1iDcbkRHfRVhERF2TJTCyFEZrpHLlx+fUKS9q5KGKMAOjTwXGnqWOzCYPDz74IG666SY88MADyvIUWRLTVnx8vM/PmEyCkcqOzgwaNEhpeu0iocc333zD3xwREZHGMQTx+RmNbHmyHKO+DfXQozekl4m+pRKmHXnCMFCmFrZfatNhs3v2MvHJcbX0XpGlSMryJNmkWsfUo/WawzK4BIaoT5rq1GaoWtJoAX5+GdjwrueywKgEYOzZ6hZ16CfyMhr8p3ZNog16YHxGaPeUkCUm4tRTT/VoBCshhNyWviFEREREvsAQpK/kYk2mgcjyCOkP0a4xJvVx6Y2XoKQDdyDSruGre9lN25BE166RbEuDWQlAgqHqhCjcSQCqjMMN/qlWCgk81r8DrH4ZaKpp3R+VCEy6EBh1avf+jrUYnGTEmaNi1MaoVgeSzXrMHxYTsv1AXL766qtAHwIRERGFCYYgvSFPlqXSQ4IPeZVPnkRT4CghBn8HRJonwUf1vkP2AbE7nFi6u0HpnyHR5cSsSBw72Nz/y+fKtgNfLwLKtrbuk0qy8ecCE3+jPk70wuHZUZg8IBL1zU7EGHVhMSJ31qxZgT4EIiIiChMMQXpCLmgl9GDwQUTke7IERpbCHML/ttXhx/2tS0aW7mpAQ7MTp4zop0bK0sfo51eANa+pVWgKHTD8BGDqpUBsRp+/hQQ6sabQDj8KCgqQl5fX7fvLtJbsbPZTISIior5hw4pDkcamcZlA2gggeTAQncyqAyIiX2uoUpuhHoLV5sTqwo4TVFYWWjuMqPaLks3AO5eoy19cAUjSIOCM54Bj7/RJABIuDj/8cFx++eVYuXJlp/eprq7GP//5T2V6zLvvvtuvx0dEREShiZUg3sjSCqn2MCf1aC03ERH1QnMDYCns3l0dTrRMofbcb5f9ThgNfqqekB5D694CfnymNfyQx4qJvwUmXaQug+mBbeVN2FnRjIQoAyZmmmA2ht9rEps2bcJDDz2E4447DlFRUcpUmAEDBijvV1ZWKh/fuHEjJk2ahEcffdTdPJWIiIioLxiCtCVjUCX4kIZ2YbAGm4goKBqhVhW0aWTctViTHnkJESiotrVrKBrhvyDBagG+fBDY+33rvtQRwOyFQOqwHn+59zfXekyA+WZPA648PB6JUeHV2yglJQWPP/64EoR8/PHHWLZsGfbu3YuGhgakpqbiggsuwLx585QqECIiIiJfYQgiE0LMiYA5GTBG+ezEEhHRITgcQNXeQzZCbe+s0bF47ZcalNWrFRlpMQacOSrWP6e7cg/w6a2AZX/rPml6OuVSdYJVDxXX2jqMwLVYHfh6TwNOH+mnnyHImc1mnH322cpGRERE5G/hG4JI6XJ0ihqAcLIIEVH/k2BBlsL0UHqMATdMT8A+i02ZDpMTH+GfCSoFK4AldwPN9eptWSZ57N1A3hG9/pIHLLZO9rsarBIRERGRP0WE5ZKX6FQgKj7QR0JEFL5qioFGS68/XUKPvAQj/Gbz/4Bv/ww4W8KJlKHAvD8B8Vl9+rLpMRGdBjtERERE5H/hEYLIK4TyCl5MKhudEhEFWl15tybBBITTCax+CVj1Quu+/NnAMXf65PEjNyECY9NN2FDS5N4XbdRh9iA24SYiIiLqD+ERgiQPARJTA30UREQko3BrioI3APnh78Av/2ndN+5XwIyrofSP8pHzxsXil+Im7FCmw+gxLTtSmRITdEwxgT4CIiIiIp8LjxAkomejC4mIyA9k+YvlQHCeWplOs+yvwMb3WvdNvwo47Dyffyu9ToeJWZHKFpRkuWhMGtDQs4a1RERERFoQHiEIEREFlrUWqN6nVlsEGzmm759oE4DogJk3A6NPQ1hRlo2mtU5K83MI8uGHH3b7vqeeeqpfj4WIiIjCB0MQIiLyr6Y6oKogeAOQH58FNryr3pZlL8fcAQw/AWEbfvST008/vdtNcO12Ts8hIiIi32AIQkRE/g1AKveqy02C0ZrXgLWvt96evTB8ApDIOCA2o9/DDxeHI0j/TRAREVFIYwhCREThGYBs+Rj46bnW20ffBIyYj7AYFR+bqb4lIiIiCjMBDUEWLVqE9957D1u2bIHZbMaMGTPwyCOPYMSIEYE8LCIi8kUPEGUJTJAGIAU/AN880np72pXAmDMQ0iKigLgMtQIkCNXV1eGbb75BQUEBmppaRwiLa665JmDHRURERKEloCGIXOz88Y9/xOGHHw6bzYbbb78dxx9/PDZt2oSYGI7mIyLS7BSYYG2CKip2AUvuBJwtfSbGng1MuAAhy2AEYtMBcxKC1Zo1azB//nzU19crYUhycjLKysoQHR2N9PR0hiBEREQUuBBk+fLl+OKLL7Br1y7lYkUuUPLz85Xw4ogjjujR11q8eLHH7Zdfflm52Fm9ejVmzpyJ2bNnK0GJN/fccw/uvffenh4+ERH5U30FUFMUvAGIBDSLbwOaG9Tb+bOBGddI902EHGnyGpMKRKcCej2C2fXXX49TTjkFzz77LBISEvDDDz/AaDTiN7/5Da699tpAHx4RERGFkG5fFckrMhJKSDghS1jklZr//ve/yv5vv/0Wc+bMwdy5c1FeXt7rg6murlbeyitAQr7PmDFjcOONN6KoqEjZpk+fjksvvRQ33XRTh8+3Wq2wWCweGxER9ZPaUsBSGLwBiMMGfHE3YDmg3k4dDhxzF6A3ICQnvqQOUytAgjwAEWvXrlUe6/V6PQwGg/J4npubi0cffVSpEiUiIiLylW5fGV1++eUwmUzYt2+fcrHy9ttvK7efeuoppTLk4MGDSElJwRVXXNHrLvHXXXcdjjzySIwdO9YdhkRERCA2NhaZmZnKJt9Tqk9kn7ceI/IKkmuTCygiIvIzCT2qDwC1B4P7VP/wDLB/pfp+VCIwb1HAJqP4te9H8mAgMVddBqMRUvUhAYiQilDpCyLksVyuO4iIiEJNs92BFTvL8Z+fCvDZxmJUNzQH+pDCRreXw3z22WfKMpWsrCyvH5dQ4sEHH8TEiRN7dSDSG2TDhg1YtmwZemvhwoW44YYb3LelEoRBCBGRHznsagNUmQQTzLZ+Cqx7U31fKj/mPQTEZSKklr7IuNvoZE0u7ZFrh5UrV2LYsGGYNWsW7r77bqXS9LXXXnO/MEJERBQqnE4nXvp+N3aX1bv3rdpTgT/MHoqkGFNAjy0cdLsSRKo8tm/f3uV99uzZ417K0hNXXXUVPvroI3z11VfIyclBb0VGRiI+Pt5jIyIiP7FZ1SajwR6AHNwEfPto6+2jbgCyJiBkRMWrS19iUjQZgIiHH37Y/SLLQw89hKSkJFx55ZUoLS3F888/H+jDI0JNYzOstpZmykREfbT1YI1HACJqrXZ8v7OM5zaYKkFuu+02nHfeeUqH9pNOOglDhgyBTqdT+njs2LED3333He64444eNSuVBOzqq6/G+++/j6+//hqDBw8+5OfI9yQiogCz1gDV+9VKkGAmjVBlEoy9ZeTq6NPVLRTIcpf4AUE78rYnpkyZ4n5flsO0b5xOFChF1Q14d/V+HKhqhNGgw9TByZg/Ngt6Pa9Hiaj3SmusXveXWLzvpwCFIPKKTEZGhvJqjfTekDBCQgzXhcvkyZPxj3/8A6effnqPlsC88cYbSoPVuLg4FBcXu9cAm81mr58j99uyZQtKSkqUCyUiIupntSXqpoVeJV892NqrJHM8cOR1CAnRKeryFw00PSXSKrvDiZeX74GlwabcbrY78f2OcsRFGTFreFqgD4+INCwnKdrr/txk7/spgCNyzzzzTGWrqqrqMCI3MTGxx9/8mWeeUd7K1Jm2XnrpJSxYsMDr55xzzjlK89VLLrlEWUJDRET9xG4DLPsBa602Trn0ANn7fWsj1Ln3aapZqFcRkWr1hykGoUQqQbuq9JRrDqL+tqu01h2AtLWmoJIhCBH1yeDUGEzMTcSafVXufWlxkThyaArPbLCFIC4SeEyaNKnP31wqSQ5FJtG09dvf/lbZiIioH0nfD1n+YtdI5/LiDcCPatCuOPYudVysloVw9YdMh2urubkZa9asUZbF3HzzzQE7LgpvneVyei7NJiIfOGdKDibkJWJPWR1SYk0Yl50IU0ToPcaHTAhCRERhQsJqWfpSX6a+rwXSB+SLu1v7lUz8LZB3BDTLYAISskOu+qOta6+91uv+v//971i1alW/Hw+RyE+NRWK0EVX1nuHvpLwkniAi6jOpgByeEads1L8YNRERkXe2JnX6S12pdgIQb31ADv89NMucCKQMCekApCsnnngi3n333UAfBoUpaX66YMYg5LWs0Y+M0GPW8FSWqxMRaRwrQYiIqKP6CqCmGHA6tHV2OvQBuR/Qa/ChTm9Qe39EJSCcvfPOO0hOTg70YVAYy4iPwpWzh6Chya5Mh4kw8PVDIiKt0+CVIRER+bX6w3JA7QGiNQe99QHR4AQHoxlIyAUiTAgXEydO9GiMKj3DZGJcaWmpMnmOKNDMJkOgD4GIiHyEIQgREanLSOrL1f4fWqv+EFYLsOSeNn1AfqPNPiAxqWrz0zBrvHj66ad73Nbr9UhLS1Omx40cOTJgx0VEREShhyEIEVG4a6oHaoqA5gZoktIH5GGgtrhNH5BLob3lL9lAVDzC0T333BPoQyAiIqIwwRCEiChcSdWENBCV/h9atv5tYM936vuR8cDce7XVByQMl78Ii8XS7fvGx4dnOERERES+p6GrRCIi8lnlREOlGoC4lo9oVclm4Ie/t+sDkgFNTX+JGyDrPxBuEhMTPfqAdMVu1/i/UyIiIgoaDEGIiMKJtQaoOQjYGhESP8uSuwGHTb192PnAwBnQBHnyL2GN9AAJU1999ZX7/T179uC2227DggULMH36dGXfihUr8Morr2DRokUBPEoiIiIKNQxBiIjCgfT7kJG3Wpz60lk1y9eLgJpC9XbGWGDq5dBM/4+EHCAyDuFs1qxZ7vfvv/9+PP744zjvvPPc+0499VSMGzcOzz//PC666KIAHSURERGFmvCrvyUiCic2K1C1DyjfGToBiNj4LrD7G/V9CRPm3gcYNJDrG0xA0uCwD0Dak6qPKVOmdNgv+3766ad++uUQERFROGAIQkQUimxNQPV+oHwH0FiNkCJ9QJY/3Xr7mDuAuExoogFqcj5gjAr0kQSd3Nxc/POf/+yw/1//+pfyMX946KGHMGPGDERHRyv9SbrD6XTi7rvvRlZWFsxmM+bOnYvt27f75fiIiIjIPzTwshkREfWo8qOuVA0+ZMlIqKkrAxbfBjia1dvjfw0MOhpBT6pVZAJMGDZA7Y6//vWvOOuss/Dpp59i2rRpyj6pAJGA4d133/XL92xqasI555yj9CB54YUXuvU5jz76KJ566imlV8ngwYNx1113Yd68edi0aROiohhuERERaQGvxoiIQkFTPVBVAJRtBxqqQjMAsTcBn98O1JeptzPHA9OuRNAzJwGJeQxAujB//nxs27YNp5xyCioqKpRN3pd98jF/uO+++3D99dcrfUe6WwXyxBNP4M4778Rpp52G8ePH49VXX0VhYSE++OADvxwjERER+R4rQYiItKzRooYCEoKEMgl1vn0MOLhRvS2TVY5/CDAYEdRi0oA4DY3sDSBZ9vLwww8jWO3evRvFxcXKEhiXhIQEpXJFepqce+65AT0+IiIi6h6GIEREWuOwq9Ue9eVqdUQ42PAOsPVj9f2ISGDeIiA6GUFN+pSE8QjcQ1m3bh3Gjh0LvV6vvN8VqboINAlAREaGZ6glt10f88ZqtSqbi8Vi8eNREhER0aEwBCEi0tKY2/qKln4fDoSNA6uB5X9rvT37diBtRI+/jMPpxH6LDSaDDpmxfn74ix8Q/CFNgE2YMEEJD9LT05X3dTqdsuSkPdlvt9u79TVvu+02PPLII13eZ/PmzRg5ciT6y6JFi5SlN0RERBQcGIIQEQV71YeEHg2VaggSbir3AJ/fAThbngRP/A0wtHU5QndJ+PHvdTWoalTDo4GJEfjN+DjEmvzQGosBSLeXl6Slpbnf94Ubb7wRCxYs6PI++fn5vframZnqBKKDBw8q02Fc5LaEOJ1ZuHAhbrjhBo9KEH9NvCEiIqJDYwhCRBRs5NVwa40aflgtodnktDtkys3HN6jnQuRNBw6/rMdfRqoL/rO+NQARe6ts+GR7PX41JtaXR8wApAcGDhzo9f2+kFDFFaz4mkyDkSBk6dKl7tBDAo0ff/wRV17ZeYPeyMhIZSMiIqLgwOkwRETBoqkOsBQCpVvVSS+hOua2OyT4+OQmoPagejt1ODD3PkBv6PGXKq61o6Kh4/KhTaU+7qfCCpBek5GzH3/c0vMFwC233ILExETMmDEDe/fuhT8UFBRg7dq1yltZbiPvy1ZbW+u+jyybef/9993Lcq677jo8+OCD+PDDD7F+/XpceOGFGDBgAE4//XS/HCMRERH5HkMQIqKAVnzUtgYfFbvVnh8OW3j/Tprr1QCkfId6Oy4LmP8YYIrp1ZeLjNB532/wvr9XGID0iUyFMZvNyvsyaeXpp5/Go48+itTUVGWMrT/cfffdmDhxIu655x4l+JD3ZVu1apX7Plu3bkV1dbVHOHP11Vfjsssuw+GHH6583uLFixEVFeWXYyQiIiLf0zm9dSELEVKmKuPr5AImPj4+0IdDRKT2+JAqB9maatXb1MpmBT65EShco96OSgRO/weQ2LflEi+vtWBrWbPHvuOHROOYweoT7z6Rcb2x/lmCES6PodHR0diyZQvy8vJw6623oqioCK+++io2btyI2bNno7S0FKGC1yZERESBffxkJQgRkT9JztxUD9SWAOU7gdItQPV+dakLAxBP0vj101taAxBTHHDyE30OQMS5Y+NwRE6U0gg12azHCUOjMXuQD169lxG4IRiA9LfY2FiUl5cr73/++ec47rjjlPelwqKhIQwbAhMREZHfsDEqEZGvQw95Mi9LOqTSQwKQcBpn25d+KLIEpnidetsYDZz0OJA6zCdfPipCh9NGxiibz5gTgTh1Ygj1jYQev//975XlKNu2bcP8+fOV/VIJMmjQIJ5eIiIi8hmGIEREfWFvbgk9ZKsDmhsZevSUjP+VAESqZIQpFjjpL0DG6OD9txkZC8RnB/ooQsbf//533Hnnndi3bx/effddpKSkKPtXr16N8847L9CHR0RERCGEPUGIiLpb4WFvUsMO6Vtha3krIQj1XtU+4JMb1OawIioBOOmvQNqI4D2rRjOQNBjQh/aKUvau4HklIiIKxesSVoIQEbVlt6lhh2tTAg8JO6zhO67WXwrXAp/fATRWqbdj0tQlMMn5CFoGI5CYF/IBSCB89913eO6557Br1y68/fbbyM7OxmuvvYbBgwfjqKOOCvThERERUYjgVRwRhVfAIctVZCytLMGoLVUrECr3AmU7gIOb1CUZFbvU5qXSzFQamNpkiQsDEJ/a+D7w0TWtAYgEH2c8H9wBiE6vBiAShJBPyRKYefPmKWNyf/75Z1itVmW/vNoj43OJiIiIfIWVIEQUvCR4UDZpLNry1nW7s00mrjjt6luHrWWfjZNYgoWEUMseB7Z+3Lov53DguAfVPht+VNlgx+ayJpgMOoxNNyEqooevAyRkq0thyOcefPBBPPvss7jwwgvx5ptvuvcfeeSRyseIiIiIfIUhCBH1jYQSrpDBFT4ob13BhL1jeOERaEiFRdu3jnb7KGRU7AaW3AVU7m7dd9h5wLQrAL1/H45+Kbbi/zbWwtHyT+rT7fX4/aR4ZMV18/vGpqv9Ssgvtm7dipkzZ3bYL2t/q6paqoWIiIhCjN3hRHmtFXFRRphNhkAfTthgCEJEXZMgwtUTQ5qAuvtlSPDRzAoLOjQJtta/Dfz4rPpvR3n0MQMzbwaGz/P7GWy2O/Hh1jp3ACLqm534ZHs9fjepG421IuPUEIT8JjMzEzt27OgwDnfZsmXIzw/iJVJERES9tOFANT78pRA1jTYYDTrMGJKCE8Zm8Xz2A4YgROQZeLjHvdarvTDkSSsrMqi3pN/Kt38Gita07kseAhz3AJA0sF/O68E6uxJ6tLenqhuTfSIigYRc/xwYuV166aW49tpr8eKLL0Kn06GwsBArVqzATTfdhLvuuotnioiIQkp1fTPeXFkAu6P1BZtvtpUhIz4KE/OSAn14IY8hCFG4kyoPaRTaVAM01bcsVyHqIwnSVr8MrHtTXS7lMu5X6vIXCRf6SWKUHga9lJx67k82Gw7dCFUCEE6C8bvbbrsNDocDc+bMQX19vbI0JjIyUglBrr76av8fABERUT/aWFTd4bpErD9QzRCkHzAEIQpHtiZ1KkejRa32IPIVqRra/TWw/G9A7cHW/XFZwOyFQPbkfj/XsSY9jsiJwvcFrf/WdTrg2MGHaHIaPwAwRvn/AEmp/rjjjjtw8803K8tiamtrMXr0aMTGxqKhoUGZGkNERBQqIjp5gcUor9qQ3zEEIQqnJ6dWC1BfATTVBfpoKBQVbwBWPg8cWN26T28EJpwPTLwwoIHCScOikRVrwMbSZhj1wOHZURia3MWo2+hkwJzYn4dIAEwmkxJ+CBmT+/jjj+PRRx9FcXExzw8REYWMcdkJWLyhGA3Ndo/9UwZyKUx/YAhCFA7hR0MlUFfW2pSSyJdKtwIr/wkUrPDcnzsNOPJ6IDE3KCoNJg+IUrZDkjG4UrlCfidBx7333oslS5YoAcgtt9yC008/HS+99JJSGWIwGHD99dfzN0FERCFFJsFcctQgfLSuCHvL65EcY8SxIzMwLCMu0IcWFhiCEIUyqfqoK1WnuhD5WslmYM1rwO5vOi4jOeIPwODZ6roTLVH6gORo77g16u6778Zzzz2HuXPnYvny5TjnnHNw8cUX44cfflCqQOS2BCFEREShJicpGlfMGgKn06m8WEP9hyEIUSiSBqc1RWpzSiJfctiBPd8B694Citd5fiw2A5i8ABg+HzBo9OElPqtfm7aGu7fffhuvvvoqTj31VGzYsAHjx4+HzWbDL7/8wgtCIiIKCwxA+p9Gr1KJyCuHA6gtVitAiHypoQrYthjY8C5QU+j5segUYNJFwKhTAINJu+c9KgEwcy1uf9q/fz8mT1ab5Y4dO1aZCCPLX3hBSERERP7CEIQoVEiz0+oD7PtBvq362L8S2PKRWv3RdtStSBoEjP81MGye9qsnJLyRZTzUr+x2u9ILxCUiIkKZCENERETkLwxBiEJBbSlQV6I2QSXNkbWgFqsTMSYdIvQd14TaHE7UNzsRZ9L5/xVy+TdUvh3YsRTY/rn676o9aXgq4UfO1NDonSE/Q0I2oNdm74maxmZERhhgitBr8t/+ggULlAoQ0djYiCuuuAIxMTEe93vvvfcCdIREREQUahiCEGn9lfrq/YC1JtBHQr20tawJH26tQ0WDA9FGHY4ZbMZReWb3x7/a3YBv9zag0eZEWowBp46I6Xq0a2+Dj8rdwI4vgJ1fAtX7Ot5HlokMPwEYeQqQNBAhJToVMHk+6daCwqoGvLt6PwqrG2Ey6DB1cArmj8vU1FKSiy66yOP2b37zm4AdCxEREYUHhiBEWmVrAqoKAFtjoI+Eeqm60Y7X19XA5lBvS7XHx9vqkRptwMhUE9YdtOLznfXu+5fWqfe/eUYiYkx9fNXfbgOKfwH2LAP2LgMs7fp8CJ0ByDsCGHkykDdDu81ODzUONzYdWmOzO/DK8j2wNKpLlJrsTizbUYZ4cwSOHpYGrZBRuERERET9KQSvaInCgEx9qdzbsUcDacqGkiZ3ANLW2mKrEoKsLW7q8DGrzYlNpU04PDuq59+wpljt8SHbvp+AJm8VRDpgwARgyBwgf3ZoNwqVion4bE0u6dlZWucOQNpaW1ClqRCEiIiIqL8xBCHSGmutWgHi9PLsmcJCt5+zy7+Vwp9bgw9vy1yUL2hQg49BRwP5xwAxqQgLMtLX2IswKQhoMLchIiIiCgoMQYi0pNGiPpFlA9SQMDY9Ep/tbECz3bOh7aQstUnkpCwTNpd6VoNERugwOq2TMbQNlUDxOqB4PVD0C1C6FXDavd/XFAvkTQcGHgnkTQMi4xFWTNGaDnvyU2OUpS+WBs9qkEkDQ7hyh4iIiMgHGIIQaQUDkJCTEKXHhYfFKY1Rpd9HrEmPYwebMTzF5A5J5g9z4Os9DUq/kIxYA04bEYNoo14NwqoLgKKW0EPCj84qPYRMPkkfA+Qcrm7powB9mD4E6PRAfA60LMKgx8UzBuO9Nfuxr6IBkRF6HJGfghlDUgJ9aERERERBLUyvgIk0Rqa/sAIkJMVH6pRGqFUNdiRF6ZAS7dnw9OiBZszIjYK1rhLRVVuBXVuA0k1A8QagsarrL540CMieooYeAyZqcgKKX8RlAhGdVNNoSGZCFP4weygamuwwGnRKMEJEREREXWMIQhTsmuqAKi6BCUUNzQ78c3UNapvU/i77LHa8srYGV47XI8e6AyiVwGMLDCVbEF1b3PUX0xuBtJFA5jggazyQMQ4wJ/bPD6IlkbFAdDJCidlkCPQhEBEREWkGQxCiYNbcyCaoIWx9SRMcDVXIt+5GtnUXsq07kGPdjpTthwg8RGQckDleDT3krQQgEWovEepiSZBMgyEiIiKisMUQhChY2ZqAKhmD20ljS9IWGWcsU33KdwLlO5TtsJLtmGotP/TnRpiBtOFq0KFsI4CEXLW3BfVsGYzByDNGREREFMYYghAFIwk+JACxNwf6SKg3GqvdQYe67QQqdwP2dpNevHxqk86EpsRhiM0e1Rp6JOapVQzUe1I5Y+bkFCIiIqJwxxCEKNjI1A+pGLBZA30k1K3qjn3tAo8dQH1Z985dZByqY/Kx2ZGH/aZBOBA1DIMGDcWpo+IBnY7n31e4DIaIiIiIWjAEIQo2lgNqM9QesNqcWL6vETsrmxEfqcdReVEYEMf/vQNR3eGVLFuR5SspQ4GUIUDKMPVtTDoSdDoc1uxAeq0dx5n1SIhixYfPxQ8ADPz/gYiIiIgYghAFl7oyoOEQY0/bcTqdeGmtBXurbO59G0qacPnkeGTH84lfr6s7KqR3x3agbIf6fl1p95ddJA9tE3gMBZLzu2xaajbqkZ/E/h5+EZWgbkREREREDEGIgoi1BqjpxlSQdnZW2jwCENFsd+K7ggacOzbOhwcYghotatDRplmpL6o7uJQlSEgTVKkCISIiIiJqwZeJiYKB9P+o3t+rT61q9D49pqLB0ceDCrE+KxIwlW0BSre2LmepK+ne55viWsKO7ld3UBCQcbhsKEtEREREbTAEIQqKSTAFvR6FOzDBqBQeyPP8tvKTjGEceBSpYYcr9JDNaulmdUdOm8CjZWN1h/ZEpwCRsYE+CiIiIiIKMgxBiAJNKkD6MAkmLcaAYwebsXRXg3tfVpwBMwdGISwCj9qDQMnmngceSnVHS1WHa0saDBjD4LyFuogoIC4z0EdBREREREGIIQhRINWWqL1A+mhufjTGZ5iwo8KGxCg9RqYaoQ/FEavSq6NsG1C8ATi4AShe371xtOYkIG0kkDYCSB0JpA4DYjPYuyMUSTVPYi5/t0RERETkFUMQokA25ZQQxEfSYyKULaQ0VKpBh2wSepRuOXTT0vaBh7yNSeOT4nAhFSDs1UJEREREnQixZ0xEGmFrAiwHAn0UwaexGihcAxxYDRT+DFTu6fr+xmggfTSQMQZIG8XAI9yZE4Ho5EAfBWnEQw89hI8//hhr166FyWRCVdWhx5MvWLAAr7zyise+efPmYfHixX48UiIiIvIlhiBE/c3h6FMj1JDS3KCGHRJ6HPhZndqCdh1e24rPATLHAhljgcxxag8PTv8gdx8QjsOl7mtqasI555yD6dOn44UXXuj2551wwgl46aWX3LcjIzklioiISEsYghD1N6kAsTWG98+/dzlQsEKt+uhseYvOAKSPBDIPAzLHq+GHLHUhak+CMOkDotfz3FC33Xfffcrbl19+uUdnTUKPzEw23iUiItIqhiBE/amuXF3yEU4cNqBoHVCwXA0/qvZ2ckcdkDocyJ4EDJgEZB0GmGL6+WBJc6QBsIw1Zh8Q6idff/010tPTkZSUhGOPPRYPPvggUlJSOr2/1WpVNheLpRvTq4iIiMhvGIIQ9ZemOqC2OHyCD6ny2PkVsPsboLGTtfYx6cDA6UDOVDX4iIrv7yMlrYvNBCLjAn0UFCZkKcyZZ56JwYMHY+fOnbj99ttx4oknYsWKFTAYDF4/Z9GiRe6qEyIiIgo8hiBE/cHeDFTtA5xd9LvQOvnZZILLtk+BXZ0EHzK+VPp55E0HBs4Akodwagv1niyPiun8FXgKP7fddhseeeSRLu+zefNmjBw5sldf/9xzz3W/P27cOIwfPx5DhgxRqkPmzJnj9XMWLlyIG264waMSJDc3t1ffn4iIiPqOIQhRf4QDSiNUW2ie65piYNtidave1/HjskwhbwYweCaQOw2ISgjEUVKokaqheDZCJU833nijMsGlK/n5+T47bfK1UlNTsWPHjk5DEOkhwuapREREwYMhCJG/1RSpU1CC1O7KZmwoaYLJoMPkAZFIjfZe0u1BJttIj48N7wH7f/I+qUOCjyHHqFUfRjO0pNnuxJpiKwprbMiIicCkrEhERugCfVjkIr1iEnJZRUQdpKWlKVt/2b9/P8rLy5GVlcXfBgWlHSW1+GxjMYqrG5GVGIUTxmQiPy020IdFRBRQDEGI/Km+Qt2C1Ld7G/Dp9nr37e/3NeKiw+IwJNno/RMaqoAtHwEb3/fS30SnNjUdfiKQPwswRkOLJAB5frUF+y2uyh0rVhY24vLJCQxCgoEpGkgcyACE+qygoAAVFRXKW7vdjrVr1yr7hw4dithY9UmiLJuRnh5nnHEGamtrld4eZ511ljIdRnqC3HLLLcr9582bx98IBZ3SGiteWb4HNoe6FHdfRQNeXr4H184ZhpRYjnYmovDFEITIX5rq1SqQINVoc2DproYOAcBnO+vxh+R2S1YsRcDafwNbP+440jZuADDyJGD4CUCc9sdGrjtobROAqIpq7FhdZMWM3KiAHRe1VIBIAMJRuOQDd999N1555RX37YkTJypvv/rqK8yePVt5f+vWraiuVid6SePTdevWKZ9TVVWFAQMG4Pjjj8cDDzzA5S4UlH4uqHQHIG0f51fvrcTxY7T/eE1E1FsMQYj81gi1IKgboZbWOdBk73h8RTVtAoDKvcCaV4HtSwCn3fOOuUcAY89S+3zou7GERiMKa9r9nN7OCwWmBwiXwJAPvfzyy8rWFWebv+FmsxmfffYZfwekGVabw+v+Jrv3/URE4YIhCJGvORyaaISaFqNX+oC0D0Ky4iLUSTY/PQ/s+kqeBrR+UHp7jDoVGHMmkJCDUDQgznugo5wXCoyYNCAug2efiKgHxg6Ix4qd5V72s0E5EYU3XtUT+ZrlQFA3QnWJitBjTr7ZoydIgtOCC6rfBf7vQ7X5qUtkHDDuV8DYs9VX5EPY+IxI/HjAin3VrSFWVpwBk7O4frrfyUjl+Cx1FC4REfWINECdNyYDX24pUZbByAsfc0ZlYFBqDM8kEYU1hiBEvlRbAjSq68e1YOZAM3LjI7C5yIKhB97H0IK3oG9uDUWUJ5+HnQeMPl3txxAGjAYdLp0Uj7XKdBg7MmINmJjJ6TD9/4swq9VGMmKZiIh6ZfaIdEwbnIKyWivS4iIRZQyd5atERL3FEITIVyT8kBBEYwbX/ozBa/7s2cQ1wgxMOB847FzNTnnpaxByeDaboAaETqcuf5FN3icioj4xmwzITQ6/x3Iios4wBCHy1SSY6gPaOpcNlcDyp4Dtn7fu0xmAUScDU34HRKcE8ugoHEXGAnFZrP4gIiIiIr9hCELUV7amlkkwGum2LtMOtn0KLP8bYLW07h8wCTjqBiB5cCCPjsKRKRqISVdDECIiIiIiP2IIQtQX0jy0am/QT4LxWLLz9cPAnmWeTU+nXw2MmM/lB9R/ZKlLZLxacSQhCBERERFRP2AIQtSXigoJQGxWbZzDwrXA0vuAujZ9S4YeBxx5LadvUP82PJWGuxKAGPgQRERERET9i1egRL1VvU/tBaKFapWfXwFWv9S6ZCcqEZi9EBh0VKCPjkKdwaRWesh0IVMcgw8iIiIiCiiGIES9YSkEGtv00wjm5qdL7gIK13j2/phztzp9g8hXDMaWLVJtbKpsZoYeRERERBRUGIIQ9VTNQaC+IvjPW8Uu4NNbWkff6vTq1JeJvwX0hkAfHXXWJ0N+T7LB9X7LmFjlra71bdvP6WrJlueObpz39t9P73lcMkFI73oboW4SfnCcLRERERFpAEMQop6oKwfqSoP/nBX8oFaANLcs14lOBY67H8g6LNBHFl5cAYErLHBvEiAYWoKElrcSMEi4QEREREREfsMQhKi7pPrDVVURzNa/DSx/qrX/R+oI4IRHgFguf/E5CS5k2Yf0vVA2o+f7rI4gIiIiIgoqDEGIuttbQ/qABDMJPb5/EtjwTuu+wbOAY+9SJ3JQ70mYIb0ujFFARFRLv4soNeggIiIiIiLNYAhCdCgNVcEfgDhswNd/ArZ92rpPen9MvaylvwT1iIQcEhwZo9WwQ95nVQcRERERkeYxBCE6ZABywEuDySBibwaW3g/s+lK9Lf0lZt0KjDwp0EemHRJyyAhXCT1kM/BPIxERERFRKOKVPpGWAxCbVW2Auvd79bY03Zx7H5A/O9BHFtykZ0dkrBp8mGI5LYeIiIiIKEwwBCHqqgdIMAcgzQ3A4tuAA6tan9jPexjImx7oIws+spRFKjwi49XwQ5a7EBERERFR2GEIQuRtCkyw9wCRCpC2AUiEGTjxESB7cqCPLLiCD6nyiJLgI57VHkRERERExBCEyENtKVB7MLhPit0GfHF3awAiT/Tn/wXIHBvoIwui4COhJfhgU1giIiIiImoV1M8Q7r33Xuh0Oq/b7NnseUA+ZikK/gBExuB+9SCwZ1lrBchJDECUxqbxA4C0kUDSQMCcyACEiIiIiIi0tRzmpptuQmFhITZs2ID33ntP2ffYY49h8eLF7ttEfSZ9P6r3A43VwX+c3/0F2LGktQfICX8CMsK0AkSawErYEZUIGKMCfTRERERERKQBQR2CxMbGIjo6GiaTCZmZme59ERERSE5O7nB/q9WqbC4Wi6Vfj5c0yGEHqgqApjoEfQDy4zPApg9ax+Aedz+QMwVhxxQNmJPVJS+y/IWIiIiIiCgUlsP01KJFi5CQkODecnNzA31IFMzszUDF7uAPQMS6t4C1/265oQOOvRMYdDTChk4PmJOAlKFAcr5aAcIAhIiIiIiIwjkEWbhwIaqrq93bvn37An1IFKya6oHynYCtEUFv51fAiqdbb8+8CRh2PMJmyUtsBpA6HEjI5rIXIiIiIiIK3eUw3khT1M5ERkYqG1GXGqrUEbjSZDTYFa8Hvrxf1sOotydfAow+HSFP+p3EpKrVH6z4ICIiIiKicK0EiYuLQ3FxMbZt2xboQyEtqjmoNkHVQgAivUoW3wrYm9Tbw08EplyCkBYRqVZ8pA4DopMZgBARERERUXiHIPPmzVPeTpo0KdCHQlprgFq5F6grhSY0VAKf3Ng6sSZ7CjDr1tANBZTwI0cNP1j9QURERERE4boc5oknnvC4PWbMGKUShKjbmhvVqgpXRYUWjvfTW9QlOyJ5CHD8Q4DBiJAjP1NsujrmNlQDHiIiIiIiChpBH4IQdZfT6cSOkloUVjciKyEKw9JjoWuU/h9F2lj+4qpY+fI+oGSTejs6FZj/GBAZi5CiNwAxaUB0CsMPIiIiIiLqNwxBKCQ4HE68umIPth6sbdnjxIgEBy4cbYBeSxUGK/4G7P5Wfd8YrQYgUikRKuR3YU5WfyYJQoiIiIiIiPoRQxAKCesOVLcGIFJN0VSLrfV2rEuJxYRMjUwMWvcWsP5t9X2dATj+QbVHRqiIjAPiMtX+H0RERERERAGgucaoRN7sLa9T37E1AlaLGoTI/iqbNk7Yrq+A5X9rvT3rFiB3GkJm3G1iHpA0kAEIEREREREFFCtBKCSkREco1R+weTY/TYnWQM5XvB5Yer+yhEcx+WJg5MkIiaUv0tNElr5oaUkSERERERGFLA08QyQ6BGstJsdWIMnoWfWRZNZjclaQL72o2qtOgnFNrhl+AjDld9A8o1mdahOXwQCEiIiIiIiCBitBSLscDqC2GKivgFkPXDklAcv3NaKo1obM2AgcmRsFszGIc776cuDjG9XlOyJ7CjDrNm2HBjo9EJsBxKQE+kiIiIiIiIg6YAhC2tRUB1QfaK2gABAXqce8odHQzPF/chNQU6TeThkKzHsYMBihWaZoID4HiDAF+kiIiIiIiIi8YghCmq3+0Cy7Dfj8TqBsm3pbKidkFK4pBpoklStK9UdqoI+EiIiIiIioSwxBSDusNYClELA3Q7OcTuCbPwH7f2odG3vS40BMGjRJxt0m5ALGqEAfCRERERER0SEFccMEojaVE1X7gMq92g9Alj8FbPu0dXTsCY8ASYOgSeYktfkpAxAi0pg9e/bgd7/7HQYPHgyz2YwhQ4bgnnvuQVOT54Sx9hobG/HHP/4RKSkpiI2NxVlnnYWDBw/223ETERFR37EShIKbLHupPQg47NC8lf8E1v9fyw0dMOduIOswaLL5afwAwJwY6CMhIuqVLVu2wOFw4LnnnsPQoUOxYcMGXHrppairq8Njjz3W6eddf/31+Pjjj/H2228jISEBV111Fc4880x8//33/E0Q+ZjT6YROy83iiSho6ZzyFyZEWSwW5SKluroa8fHxgT4c6onmBnXpi7wNBWteA358tvX27IXAyJOhORFRQGKuugyGiEJauD2G/vnPf8YzzzyDXbt2ef24nIe0tDS88cYbOPvss91hyqhRo7BixQocccQR3fo+4XZeiXpqX0U9Pl5fhL3l9UiOMeLYkRmYPDCJJ5IozFl8+PjJShAKvqUvUvnRUImQsf5tzwDkyOu0GYBEJQDx2YCeq+iIKPTIRVVycnKnH1+9ejWam5sxd+5c976RI0ciLy+vyxDEarUqW9uLOCLyrqHJjpe+34OGZrUCuKKuGe+s3o8EcwSGpsfxtBGRT/DZDAUHKUiqKwfKt4dWALL5f8D3T7TennYFMO4caE5cploBwgCEiELQjh078Le//Q2XX355p/cpLi6GyWRCYqLnUsCMjAzlY51ZtGiR8sqVa8vNzfXpsROFkvUHqt0BSFsr94TQtSERBRxDEAq8RgtQvgOoKQqN3h8uG95RJ8G4TLoImPhbaK7/R2Iex98SkSbcdtttSg+BrjZZwtLWgQMHcMIJJ+Ccc85R+oL42sKFC5UqE9e2b98+n38PolBhczi877d7309E1BtcDkOBI/0+aoqBprrQq2pZ8yrw0/Ot+8b/Gjjc9xfXfiXTayQA4fQXItKIG2+8EQsWLOjyPvn5+e73CwsLccwxx2DGjBl4/vk2f7O9yMzMVKbHVFVVeVSDyHQY+VhnIiMjlY2IDm1MVgI+WV+E9pnHuBw2Yyci32EIQv3P1qT2/WisDr2zLwHIj88Aa//dum/ihcDUywAtdTg3moHEgYCBfyKISDukcals3SEVIBKATJ48GS+99BL0h1juJ/czGo1YunSpMhpXbN26FQUFBZg+fbpPjp8o3CVEG3Hu4Xn48JdC1DTaYDToMGNIKibkMgQhIt/hMxzq36andSVqz49QHErkdADf/QXY9EHrvml/ACZeAE2JjAUS8tj/g4hClgQgs2fPxsCBA5WRuKWlpe6Puao65D5z5szBq6++iqlTpyr9PH73u9/hhhtuUBqoSmf6q6++WglAujsZhogObWx2AkZlxaO81oq4KCPMJgNPGxH5FEMQ8j/p81FXCtRXqEFBqC7t+fIBYPc3LTt0wNE3AmPOgKaYE9UJMFqqWiEi6qElS5YozVBly8nJ8fiYsyWkl0kwUulRX1/v/thf//pXpWJEKkFk4su8efPwj3/8g+efyMcMeh3S46N4XonIL3RO16N9CPLlLGHqbfhRBtSXh274IaSvyeJb1eauQmcAjrkDGD4PmhKTqk6BISLiY6jf8NqEiIgosI+frAQh/yx7keAj1MMPUbwe+Gxh61hfUwww9z4gT2Prw2MzgNjuraMnIiIiIiLSKoYg5Dv25pbwI4SXvbS15WPg2z8Djmb1tiwjOfFRIGkQNCUuC4hJCfRREBERERER+R1DEOo7m1Vd9tJYFZoNT9uTkb7fPwFs/aR1X/Zk4LgHgSiNLbuKHwBEJwf6KIiIiIiIiPoFQxDqWxgg4Ye1JnzOYslm4It7Acv+1n1jzgRmXKu9cbIMQMKWze7ArrI6ad+L/LRYpQEdha+KuiYUVjUgIz4KaXGRgT4cIiIiIr/S2LM2Cjip9JCKD1nyIhNRwoUs71n7H2Dlc2rDV2E0A0fdCIw4EZrDACRsyZPdV1bsgaXBptxOjDZiwYxByhNgCj+frC/Csh1lyp92GQp1+KAknD4hGzpOiCIiIqIQpQ/0AZCG+n3UlgClW4HqA+EVgFTuAT68GvjxH60BSPpo4OyXtRmASA8QLoEJW++s3u8OQERVfTPe+/lAQI+JAmNHSS2+264GIELe/rS7EhsLLfyVEBERUchiJQh1zVoLNFSoS17Cod9H+14nP78CrP034HA9adQBE38LTPmd9pa/uKbAsAlq2LI0NqOourHD/oKKejQ22xFlNATkuCgwthZ7X8q47WANxmYn9PvxEBEREfUHDT6Lo36p+mioUse+2pvC84Tv+xH47jHAUui5hGTWbWoTVC2KSeUY3DAXGaGH0aBDs93ZYX8E+4KEndgo75cAsZG8NCAiIqLQxSsdUkmVh9WiBh/S8DTcqj5cyncCPz4LFCxv3aePACZcAEy6CIjQaNNAcxIQlxnoo6AAi4wwYOrgZHy/o9xj//QhKYgwcHVkuJmUl4hvt5WivsnuEYgdPogTo4iIiCh0MQQJd7LcpbFaDUBc/S7CkaUIWPUvYNtnkgi17s+aAMy8GUgaBM2KjFOrWIgAzB+bhfgoI9buq1Kmw0wamIQZQ1J4bnxNgmT5mypNlWVr+3dFoQN0+tZN3/0Qqqq+SQmt+lqxERdlxOUz87F0S4l7OsyxI9ORFGPq09clIiIiCmYMQcKRVHo0WtTww93rIkzVHlSnvmz6AHA0t+6PSQemXgoMP1EdmaBVMsEmIVfbPwP5lF6vw8zhacpGPgg6bI0tm1XdZDmh/F3t6d9W+X9UZwD0ri2iZTOq/Yf0RpQ1OPHWz8XYX9Wo3H10VjzOnpzTp14u6fFROG9qXq8/n4iIiEhrGIKEA4cDaKpVm5vKFu7Bh6jYrTY83fG5ZwWMVE1MvBAYe5Z2l764yPFLBUsPXmEmokOQoEP+jsrf1Kb6lioPHwUqzq7Dkzd+rEJRjV0JTJw6AzbuqUWMoxZnTMgCDBKWmNSNoScRERFRpxiChPKFulR8KBfrdb67UNcyeZJRtBb45T/A3u87BgbjzgEm/EYNQrROXkFOHKi+okxEfWO3AY3SLLpKrfoIgJI6mxqAeAQmwC8F5Tgjv91Sm7aBiPxtU27LWxNDUSIiIgp7DEFCha0JaK5TAw95dTJcp7p4I/1Oti5Wl7xU7fX8mCkOGHumGoBI89BQIP0FEvOACK7rJ+rz39W6UjUACXCzaH0n1R0Gb7tlSY5sqPPyCUbPUET+TiiBSSQDEiIiIgoL4RGCNDcAjpjQeVVclm/Iq5HyczXXq2+VC15yk8qXol+ArZ8AO5eqlTFtxaQB488FRp0CmGJC68QlZAOm6EAfBZG2Kz+kX1AQhB8uqdEGDE6KwO5Kz+UyUwb0cNmeOyCp915BJgGJUj1iAhoa+njURERERMEnImz6PzSXtjSbc70K1lIu7Lroc+0PprXU0svD7mq21+TZfI+8q9gFbP8M2L5EfRLTXtZhwKjTgCHHqr/vUBObAUQlBPooiLSrrhyoKwnKaVnnjY3Df7fWYXNpE4wGnRKAHDfEh4Gnq6GrBOuiptZ3X5uIiIgoSIRHCOIiF7WuKorOuDvyt+3O77ptaO3e7x5taGjp6q/rfrChjExsORblrVx42lumCrS8SueaMEBdk1dpK3YCu78Fdn2tvt+eVHrIlJfRpwHJ+aF7Rs2JQGzPJn40NNnx8foibDhQjcgIPaYOTlZGZOqCKQwk6q+lL5b96nLCIBUXqcdvxsfB7nAqDzmdLZEhIiIios6FVwjSHb0ZbSiUi9FOwhClnNoZNGXVIVGqfnCD2tx09zeA5UDH+0g4lTsVGHY8MOhodVRsKJOfLz67x5/2xk8F2FGivtprtTnwxeYS5f05ozJ8fohEQUtGhsvfET9UfxTV2FDT5EBeghFREb4JLQx6hh9EREREvcUQxFfcQYfPviK1ZSkE9v0I7PsJOLBK7YXiTfpoNfgYOjd0Gp0eiizrkUkwPXxVuLzW6g5A2vppdwVDEAoftSXq5mNWmxNvrK/BtnK1X1NkhA5njYrBuAyNj94mIiIi0jiGIBScGquBonXAgdXAvh+A6n3e7ycVH9LnI38WMGgmEJuOsOKaBGPo+f/KTXbvY5OlIoQoLILr6v3q3xo/+HpPgzsAcYUib2+qw5BkI6KNer98TyIiIiI6NIYgFBxqS4GitepEF3lbubvz+0rjz5yp6nKXvBlqL4xwJZNgernUJzM+CqmxJpTVeo5THjMg3kcHRxSkpDeTBKvWGr99i63lHceUN9ud2FnRzGoQIiIiogBiCEL9T6bblG0DSjYDJZuAko3qcpfOSLVHxhggd5q6pQ4PnXHHfSFVL32YBCPNT8+floc3fixwByFD0mJw0vgsHx4kURAGIFV7gaY6v34btdqjY48RM6tAiIiIiAKKIQj5lzSZlRHFpRJ4tGwyxlam4nQVeqQOA7ImqEtdBkwEIuP4m2pLzocPlv5kJZhxw3HDUWxphMmgR0os+xVQqC+BKfB7ACJm5EYpVR9tZcUZMCSJD7tEREREgcSrMfIdeWJRvlMdU1u+XX1f3krlR1cMJrWhqQQesmWMVcfaUif/10YBCbk+OztSESJhCFHIU5bAdGwG7A+j00w4f1wsvt3bqEyHGZZsxPFDojl++v/buxPoqOrz/+Of7AvZISRBIKyCgISKokBdsUL1oKg/f562VtEW2x6pVuu/rUcEl/6KnkMprcdijy1at3+t/aO1tlARNeK+wU9QFqGEKCQkBLKSPfM/z72ZLJCQhSSzvV/nXGfuzUy43ps7851nvs/zAAAA+BhBEPSctZG09JXDu5sDHbbslipOkNLStpBn6ig36JE+0b1NG+N2OEHXLA3ICqGGU1gR6JHyArcV7gCyTjB0gwEAAPAvBEHQOZvBYd+cHsmTjuyTSptvbVvj8UX/OpQ0TEo/TRravFg9j6h4jnpvWAtcmwESGc3xA3qiqkQ6WsIxAwAAAEGQkGc58tWH3ZkdpfluwMOKBtptRYHk6Wa71Mg4afAYafA4KW1c8+0YKSYh5A9xn0nI5HgCPWUdYOy1DAAAAGAmSIhobJAqC6Xy/W6ww27L9reuN1T3LB3DZiOkZLtBDgt22GIzPizVBf0jLlUaNJijC/REQ51U9hXHDAAAAC1IhwkW9UfbBDbaBDtsqTh44m4snc3sSB0ppYySUrPdOh52P+kUKYI/mwEVHe8GmQD0sBVuvlvDCAAAAGjGp9mASls50hrYaBfw2O/+rKfCI6XELDewYR+ybYaHN+AxaKhbgwK+ZZ1zkkdyLoCestfFhhqOGwAAANohCBIIaSvWdcVue5K24mWtZp0gR5slufl2ULqb3gL/7gTDzBugZ44elmrKOGoAAAA4DkEQX6WteAMbbWdz9CZtxcQPPj7A4V1ik5lFEMidYKJifb0nQGCpr6EQKgAAADpFECQQ01baBjsSh/FBORjZ+aazDtDzOiDWwttehwEAAIAOEATpDWsbW1UilX/ldh6wxXvfAh31vUhbiYrveCaHLQlDSVsJJZamFJ/m670AAo+1wm2o9fVeAAAAwI8RBDlhoKO4NcjREuiwGR1f9W6g3ZK20jybo23QIzaFtBW4rXATMzgSQE9ZDZDezLQDAABASCEIUlMule6TjuS5tzaV2gl4HJAa63p2NMMipMRMKXn48YVILb0hKq6/ziOCQUwirXCB3misd1+zAQAAgC5EhsysjorC5mDHPqk0TzqS79729JtDq8/hzOQY7gY3rHilN+iRkEknD/SOdfGxTjC0JQZ6zgLXTb0oKg0AAICQExpBkCevkCJ7MKsjItoNdDjBDQt2DG+9T30O9LXoeCklmwAI0BtWn6muimPXF2orpcP/kQ7vcW/37+S4AgCAoBMaQZDGWikyrOMaHfbh05bU5lv7Nt4CHWHhvthThBpLkUoZJYXz9wb0mNVmqizkwPWUdc+x2ZGHdkjFu9ygR8me449lbfB22cnLy9MDDzyg1157TYWFhRo2bJiuu+463X333YqOju70eRdccIFyc3PbbfvBD36gRx99dAD2GgAA9IXQCIJYqkrWmOMDHlaDAfBpCkw2ARCgtx/kLQ2GdrhdHyfrmlO8sznosdNdastD+u9ux44dampq0h/+8AeNGzdO27Zt06JFi1RVVaUVK1ac8Ln2uPvvv79lPT4+fgD2GAAA9JXQCIL81+NSYoKv9wJoZQE4aoAAvVdZ1Lt25KGQ0lL0mVTwv1LR590PeFib9rSx0uAx7q0t0RnSg5coGM2bN89ZvMaMGaOdO3dq9erVXQZBLOiRmZk5AHsJAAD6Q2gEQQB/Ep/mdguiCCrQO1YD5Oghjp6xtJbCT6XCre6tpbXI03Ur7iETpHRbJkqDx7udzY59TaqoDKljXFZWprS0tC4f98wzz+jpp592AiHz58/XPffcw2wQAADaqG1oVHREuML89PMOQRBgINkHjUFDOOZAbzU1SWX7QzcNxtq67/9Y+upDd6k40LOAh90OsrpX/jko8ZXdu3fr4Ycf7nIWyLe//W1lZ2c7NUQ+/fRT/fznP3dmkKxdu7bT59TW1jqLV3l5aKciAQCC1+cHyrVuW4EOVdZp8KBozZuSqSmnJMvfEAQBBkJ4hNthiDo0wMmx+haNPej2Fega691ZHt6gR/GOzmd6WEFvS2PJPF3KnOreJmSEVMDjF7/4hR566KETPmb79u2aOHFiy/r+/fud1JhrrrnGqfdxIjfffHPL/dNPP11ZWVmaM2eO9uzZo7Fjx3b4nOXLl+u+++7r8f8LAACBpKi8Rs+8v09NzcOUkqo6/d8P8rX4onHKSo6TPwnzeIL36zT7tiU5OVllu95VEjVB4MsOMMkjpMjOOw4A6IaaMqn0y4A5VFsP1urNfTWqqGvS+LQoXTI2Xokx4d1L98l/V8rb5N521gI4PErKnCJlTXMDHhlT3ILLfaS8olLJp8500kSSkpIUCIqLi1VSUnLCx1j9D28HmAMHDjgdX8455xw98cQTCu9hpy4rpJqQkKD169dr7ty53Z4JMmLEiIA6rgAAdOXfnxXqjZ3Fx20/d/wQXXp6lvrss30fvH8yEwR+ZUthrT4+UOt8z/m1zGhNHxarQFHb4NHbX1ZrV0m9EqPD9fXsOGWfMkwalB5S38QC/aKxQSovCJiD+1lRnZ7d2lpT46MDtdpf0aAfz0juOD/WCpruzZX2bHTTXZoaOv7FNtNj+FnukpXjBlnRIj093Vm6w2aAXHjhhZo+fboef/zxHgdAzJYtW5xbmxHSmZiYGGcBACCYNXmngByjsZPtvkQQBH4jN69a63cfbVnfc7heh6ub9I2xgdF+8KlPK5x9doRH6vMK6Xtp8RqTQAAEOGnlX3UeGPBD735Vc9y2gopG7TnSoHFpUe6Ghlp3psfuDdK+dzpO87EUupEzpRHnSMPPlOIHD8DeBz8LgNgMEKvvYXVAbAaJl7fziz3GUl2efPJJzZgxw0l5efbZZ3XppZdq8ODBTk2Q22+/Xeedd56mTp3qw/8bAAB8b+qIFG3afahd2Tb73mfaiBT5G4Ig8AtNHo/e3Hd8u8u3v6zR+aPiFB3h34GE/LJ6NwBiOflRsVJkrJokvbmrWGPSac8MnJSqEnemRAA5Wm+vAMertu1H8qTPX5R2rZdqK45/kNXxGHWuNPo8KTNHiuCtuq9t2LDBKYZqy/Dhw9v9zJslXF9f7xQ9PXrUDc5bCs2rr76qVatWOWkwltJy9dVXa8mSJX2+fwAABJpTUuJ09RmnaP22QlXWNmpQdIQumZypEWn+94U2I6tAKKgZEe3eWv53eGTz/Qj3A3eY99YWCxSEHZ960RKO80ieJnfdubWl0V1vsttG95vWlvtt1vtZXaNHR+s9HaaY2IeG6IgI+bPS2jB3WnpkbLvjX1rdPDMEQO/U10iVhQF39CYOiVZBRWtgN8JTr5yj72rSuxukwv/tuIvL2Iul8d/Qrohx2vCfGh3c1ajhhVWaNy5eI5ObZ4+gTyxcuNBZTmTUqFEtARFjQY/c3FzOAAAAnZienaac4Skqr2lQUmykIiN6nmo6EAiC+FOgwz5AR8a4971LL3KU+5wTJLFgSIPbqaDd/Xo3V99uTyJYEhsZrlOSIrW/vP109/RBEUrqTiFBX7HzFT9Y2YmDFL5nV0s1ZC9mgQAn2w73y4Bsh3t+dpzzepZXXKazy/6tc8v+rsSGw+0fZK/xYy6UTp0rnTLdCXIXVDTozx+UtbyW7D3SoDWbK3T7OSlKjvXj10IAAADJCXykDfLvhhAEQXwR8HBmDMQ1p03Yff/+I3FmNkREucuJivDZBxYnKOINjniXutagic0+6cQVEwbp8S3lqm6eERITGaYFEwd1XETQ1+cwJkmKS2npxGDdr63q8T+3FrR8XstIitFFE4f6dl+BQFZxwK2bEYBi6kt1Y/3zavrq/ym87phUntRR0mlXSBPmua8lbXx4oPa4YKrNiLOi0ZYaCAAAgJNDEKS/OYGDePfDst1a4CNY2ayV8Bh3dkRnvLNGvAGSlvt1GpHWoJ99PUI7iuucDwGnDYlSXJQffPNpQRibpWPn0IoU2nnsIDAze9wQTcpK0hdFlUqKi9SpQxMVHu5nARwgUFQfkapLFXCsdsnmp6RtzzsBnNZXsDC3xsfp/+12dekkuGupgR3+2k62AwAAoGcIgvQ1q83h/bBstycKCIQiK/BnSyczSixENC3TGyDpIAWnZWk84aySXrOaKxa48qYmeWftdDMtKXVQtGaMTuv7/QJCrQ5IALXDddjrlBU7/fhxqaas/WvKqfOknG9Lqdld/prJ6dFOm/BjTRnq5zMGAQDopcraBm34vFBfHKxUYmyUzh0/RFNOsXnWQP8gCNIXLK/bgh7ewIe/pW8EGm/qTVcs/cZbwLXl1lvw1b419bTetmguHOstJGuFZZ1is97FD2aeAKGspQ5IPwQ5+4O9xuzNld5b7bbx9bJC1pMXSDnfcru9dNNp6dG6aHSc0y2roclNC5w7Nl7DEnm7BgAEHytA/adNe1VY7raWP3K0Xs9+kK/rZ2ZrYmb7lFGgrzCq6vWRi5Vik9x87mBOcfFnTsAivHsBEwCBoXx/t+qAfHSgRpsL6pxY5tcyozV9mA9eh6uKpU0rpLy32m8f9w1pxg+kpKxe/dpvjI3X7JGxKjna5BSHjo0ksA4ACE57iitbAiBtv194e3cJQRD0G4IgPZ3xEZvsLgQ+AKBvVZW0TyXpxIY9R/Xa3tb2s3sO16u81qMLRw9Q4VAbne38p/TOw1LboqdZX5Nm3iINPe2k/4n4qHDFJzMzDQAQ3KpqO+4uebS2fcdIoC8RBOlOJxAn8GGdQOL79OADANoUFK0s7PJw1Dd69PaX7b8xMpvyq3Vudqwi+7sYcUWhlPug9NWHrdvi0qSv3yGNuYB0SAAAemDs0ATnvbvhmNZoEzITOY7oNwRBOmLzq6MT3Baolu5CjQ8A6D8Ndc11QLrugFLT4HFaxh7LWmtbZ5V+DYLseV3KXS7VVbVus6Kns25z0yN7oKiq0ZnBkhwbrolDohTO+wwAIAQlxETq6unD9eLm/aq1YliSxg9N0PkT0n29awhiBEHastoS9o2eBT+oMwEAA1MItTTfLWzcDYkx4cpIiNDByvaPH5YY4aSQ9Fvnl3d+J322tnXboKHSef9Hyp7V41/3+t5qvbLnaMt6VmKEvn9GUv/tPwAAfmzaiBRNzEzUvpKjSoqLVFbyAKW3ImQx4jIxCVLKSCl9gpSQTgAEAAaCzfwoy5cajk9vOZErJw5SXFTrjI/4qDAtmJjQDztoScmHpX/c2j4AMu5i6b+f6lUA5HB1ozb8pzUAYgoqGpWb11rjBACAUBMbFeGkwBAAwUCIDO1aHylS/GApMtrXewMAocfqa1gtkB7KTonSz2anauchtzvMhMHRTivZPndol7Tu51JVUWtx7Nm3S6fN73Wa5N4j9R1m/fznCAXgAAAABkLoBUFsEGuBj7jU5harAACfdII5WtLrp1vb2JzMGPWb/PekV5ZIDdWt6S9zf3XSnV9S4yI62c77EQAAwEAInSBIVJw0aIjb6QUA4DvVpVJFgf+egR3/lHIfkjzNdUcyprgBEAugn6TRKZEalRKpvNLWmR+R4dK5I8l/BgAAGAihEQRJzZYGZ/l6LwAAlv5Svt9/j8PW56W3V7Wujz5fmrNMiuybWSdhYWFaOC1J73xZo93N3WFmjYjV8CQ/fDvuo/9nAAAAf+KHo65+ED3I13sAAKg76naC6UYrXJ/Y/Iz0/u9b16f8lzTrVreGVB+y+iUXjo5zFr+eORnfj+2GAQAAfCQ0giAAAN+qr5FK90meJv88E1uebR8AOeMG6axFvS6AGpAs+JEwVIpJdNfryn29RwAAAH2OIAgAoH811EpH8qSm5hob/mbbWum9R1rXz7pZmn6DQjb4AQAAEMQIggAA+jcAcniv1OSnLWB3rpPe+nVoBkAiY93gR2ySr/cEAABgwBAEAQD08wwQPw2AWBvcN5a3rn/t+tAIgFireAt+xKX4ek8AAAAGXLgCwCOPPKJRo0YpNjZWZ599tj744ANf7xIAoKsaIDYDpLHeP49T0XbplSWtbXCnXC3NuFlBLTxSShomDRlPAAQAAIQsvw+CPPfcc7rjjju0bNkyffLJJ8rJydHcuXNVVFTk610DAHTWBeaIH6fAVBZJ634mNVS762MukGbdFrxFUMPC3ZkfQ06V4tOC9/8TAAAgGIIgK1eu1KJFi3TjjTdq0qRJevTRRxUfH681a9b4etcAAMeqrfDvIqiWovPvu6Tqw+565lTpoqV93gbXb1jKi838sCBIuN+/5QMAAPQ7vx4R1dXV6eOPP9bFF1/csi08PNxZf/fdd326bwCAY1QfkUrz/bcNrscj5T4kFe9w1xOHSfMelCJjFJQdX9LGSMnDpYgoX+8NAACA3/DrwqiHDh1SY2OjMjIy2m239R07mgexbdTW1jqLV1lZmXNbXl4+AHsLACGssliqKpZf2/o3adt6935EjPT1e6T6CKm+UkFV9yMhXYpKlWoapJrev/953zs9FjxCn/EeT8YmAAD4Zlzi10GQnlq+fLnuu+++47aPGDHCJ/sDAPBXFdL/fMfXOxEQSkpKlJyc7OvdCKrjaRibAADgm3GJXwdBhgwZooiICB08eLDddlvPzMw87vF33XWXU0TVq7S0VNnZ2crPz2cAFyTRPxs0fvnll0pKSvL17qAPcE6DC+czuNhsypEjRyotLc3XuxJUvMeTsUlw4HUvuHA+gwvnM7iU9eG4xK+DINHR0Zo+fbo2btyoBQsWONuampqc9cWLFx/3+JiYGGc5lkWK+NAcPOxccj6DC+c0uHA+g4vV4kLfH0/GJsGF173gwvkMLpzP4BLeB+MSvw6CGJvZccMNN+jMM8/UjBkztGrVKlVVVTndYgAAAAAAAIImCHLttdequLhYS5cuVWFhoaZNm6b169cfVywVAAAAAAAgoIMgxlJfOkp/6YqlxixbtqzDFBkEHs5n8OGcBhfOZ3DhfHJcwXUSanjdCy6cz+AS04ef7cM89L4DAAAAAAAhgGpnAAAAAAAgJBAEAQAAAAAAIYEgCAAAAAAACAlBHQR55JFHNGrUKMXGxurss8/WBx984OtdAgDAbyxfvlxnnXWWEhMTNXToUC1YsEA7d+709W4FNcYmAAD4dmwStEGQ5557TnfccYdTQfaTTz5RTk6O5s6dq6KiIl/vGk5Cbm6uRowY4dxn8A70j9WrV2vq1KlKSkpylpkzZ2rdunU9/j333XefrrvuOh0+fFg//vGPNWHCBMXFxWnkyJG69dZbVVZW1i/7j569pt5yyy167733tGHDBtXX1+uSSy5RVVUVh7EfMDYJToxNgP7H2CR05A7A2CRogyArV67UokWLdOONN2rSpEl69NFHFR8frzVr1vh613AS/v73v2v+/PnOfQbvgenee+9VWFhYh8sFF1zg692DpOHDh+vBBx/Uxx9/rI8++kgXXXSRrrjiCn322Wc9vl4vv/xyHThwwFlWrFihbdu26YknntD69ev1ve99j+PtY3YeFi5cqMmTJztfFti5yc/Pd869sWuys+vVrmX0DGOT4MTYJPAxNvF/jE1Cx/qBGJt4glBtba0nIiLC88ILL7Tbfv3113suv/xyn+0XOrds2TKP/Tl2tJx//vktjxs7dqxn3bp1Hf6OoqIi5/G5ubkcaj9WUVHhWbRokWfmzJmegoICZ/npT3/qmTx5sqekpMTXu4dOpKamev74xz8612Nn16pdx175+fme6OhoT1lZWYe/769//avz8/r6eo65H/niiy+cc7l161Zn3a5JuzbtGvVer3bt2jVs1zK6j7FJ4GFsEjoYmwQmxiah4Yt+GJtEKggdOnRIjY2NysjIaLfd1nfs2OGz/ULn7rzzTuebYvuWeO3atc42+9bYIoHedfsW2tKZ7Fvpjnin1qelpXGo/VhCQoIzKys6OlqZmZkt2yIjIzl3fsheS59//nlnCqKlxVx55ZU677zzNG/ePOe6NVdddZWmTJnSsm5eeuklJ1Jv6TSdXa/2Mzvv8A9NTU36yU9+otmzZzvn0/t6aufIrlHv9WrXrl3Dtg3dx9gk8DA2CR2MTQILY5PQ0dRPYxNGnwiYNx+bbmp1Xewx3blAAPTe1q1bnaBHTU2Ncy2+8MILTmqh6c4bj12vlkLT2YfBBx54QDfffDOnyI9Y/q0Fot966y1f7wrgFxibAP6FsUnouaWfxiZBWRNkyJAhioiI0MGDB9ttt3XvoB2Bx1tf4EQXyF/+8pcB3y8gGFkR0y1btuj999/Xj370I91www36/PPPu/Xc8vJyp2ZPR9er/eyyyy5zAirUlPAfixcv1ssvv6zXX3/dybtG32NsEpwYmwADh7FJaFncj2OToAyC2LeS06dP18aNG9vNFLB1+2YTgaegoECbN292Pjwdi8F74LNCRvC/19Fx48Y5r6XWickKU/32t7/t1nOtk4wFObydnLwqKiqcNBpreWYzS6Kiovpp79FdHo/HeQ218/Haa69p9OjRXT6H67V3GJsEH8YmwY3XOv/D2CQ0eAZgbBKUQRBj7XEfe+wx/fnPf9b27dudbzItp926xSAwtP1j/sc//qFZs2a1qxnRmwsE/sk+FBcWFmrXrl2+3hV0wgLJtbW13Xrj6SgVxmaAWHszG8BYvZDY2FiOtR+wWXRPP/20nn322Zbr0Jbq6upOn2OPs/patJzvOcYmgY+xSehgbOL/GJsEp1sGYGwStEGQa6+91imsuXTpUk2bNs2Z1m1FNo8tlorAePOxD03HTq3vzQUC/2S1XswZZ5zh612BpLvuuktvvvmm8vLynPxbW3/jjTf0ne98p8s3noaGBmcmSNvr1RsAsUD0n/70J2fde71acTP4zurVq50itVbENisrq2V57rnnOn3ONddco02bNummm24a0H0NBoxNAh9jk9DB2MS/MDYJHasHYmzSzx1tgG677bbb2rXD3bZtmycjI8NpiRQbG+u0R2qrszadjz/+OEcdOAk33XSTJzs722lhm56e7pkzZ47nlVdeafl5Tk5Ou3a4Tz75pCc+Pt5z2WWXeV599VXP8OHD2/2+119/vdPrde/evZwrAH6LsQngHxiboC+F2X/6Lm4D9D1rkbtkyZJuF2UE4Du33nqrMxvk97//PacBQNBibAIEDsYmOBYtchEQLeoeeughX+8GgG6wFtUUoAYQ7BibAIGDsQmOxUwQAAAAAAAQEoK2MCoAAAAAAEBbBEEAAAAAAEBIIAgCAAAAAABCAkEQAAAAAAAQEgiCAAAAAACAkEAQBECvLVy4UAsWLOAIAgAAv8DYBEBXaJELoNfKysrk8XiUkpLCUQQAAD7H2ARAVwiCAAAAAACAkEA6DIAu/e1vf9Ppp5+uuLg4DR48WBdffLGqqqraTTktLi5WZmamfvWrX7U875133lF0dLQ2btzY5b+xZ88eXXHFFcrIyFBCQoLOOussvfrqq+0eM2rUKP3yl7/U9ddf7zwmOztbL730kvNv23Nt29SpU/XRRx9xVgEACGKMTQD0FkEQACdUUFCgb33rW7rpppu0fft2vfHGG7rqqqucNJi20tPTtWbNGt17771OEKKiokLf/e53tXjxYs2ZM6fLo1xZWalLL73UCZhs3rxZ8+bN0/z585Wfn9/ucb/5zW80e/Zs5zGXXXaZ829YUOS6667TJ598orFjxzrrx+4fAAAIDoxNAJwM0mEAnJAFFqZPn668vDxn5kVbNhOktLRUL774Ysu2W265xZnBceaZZ2rr1q368MMPFRMT06ujPGXKFP3whz90AinemSDnnnuunnrqKWe9sLBQWVlZuueee3T//fc729577z3NnDnTGSDZzBQAABBcGJsAOBnMBAFwQjk5Oc5MDkuHueaaa/TYY4/pyJEjnT5+xYoVamho0PPPP69nnnmm2wEQmwly55136rTTTnMKrVpqi808OXYmiKW7eFnqjLF9O3ZbUVERZxYAgCDE2ATAySAIAuCEIiIitGHDBq1bt06TJk3Sww8/rAkTJmjv3r2d1vY4cOCAmpqanNkj3WUBkBdeeMGpKbJp0yZt2bLFCW7U1dW1e1xUVFTL/bCwsE632b8PAACCD2MTACcj8qSeDSAkWGDB6nDYsnTpUictxgIWx7KAhdXmuPbaa51Ayfe//30nJWbo0KFd/htvv/22k15z5ZVXtswM6UkQBQAAhA7GJgB6iyAIgBN6//33nWKll1xyiRPMsHXrxmJpK59++mm7x959990qKyvT7373Oyed5V//+pdTUPXll1/u8iiPHz9ea9eudYqh2sDG6nwwmwMAADA2AdCXSIcBcEJJSUl68803nc4tp556qpYsWaJf//rX+uY3v9nucdY1ZtWqVU7RUntOeHi4c99SW1avXt3lUV65cqVSU1M1a9YsJxAyd+5cnXHGGZwdAADA2ARAn6E7DAAAAAAACAnMBAEAAAAAACGBIAiAATF58mSnTkhHi7XSBQAAGEiMTYDQRDoMgAGxb98+1dfXd/izjIwMJSYmciYAAMCAYWwChCaCIAAAAAAAICSQDgMAAAAAAEICQRAAAAAAABASCIIAAAAAAICQQBAEAAAAAACEBIIgAAAAAAAgJBAEAQAAAAAAIYEgCAAAAAAACAkEQQAAAAAAgELB/wcXFSTWp0D8EwAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1100x500 with 2 Axes>"
       ]
@@ -748,21 +1165,23 @@
     }
    ],
    "source": [
-    "cc = CCRegression(formula=\"theta ~ psi\", data=df_rad)\n",
+    "# Pewsey §8.6 orientation: noon ~ 6am. Reproduces the book exactly —\n",
+    "# rho = 0.502, higher-order p = (0.0997, 0.9475): no reason to go beyond m = 1.\n",
+    "cc = CCRegression(formula=\"noon ~ six_am\", data=df_wind)\n",
     "cc.plot()\n",
     "cc.summary()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "A.k=0.45009345605461604; kappa=1.00676201668104\n"
+      "A.k=0.5063665257691505; kappa=1.1703113815294874\n"
      ]
     }
    ],
@@ -779,45 +1198,108 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`CCRegression` is a direct port from R/circular's `lm.circular.cc`, so the result is expected to be the same:\n",
+    "`CCRegression` is a direct port from R/circular's `lm.circular.cc`, so the result is expected to be the same — and `rho` and the higher-order `p.values` also match the values reported in Pewsey §8.6 (0.502 and (0.0997, 0.9475)):\n",
     "\n",
     "```R\n",
     "$coefficients\n",
-    "                  [,1]       [,2]\n",
-    "(Intercept) 0.03765164 -0.2377535\n",
-    "cos.x       0.47746346 -0.2890068\n",
-    "sin.x       0.26616458  0.2311712\n",
+    "                   [,1]      [,2]\n",
+    "(Intercept)  0.01441183 0.1145101\n",
+    "cos.x        0.33465348 0.5494121\n",
+    "sin.x       -0.07478683 0.4821004\n",
     "\n",
-    "$p.values[:]\n",
-    "          [,1]      [,2]\n",
-    "[1,] 0.1518748 0.2065554\n",
+    "$p.values\n",
+    "           [,1]      [,2]\n",
+    "[1,] 0.09971087 0.9474847\n",
     "\n",
     "$A.k\n",
-    "[1] 0.4500935\n",
+    "[1] 0.5063665\n",
     "\n",
     "$kappa\n",
-    "[1] 1.006762\n",
+    "[1] 1.170311\n",
     "\n",
     "$call\n",
-    "lm.circular.cc(y = ..1, x = ..2, order = 1)\n",
+    "lm.circular(type = \"c-c\", y = noon, x = six_am, order = 1)\n",
     "\n",
     "$message\n",
     "[1] \"Higher order terms are not significant at the 0.05 level\"\n",
     "\n",
     "attr(,\"class\")\n",
-    "[1] \"lm.circular.cc\""
+    "[1] \"lm.circular.cc\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Nonparametric CC via a cyclic smooth\n",
+    "\n",
+    "The same backend dispatch applies to `CCRegression`: a smooth term fits **two\n",
+    "cyclic-smooth `gam`s** on the cos/sin embedding of the response and reassembles\n",
+    "the mean direction via `arctan2` — the nonparametric counterpart of the\n",
+    "harmonic fit above. The cyclic period defaults to `[0, 2π]` (override via\n",
+    "`knots=`); `ρ`, the residual `κ̂`, and a single `plot()` / `summary()` carry\n",
+    "over from the parametric backend.\n",
+    "\n",
+    "One caveat when reading the two summaries side by side: `rho` measures the\n",
+    "*amplitude* of the fitted cos/sin embedding, which the smoothing penalty\n",
+    "deliberately shrinks toward a constant — so the gam's `rho` (≈ 0.32 here) is\n",
+    "not comparable with the parametric one (≈ 0.50). Compare the angular fit via\n",
+    "the residual `κ̂` instead (≈ 1.16 vs 1.17 — essentially identical)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Circular-Circular Regression (smooth / gam backend)\n",
+      "\n",
+      "Circular Correlation Coefficient (rho): 0.32035\n",
+      "Mean Residual Cosine (A_k):             0.50309\n",
+      "Residual Concentration (kappa):         1.16037\n",
+      "\n",
+      "Per-coordinate smooths:\n",
+      "\n",
+      "  cos(θ):  s(six_am) edf=1.33;  deviance explained 19.9%\n",
+      "  sin(θ):  s(six_am) edf=1.56;  deviance explained 23.4%\n",
+      "\n",
+      "Smooth (gam) backend: the higher-order harmonic test does not apply; smoothness is selected by REML/GCV per coordinate.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cc_gam = CCRegression(\"noon ~ s(six_am, bs='cc')\", df_wind)\n",
+    "cc_gam.plot()\n",
+    "cc_gam.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: 2026-06-08 17:39:06 CEST\n",
+      "Last updated: 2026-06-10 19:29:48 CEST\n",
       "\n",
       "Python implementation: CPython\n",
       "Python version       : 3.13.11\n",
@@ -825,7 +1307,7 @@
       "\n",
       "matplotlib : 3.10.9\n",
       "numpy      : 2.4.4\n",
-      "pandas     : 3.0.2\n",
+      "polars     : 1.41.2\n",
       "pycircstat2: 0.1.16\n",
       "\n",
       "Watermark: 2.6.0\n",
diff --git a/examples/T5-clustering.ipynb b/examples/T5-clustering.ipynb
index e44b855..4dbdf70 100644
--- a/examples/T5-clustering.ipynb
+++ b/examples/T5-clustering.ipynb
@@ -42,7 +42,7 @@
     {
      "data": {
       "text/plain": [
-       "<pycircstat2.clustering.CircKMeans at 0x12063c200>"
+       "<pycircstat2.clustering.CircKMeans at 0x11cef6cf0>"
       ]
      },
      "execution_count": 2,
@@ -51,7 +51,7 @@
     }
    ],
    "source": [
-    "d = load_data(\"B3\", source=\"fisher\")[\"θ\"].values[:]\n",
+    "d = load_data(\"B3\", source=\"fisher\")[\"θ\"].to_numpy()\n",
     "\n",
     "movm = MovM(n_clusters=2, unit=\"degree\", random_seed=2046)\n",
     "movm.fit(d)\n",
@@ -206,7 +206,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x800 with 7 Axes>"
       ]
@@ -307,7 +307,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 500x300 with 1 Axes>"
       ]
@@ -387,18 +387,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: 2025-11-03 14:19:23CET\n",
+      "Last updated: 2026-06-09 14:24:41 CEST\n",
       "\n",
       "Python implementation: CPython\n",
-      "Python version       : 3.12.12\n",
-      "IPython version      : 9.6.0\n",
+      "Python version       : 3.13.11\n",
+      "IPython version      : 9.13.0\n",
       "\n",
-      "pandas     : 2.3.3\n",
-      "numpy      : 2.3.4\n",
-      "matplotlib : 3.10.7\n",
-      "pycircstat2: 0.1.15\n",
+      "matplotlib : 3.10.9\n",
+      "numpy      : 2.4.4\n",
+      "pandas     : 3.0.2\n",
+      "pycircstat2: 0.1.16\n",
       "\n",
-      "Watermark: 2.5.0\n",
+      "Watermark: 2.6.0\n",
       "\n"
      ]
     }
@@ -411,7 +411,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv (3.12.12)",
+   "display_name": "pycircstat2 (3.13.11)",
    "language": "python",
    "name": "python3"
   },
@@ -425,7 +425,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.12"
+   "version": "3.13.11"
   }
  },
  "nbformat": 4,
diff --git a/pycircstat2/base.py b/pycircstat2/base.py
index d06c9e3..2e10265 100644
--- a/pycircstat2/base.py
+++ b/pycircstat2/base.py
@@ -459,7 +459,7 @@ def plot(self, ax=None, config=None):
         ```
         from pycircstat2 import load_data, Circular
 
-        data = load_data("B3", source="fisher")["θ"].values
+        data = load_data("B3", source="fisher")["θ"].to_numpy()
         c = Circular(data, unit="degree")
         c.plot(config={"scatter": {"color" : "blue", "size": 15}})
         ```
diff --git a/pycircstat2/data/pewsey/lung_deaths.csv-metadata.json b/pycircstat2/data/pewsey/lung_deaths.csv-metadata.json
index 6c9be99..597c4f5 100644
--- a/pycircstat2/data/pewsey/lung_deaths.csv-metadata.json
+++ b/pycircstat2/data/pewsey/lung_deaths.csv-metadata.json
@@ -1,7 +1,7 @@
 {
-    "Description": "Monthly numbers of deaths attributed to lung disease in a given area, recorded over six consecutive years (1974-1979). Used in Pewsey, Neuhäuser & Ruxton (2014) to illustrate linear-circular regression with a periodic (annual) seasonal component. The February 1976 (3891) and February 1979 (3535) observations are typically treated as outliers and dropped before fitting the cosine regression model in §8.4.1-§8.4.2.",
+    "Description": "Monthly numbers of deaths attributed to lung disease in a given area, recorded over six consecutive years (1974-1979). Used in Pewsey, Neuhäuser & Ruxton (2013) to illustrate linear-circular regression with a periodic (annual) seasonal component. The February 1976 (3891) and February 1979 (3535) observations are typically treated as outliers and dropped before fitting the cosine regression model in §8.4.1-§8.4.2.",
     "Source": "Hand et al. (1994) — A Handbook of Small Data Sets, Chapman & Hall.",
-    "Examples": "Pewsey, Neuhäuser & Ruxton (2014) §8.4.1-§8.4.4",
+    "Examples": "Pewsey, Neuhäuser & Ruxton (2013) §8.4.1-§8.4.4",
     "Columns": {
         "year": {
             "name": "year",
diff --git a/pycircstat2/data/pewsey/wind.csv-metadata.json b/pycircstat2/data/pewsey/wind.csv-metadata.json
index 2d128d7..13dbce1 100644
--- a/pycircstat2/data/pewsey/wind.csv-metadata.json
+++ b/pycircstat2/data/pewsey/wind.csv-metadata.json
@@ -1,7 +1,7 @@
 {
     "Description": "In a place named 'Col de la Roa' in the Italian Alps there is a meteorological station that records via data-logger several parameters. Measures are made every 15 minutes, in this dataset we report the wind direction recorded every day from January 29, 2001 to March 31, 2001 from 3.00am to 4.00am included. Which means 5 observations every day for a total of 310 measures.",
     "Source": "Agostinelli, 2007",
-    "Examples": "Pewsey et al. (2014) Ch2.2",
+    "Examples": "Pewsey et al. (2013) Ch2.2",
     "Columns": {
         "x": {
             "name": "x",
diff --git a/pycircstat2/descriptive.py b/pycircstat2/descriptive.py
index 98054aa..d7a637b 100644
--- a/pycircstat2/descriptive.py
+++ b/pycircstat2/descriptive.py
@@ -302,7 +302,7 @@ def circ_skewness(alpha: np.ndarray, w: Optional[np.ndarray] = None) -> float:
 
     $$\hat s = [\hat\rho_2 \sin(\hat\mu_2 - 2 \hat\mu_1)] / (1 - \hat\rho_1)^{\frac{3}{2}}$$
 
-    But unlike the implementation of Fisher (1993), here we followed Pewsey et al. (2014) by NOT centering the second moment.
+    But unlike the implementation of Fisher (1993), here we followed Pewsey et al. (2013) by NOT centering the second moment.
 
     Parameters
     ----------
@@ -338,7 +338,7 @@ def circ_kurtosis(alpha: np.ndarray, w: Optional[np.ndarray] = None) -> float:
 
     $$\hat k = [\hat\rho_2 \cos(\hat\mu_2 - 2 \hat\mu_1) - \hat\rho_1^4] / (1 - \hat\rho_1)^{2}$$
 
-    But unlike the implementation of Fisher (1993), here we followed Pewsey et al. (2014) by **NOT** centering the second moment.
+    But unlike the implementation of Fisher (1993), here we followed Pewsey et al. (2013) by **NOT** centering the second moment.
 
     Parameters
     ----------
diff --git a/pycircstat2/distributions.py b/pycircstat2/distributions.py
index cc570e3..a428a8b 100644
--- a/pycircstat2/distributions.py
+++ b/pycircstat2/distributions.py
@@ -1,7 +1,8 @@
 import types
+from functools import lru_cache
 
 import numpy as np
-from scipy.integrate import quad, quad_vec
+from scipy.integrate import quad
 from scipy.interpolate import PchipInterpolator
 from scipy.optimize import minimize, minimize_scalar, brentq, root_scalar
 from scipy.special import beta as beta_fn
@@ -10,23 +11,34 @@
     i0,
     i0e,
     i1,
+    i1e,
     ndtr,
     ndtri,
     iv,
+    ive,
     betainc,
     betaincinv,
     gammaln,
     digamma,
-    lpmv,
+    expit,
+    log_ndtr,
     logsumexp,
+    owens_t,
+    polygamma,
+    roots_legendre,
 )
+from hea.family import IdentityLink, Link, LogitLink, LogLink
 from scipy.stats import rv_continuous
 from scipy.stats._distn_infrastructure import rv_continuous_frozen
 
 from .descriptive import circ_kappa, circ_mean_and_r
-from .utils import angmod
+from .utils import A1, A1inv, A1prime, A1prime2, A1prime3, angmod
 
 __all__ = [
+    "TanHalfLink",
+    "TanhLink",
+    "LogitHalfLink",
+    "get_link",
     "circularuniform",
     "triangular",
     "cardioid",
@@ -34,6 +46,7 @@
     "wrapnorm",
     "wrapcauchy",
     "vonmises",
+    "projectednormal",
     "vonmises_flattopped",
     "jonespewsey",
     "jonespewsey_sineskewed",
@@ -51,7 +64,6 @@
 _VMFT_GRID_SHARPNESS = 12.0
 _VMFT_KAPPA_TOL = 1e-9
 _VMFT_KAPPA_UPPER = 1e3
-_VMFT_ENV_MIN_KAPPA = 1e-6
 _VMFT_ACCEPT_EPS = 1e-12
 _VMFT_NEWTON_MAXITER = 50
 _VMFT_NEWTON_TOL = 1e-12
@@ -102,6 +114,262 @@
 ]
 
 
+class _RegressionReady:
+    """Regression overlay for a circular distribution (read by the regression
+    engine only; descriptive use never sees it).
+
+    A distribution opts in by composing this mixin and declaring two class
+    dicts in **book-named** parameters:
+
+    - ``param_roles``  : ``{book_name -> role}``  (e.g. ``{"mu": "location"}``)
+    - ``default_links`` : ``{role -> link_name}`` (e.g. ``{"location": "tanhalf"}``,
+      resolved to a link object by :func:`get_link` below)
+
+    The mixin derives the inverse views for free — nothing is renamed; values
+    always flow in the distribution's own book-named dicts. Roles exist only so
+    a generic engine can find the mean-direction vs concentration vs shape
+    parameters and attach each one's default link. See the Phase 1 contract in
+    ``dev/plans/circular_gam_integration.md``.
+
+    Derivatives (optional, tiered) keep book names too:
+
+    - ``dlogpdf(x, **params) -> {name: ∂logpdf/∂name}``                  (l1)
+    - ``d2logpdf(x, **params) -> {(name_i, name_j): ∂²logpdf/∂i∂j}``     (l2)
+
+    only the unique unordered pairs are stored in ``d2logpdf``.
+    """
+
+    param_roles: dict = {}
+    default_links: dict = {}
+
+    @classmethod
+    def params_by_role(cls) -> dict:
+        """``{role -> [book_name, ...]}`` — one-to-many (e.g. several shape
+        params), insertion-ordered by ``param_roles``."""
+        out: dict = {}
+        for name, role in cls.param_roles.items():
+            out.setdefault(role, []).append(name)
+        return out
+
+    @classmethod
+    def link_for(cls, name: str) -> str:
+        """Default link name for a parameter, via its role."""
+        return cls.default_links[cls.param_roles[name]]
+
+
+# --- links (the contract's other half: resolving ``default_links`` names) ----
+# A link maps a parameter onto the unconstrained linear-predictor scale,
+# η = g(param). Every link resolved here is a ``hea.family.Link``, so one
+# authored object serves both regression consumers (plan §3.1): the parametric
+# ``CLRegression`` reads ``linkinv`` (η → parameter) and ``mu_eta`` (∂param/∂η)
+# for Fisher scoring; the hea general-family bridge (Phase 2) hands the same
+# object to ``hea.gam``, whose ``gamlss_etamu`` chain rule additionally
+# consumes ``link`` (g) and ``d2link``/``d3link``/``d4link`` (g″, g‴, g⁗ —
+# derivatives w.r.t. the *parameter*, mgcv convention). hea's catalog already
+# carries those hooks for log/logit/identity, so only the circular-specific
+# tan-half link — which hea must not learn — is authored here.
+
+
+class TanHalfLink(Link):
+    r"""Fisher–Lee tan-half link for a circular location parameter.
+
+    $$\eta = g(\mu) = \tan(\mu/2), \qquad
+      \mu = g^{-1}(\eta) = 2\arctan(\eta) \in (-\pi, \pi).$$
+
+    Maps the principal angle branch monotonically onto ℝ; a CL model offsets
+    it by the intercept direction, ``μ_i = μ₀ + 2 arctan(x_iᵀβ)`` (Fisher &
+    Lee 1992). ``tan(μ/2)`` is 2π-periodic in μ, so ``link`` returns the
+    principal-branch η for any angle parameterization; the only singularity
+    is ``μ ≡ π (mod 2π)``, the antipode of the offset.
+
+    Derivatives w.r.t. μ, in ``t = tan(μ/2)`` (so ``dt/dμ = (1+t²)/2``):
+
+    $$g' = \tfrac{1+t^2}{2},\quad g'' = \tfrac{t(1+t^2)}{2},\quad
+      g''' = \tfrac{(1+t^2)(1+3t^2)}{4},\quad
+      g'''' = \tfrac{t(1+t^2)(2+3t^2)}{2}.$$
+    """
+
+    name = "tanhalf"
+
+    def link(self, mu):
+        return np.tan(0.5 * np.asarray(mu, dtype=float))
+
+    def linkinv(self, eta):
+        return 2.0 * np.arctan(np.asarray(eta, dtype=float))
+
+    def mu_eta(self, eta):
+        eta = np.asarray(eta, dtype=float)
+        return 2.0 / (1.0 + eta * eta)
+
+    def d2link(self, mu):
+        t = np.tan(0.5 * np.asarray(mu, dtype=float))
+        return 0.5 * t * (1.0 + t * t)
+
+    def d3link(self, mu):
+        t2 = np.tan(0.5 * np.asarray(mu, dtype=float)) ** 2
+        return 0.25 * (1.0 + t2) * (1.0 + 3.0 * t2)
+
+    def d4link(self, mu):
+        t = np.tan(0.5 * np.asarray(mu, dtype=float))
+        t2 = t * t
+        return 0.5 * t * (1.0 + t2) * (2.0 + 3.0 * t2)
+
+
+class TanhLink(Link):
+    r"""Tanh link for a (−1, 1)-bounded shape parameter (the sine-skew λ).
+
+    $$\eta = g(\lambda) = \operatorname{atanh}(\lambda), \qquad
+      \lambda = \tanh(\eta) \in (-1, 1).$$
+
+    Derivatives w.r.t. λ:
+
+    $$g' = \frac{1}{1-\lambda^2},\quad g'' = \frac{2\lambda}{(1-\lambda^2)^2},
+      \quad g''' = \frac{2+6\lambda^2}{(1-\lambda^2)^3},\quad
+      g'''' = \frac{24\lambda(1+\lambda^2)}{(1-\lambda^2)^4}.$$
+    """
+
+    name = "tanh"
+
+    def link(self, mu):
+        return np.arctanh(np.asarray(mu, dtype=float))
+
+    def linkinv(self, eta):
+        # clamped inside the open interval (mgcv convention) — at λ = ±1 a
+        # sine-skewed density touches 0 and its logpdf has no finite limit
+        eps = np.finfo(float).eps
+        return np.clip(np.tanh(np.asarray(eta, dtype=float)),
+                       -1.0 + eps, 1.0 - eps)
+
+    def mu_eta(self, eta):
+        # sech²η = 4e^{-2|η|}/(1+e^{-2|η|})², overflow-safe; floored at eps
+        a = np.exp(-2.0 * np.abs(np.asarray(eta, dtype=float)))
+        return np.maximum(4.0 * a / (1.0 + a) ** 2, np.finfo(float).eps)
+
+    def d2link(self, mu):
+        mu = np.asarray(mu, dtype=float)
+        return 2.0 * mu / (1.0 - mu * mu) ** 2
+
+    def d3link(self, mu):
+        mu = np.asarray(mu, dtype=float)
+        return (2.0 + 6.0 * mu * mu) / (1.0 - mu * mu) ** 3
+
+    def d4link(self, mu):
+        mu = np.asarray(mu, dtype=float)
+        return 24.0 * mu * (1.0 + mu * mu) / (1.0 - mu * mu) ** 4
+
+
+class LogitHalfLink(Link):
+    r"""Scaled logit for a (0, ½)-bounded concentration (the cardioid ρ).
+
+    $$\eta = g(\rho) = \log\frac{\rho}{\tfrac12-\rho}, \qquad
+      \rho = \tfrac12\,\operatorname{expit}(\eta) \in (0, \tfrac12).$$
+
+    The general scaled logit on (0, c): a subclass overriding ``hi`` serves
+    any upper bound c (the link inventory of the regression plan).
+    """
+
+    name = "logit_half"
+    hi = 0.5
+
+    def link(self, mu):
+        mu = np.asarray(mu, dtype=float)
+        return np.log(mu / (self.hi - mu))
+
+    def linkinv(self, eta):
+        # clamped to (hi·eps, hi·(1−eps)) — at ρ = ½ the cardioid density
+        # touches 0 at the antimode (logpdf → −∞ there)
+        eps = np.finfo(float).eps
+        return self.hi * np.clip(expit(np.asarray(eta, dtype=float)),
+                                 eps, 1.0 - eps)
+
+    def mu_eta(self, eta):
+        # hi·e^{-|η|}/(1+e^{-|η|})², overflow-safe; floored at eps
+        a = np.exp(-np.abs(np.asarray(eta, dtype=float)))
+        return np.maximum(self.hi * a / (1.0 + a) ** 2, np.finfo(float).eps)
+
+    def d2link(self, mu):
+        mu = np.asarray(mu, dtype=float)
+        return 1.0 / (self.hi - mu) ** 2 - 1.0 / mu**2
+
+    def d3link(self, mu):
+        mu = np.asarray(mu, dtype=float)
+        return 2.0 / (self.hi - mu) ** 3 + 2.0 / mu**3
+
+    def d4link(self, mu):
+        mu = np.asarray(mu, dtype=float)
+        return 6.0 / (self.hi - mu) ** 4 - 6.0 / mu**4
+
+
+# Names a distribution may declare in ``default_links``. tanhalf, tanh and
+# logit_half are ours (circular/bounded-shape knowledge hea must not learn);
+# the rest resolve to hea's implementations (log for κ > 0, logit for a
+# (0, 1)-bounded concentration such as the wrapped Cauchy ρ, identity for
+# unconstrained shape parameters).
+_LINKS = {
+    "tanhalf": TanHalfLink,
+    "log": LogLink,
+    "logit": LogitLink,
+    "identity": IdentityLink,
+    "tanh": TanhLink,
+    "logit_half": LogitHalfLink,
+}
+
+
+def get_link(link: "str | Link") -> Link:
+    """Resolve a link declared by the regression overlay to a link object.
+
+    Accepts a name from ``default_links`` (e.g. ``vonmises.link_for("mu")``)
+    or an already-constructed ``hea.family.Link``, which passes through so a
+    custom link can be handed anywhere a name is accepted.
+    """
+    if isinstance(link, Link):
+        return link
+    if isinstance(link, str) and link in _LINKS:
+        return _LINKS[link]()
+    raise ValueError(f"unknown link {link!r}; available: {sorted(_LINKS)}")
+
+
+def _inverse_cdf_knots(phi, cumulative, min_step=1e-14):
+    """Strictly-increasing (cdf, angle) knots for an inverse-cdf
+    interpolant, greedily thinned to steps ≥ ``min_step``: in dead tails a
+    tabulated cdf grows by denormal amounts per node (the pdf is floored at
+    ``np.finfo(float).tiny``), so dφ/dq overflows and Pchip's derivative
+    screen rejects the work arrays ("``dydx`` must contain only finite
+    values" — the κ ≳ 200 vmft crash of the methods-parity P1 review, and
+    its inverse-Batschelet twin at κ ≳ 400). Quantile accuracy is
+    unaffected for any q the thinned knots can distinguish; the forced
+    q = 1 endpoint keeps the domain closed (its gap is ≥ ~1e-16, because
+    doubles within eps of 1 collapse to 1). Returns ``(values, angles)``,
+    or ``None`` when fewer than two knots survive."""
+    unique_vals, unique_idx = np.unique(cumulative, return_index=True)
+    if unique_vals.size < 2:
+        return None
+    sel = [0]
+    last = unique_vals[0]
+    for i in range(1, unique_vals.size):
+        if unique_vals[i] - last >= min_step:
+            sel.append(i)
+            last = unique_vals[i]
+    if sel[-1] != unique_vals.size - 1:
+        sel.append(unique_vals.size - 1)
+    keep = np.asarray(sel, dtype=int)
+    return unique_vals[keep], np.asarray(phi, dtype=float)[unique_idx[keep]]
+
+
+def _as_scalar_param(value, dist_name):
+    """Collapse a shape parameter to a float, tolerating arrays that repeat a
+    single value — scipy's ``cdf`` machinery broadcasts shape parameters to
+    the shape of ``x`` before ``_cdf`` is called, so a scalar parameter can
+    arrive as a constant array."""
+    arr = np.asarray(value, dtype=float)
+    if arr.size == 1:
+        return float(arr.reshape(-1)[0])
+    first = float(arr.flat[0])
+    if np.all((arr == first) | (np.isnan(arr) & np.isnan(first))):
+        return first
+    raise ValueError(f"{dist_name} parameters must be scalar-valued.")
+
+
 class CircularContinuous(rv_continuous):
     """
     Base class for circular distributions with fixed loc=0 and scale=1.
@@ -615,7 +883,14 @@ def r(self, *args, **kwargs) -> float:
         return float(np.clip(abs(m1), 0.0, 1.0))
 
     def mean(self, *args, **kwargs) -> float:
-        """Circular mean direction μ = arg(m₁)."""
+        """Circular mean direction μ = arg(m₁).
+
+        Returns ``nan`` when the mean resultant length R = |m₁| is ≈ 0
+        (within 1e-12): for an isotropic law — the circular uniform, or
+        any family at its uniform limit — every direction is equally
+        central, so no mean direction exists. This is the standard
+        circular-statistics convention, not an error.
+        """
         m1 = self.trig_moment(1, *args, **kwargs)
         R = np.clip(abs(m1), 0.0, 1.0)
         if np.isclose(R, 0.0, atol=1e-12):
@@ -762,13 +1037,16 @@ class circularuniform_gen(CircularContinuous):
     pdf(x)
         Probability density function.
 
+    logpdf(x)
+        Logarithm of the probability density function.
+
     cdf(x)
         Cumulative distribution function.
 
     ppf(q)
         Percent-point function (inverse of CDF).
 
-    rvs(size, random_state) 
+    rvs(size, random_state)
         Random variates.
     """
 
@@ -795,6 +1073,26 @@ def pdf(self, x, *args, **kwargs):
         """
         return super().pdf(x, *args, **kwargs)
 
+    def _logpdf(self, x):
+        return np.full_like(np.asarray(x, dtype=float), -np.log(2.0 * np.pi))
+
+    def logpdf(self, x, *args, **kwargs):
+        r"""
+        Logarithm of the probability density function of the Circular
+        Uniform distribution: the constant $-\log 2\pi$.
+
+        Parameters
+        ----------
+        x : array_like
+            Points at which to evaluate the log-density.
+
+        Returns
+        -------
+        logpdf_values : array_like
+            Logarithm of the probability density function evaluated at `x`.
+        """
+        return super().logpdf(x, *args, **kwargs)
+
     def _cdf(self, x):
         return x / (2 * np.pi)
 
@@ -889,16 +1187,19 @@ class triangular_gen(CircularContinuous):
     pdf(x, rho)
         Probability density function.
 
+    logpdf(x, rho)
+        Logarithm of the probability density function.
+
     cdf(x, rho)
         Cumulative distribution function.
 
     ppf(q, rho)
         Closed-form quantile (inverse CDF).
-        
+
     rvs(rho, size=None, random_state=None)
         Random variates via inverse-transform using the closed-form quantile.
 
-    fit(data)
+    fit(data, *, weights=None, method="mle" | "moments", ...)
         Fit the distribution to the data and return the parameter (rho).
 
     Notes
@@ -938,6 +1239,55 @@ def pdf(self, x, rho, *args, **kwargs):
 
         return super().pdf(x, rho, *args, **kwargs)
 
+    def _logpdf(self, x, rho):
+        # the piecewise-linear density never underflows, so the log of the
+        # closed form is already the stable log-density (kinks unchanged);
+        # −inf only at the honest zero (x = π at ρ = 4/π²)
+        with np.errstate(divide="ignore"):
+            return np.log(self._pdf(x, rho))
+
+    def logpdf(self, x, rho, *args, **kwargs):
+        r"""
+        Logarithm of the probability density function of the Triangular
+        distribution: the log of the closed form, $-\infty$ only where the
+        density is genuinely zero (the antipode at $\rho = 4/\pi^2$).
+
+        Parameters
+        ----------
+        x : array_like
+            Points at which to evaluate the log-density.
+        rho : float
+            Concentration parameter, 0 <= rho <= 4/pi^2.
+
+        Returns
+        -------
+        logpdf_values : array_like
+            Logarithm of the probability density function evaluated at `x`.
+        """
+        return super().logpdf(x, rho, *args, **kwargs)
+
+    def trig_moment(self, p: int = 1, *args, **kwargs) -> complex:
+        """Closed-form trigonometric moment (J&S §2.2.3): the mode-at-0
+        triangular law has m_p = ρ/p² for odd p and 0 for even p (real —
+        the density is symmetric about 0)."""
+        shape_args, non_shape_kwargs = self._separate_shape_parameters(
+            args, kwargs, "trig_moment"
+        )
+        call_kwargs = self._prepare_call_kwargs(non_shape_kwargs, "trig_moment")
+        (rho,) = (float(np.asarray(v, dtype=float))
+                  for v in self._parse_args(*shape_args, **call_kwargs)[0])
+
+        if not np.isscalar(p):
+            raise ValueError("`p` must be an integer scalar.")
+        if int(round(p)) != p:
+            raise ValueError("`p` must be an integer.")
+        k = abs(int(round(p)))
+        if k == 0:
+            return complex(1.0, 0.0)
+        if k % 2 == 0:
+            return complex(0.0, 0.0)
+        return complex(rho / (k * k), 0.0)
+
     def _cdf(self, x, rho):
         x_arr = np.asarray(x, dtype=float)
         rho_arr = np.asarray(rho, dtype=float)
@@ -1151,14 +1501,15 @@ def rvs(self, rho=None, size=None, random_state=None):
             raise ValueError("'rho' must be provided.")
         return self._rvs(rho_val, size=size, random_state=random_state)
 
-    def fit(self, data, *, weights=None, method="mle", return_info=False):
+    def fit(self, data, *, weights=None, method="mle", return_info=False,
+            optimizer=None):
         r"""
         Estimate the concentration parameter $\rho$ of the circular triangular law on $[0,2\pi)$.
 
         Methods
         -------
 
-        mle (default): 
+        mle (default):
             maximize the log-likelihood. This solves the 1-D score equation
             $\sum_i \frac{c_i}{4+\rho\,c_i}=0$ with $c_i = 2\pi\,|\,\pi-x_i\,| - \pi^2$.
             Unique solution in $[0, 4/\pi^2)$ or at a boundary.
@@ -1175,6 +1526,11 @@ def fit(self, data, *, weights=None, method="mle", return_info=False):
             Estimation method (see above).
         return_info : bool, optional
             If True, also return a dict with diagnostics (loglik, se, n_effective, method).
+        optimizer : str, optional
+            Accepted for cross-family ``fit`` signature uniformity and
+            ignored: the MLE is the exact root of a strictly monotone 1-D
+            score equation (bracketed Brent), so there is no optimizer to
+            choose.
 
         Returns
         -------
@@ -1191,6 +1547,7 @@ def fit(self, data, *, weights=None, method="mle", return_info=False):
         The MLE solves a strictly monotone score equation, so bracketing root-finding
         is robust and $O(n)$ per evaluation.
         """
+        del optimizer  # signature uniformity only — exact 1-D root inside
         x = np.asarray(data, dtype=float)
         x = np.mod(x, 2*np.pi)
 
@@ -1259,14 +1616,14 @@ def score(rho):
         se = (1.0 / np.sqrt(info_obs)) if info_obs > 0 else np.nan
 
         if return_info:
-            return rho_hat, {"loglik": ll, "se": se, "n_effective": n_eff, "method": "mle", "converged": converged}
+            return rho_hat, {"loglik": ll, "se": se, "n_effective": n_eff, "method": "mle", "converged": converged, "optimizer": "brentq"}
         return rho_hat
 
 
 triangular = triangular_gen(name="triangular")
 
 
-class cardioid_gen(CircularContinuous):
+class cardioid_gen(_RegressionReady, CircularContinuous):
     r"""Cardioid (cosine) Distribution
 
     ![cardioid](../images/circ-mod-cardioid.png)
@@ -1282,6 +1639,8 @@ class cardioid_gen(CircularContinuous):
     -------
     pdf(x, mu, rho)
         Probability density function.
+    logpdf(x, mu, rho)
+        Logarithm of the probability density function.
     cdf(x, mu, rho)
         Cumulative distribution function.
     ppf(q, mu, rho)
@@ -1293,9 +1652,66 @@ class cardioid_gen(CircularContinuous):
 
     Notes
     -----
-    Implementation based on Section 4.3.4 of Pewsey et al. (2014).
+    Implementation based on Section 4.3.4 of Pewsey et al. (2013).
     """
 
+    # --- regression overlay (Phase 1 contract; read by the regression engine
+    # only). Book names mu/rho are preserved; ρ is the mean resultant length,
+    # bounded in (0, ½), so its default link is the scaled logit. ---
+    param_roles = {"mu": "location", "rho": "concentration"}
+    default_links = {"location": "tanhalf", "concentration": "logit_half"}
+
+    # The log-density is ℓ = log P − log 2π with P = 1 + 2ρ cos(θ−μ): the
+    # same −log-quadratic pattern as the wrapped Cauchy's −log D, but P is
+    # *linear* in ρ (P_ρρ = 0), so the derivative table below is even
+    # sparser. All pure numpy, broadcasting over per-observation parameter
+    # arrays. Likelihood hazard documented in the regression plan: at
+    # ρ → ½ the density touches 0 at the antimode (θ−μ = π), where ℓ and
+    # every derivative diverge — the logit_half link keeps ρ interior, but
+    # data at the antimode still produce −∞/large scores.
+
+    def dlogpdf(self, x, mu, rho):
+        r"""First derivatives of ``logpdf`` w.r.t. the parameters (l1).
+
+        $$\frac{\partial\ell}{\partial\mu} = \frac{2\rho\sin(\theta-\mu)}{P},
+          \qquad
+          \frac{\partial\ell}{\partial\rho} = \frac{2\cos(\theta-\mu)}{P},
+          \qquad P = 1 + 2\rho\cos(\theta-\mu).$$
+
+        Vectorizes over per-observation ``mu``/``rho`` arrays. Returns a
+        book-named dict ``{"mu": …, "rho": …}``.
+        """
+        x = np.asarray(x, dtype=float)
+        mu = np.asarray(mu, dtype=float)
+        rho = np.asarray(rho, dtype=float)
+        d = x - mu
+        s, c = np.sin(d), np.cos(d)
+        w = 1.0 / (1.0 + 2.0 * rho * c)
+        return {"mu": 2.0 * rho * s * w, "rho": 2.0 * c * w}
+
+    def d2logpdf(self, x, mu, rho):
+        r"""Second derivatives of ``logpdf`` (l2) — unique unordered pairs.
+
+        $$\ell_{\mu\mu} = -\frac{2\rho(cP + 2\rho s^2)}{P^2},\quad
+          \ell_{\mu\rho} = \frac{2s}{P^2},\quad
+          \ell_{\rho\rho} = -\frac{4c^2}{P^2},$$
+
+        with $s = \sin(\theta-\mu)$, $c = \cos(\theta-\mu)$ (the ℓ_{μρ}
+        numerator collapses because $P - 2\rho c = 1$).
+        """
+        x = np.asarray(x, dtype=float)
+        mu = np.asarray(mu, dtype=float)
+        rho = np.asarray(rho, dtype=float)
+        d = x - mu
+        s, c = np.sin(d), np.cos(d)
+        P = 1.0 + 2.0 * rho * c
+        w2 = 1.0 / (P * P)
+        return {
+            ("mu", "mu"): -2.0 * rho * (c * P + 2.0 * rho * s * s) * w2,
+            ("mu", "rho"): 2.0 * s * w2,
+            ("rho", "rho"): -4.0 * c * c * w2,
+        }
+
     def _argcheck(self, mu, rho):
         try:
             mu_arr, rho_arr = np.broadcast_arrays(mu, rho)
@@ -1335,6 +1751,62 @@ def pdf(self, x, mu, rho, *args, **kwargs):
         """
         return super().pdf(x, mu, rho, *args, **kwargs)
 
+    def _logpdf(self, x, mu, rho):
+        # log1p keeps the log-density exact down to the honest zero at the
+        # antipode for ρ = 1/2 (the linear-space density never underflows)
+        with np.errstate(divide="ignore"):
+            return np.log1p(2.0 * rho * np.cos(x - mu)) - np.log(2.0 * np.pi)
+
+    def logpdf(self, x, mu, rho, *args, **kwargs):
+        r"""
+        Logarithm of the probability density function of the Cardioid
+        distribution,
+
+        $$
+        \log f(\theta) = \mathrm{log1p}\big(2\rho \cos(\theta - \mu)\big) - \log 2\pi,
+        $$
+
+        $-\infty$ only at the honest zero (the antipode at $\rho = 1/2$).
+
+        Parameters
+        ----------
+        x : array_like
+            Points at which to evaluate the log-density.
+        mu : float
+            Mean direction, 0 <= mu <= 2*pi.
+        rho : float
+            Mean resultant length, 0 <= rho <= 0.5.
+
+        Returns
+        -------
+        logpdf_values : array_like
+            Logarithm of the probability density function evaluated at `x`.
+        """
+        return super().logpdf(x, mu, rho, *args, **kwargs)
+
+    def trig_moment(self, p: int = 1, *args, **kwargs) -> complex:
+        """Closed-form trigonometric moment (book §4.3.4): the cardioid is
+        a pure first-harmonic perturbation, so m₁ = ρ·e^{iμ} and m_p = 0
+        for every |p| ≥ 2."""
+        shape_args, non_shape_kwargs = self._separate_shape_parameters(
+            args, kwargs, "trig_moment"
+        )
+        call_kwargs = self._prepare_call_kwargs(non_shape_kwargs, "trig_moment")
+        mu, rho = (float(np.asarray(v, dtype=float))
+                   for v in self._parse_args(*shape_args, **call_kwargs)[0])
+
+        if not np.isscalar(p):
+            raise ValueError("`p` must be an integer scalar.")
+        if int(round(p)) != p:
+            raise ValueError("`p` must be an integer.")
+        k = int(round(p))
+        if k == 0:
+            return complex(1.0, 0.0)
+        if abs(k) > 1:
+            return complex(0.0, 0.0)
+        value = rho * np.exp(1j * mu)
+        return complex(np.conjugate(value)) if k < 0 else complex(value)
+
     def _cdf(self, x, mu, rho):
         return (x + 2 * rho * (np.sin(x - mu) + np.sin(mu))) / (2 * np.pi)
 
@@ -1730,7 +2202,7 @@ def _grad(params):
 cardioid = cardioid_gen(name="cardioid")
 
 
-class cartwright_gen(CircularContinuous):
+class cartwright_gen(_RegressionReady, CircularContinuous):
     """Cartwright's Power-of-Cosine Distribution
 
     ![cartwright](../images/circ-mod-cartwright.png)
@@ -1741,6 +2213,9 @@ class cartwright_gen(CircularContinuous):
     pdf(x, mu, zeta)
         Probability density function.
 
+    logpdf(x, mu, zeta)
+        Logarithm of the probability density function.
+
     cdf(x, mu, zeta)
         Cumulative distribution function.
 
@@ -1755,9 +2230,91 @@ class cartwright_gen(CircularContinuous):
 
     Note
     ----
-    Implementation based on Section 4.3.5 of Pewsey et al. (2014)
+    Implementation based on Section 4.3.5 of Pewsey et al. (2013)
     """
 
+    # --- regression overlay (Phase 1 contract; read by the regression engine
+    # only). Book names mu/zeta are preserved; ζ > 0 is an inverse
+    # peakedness, so its default link is log. ---
+    param_roles = {"mu": "location", "zeta": "concentration"}
+    default_links = {"location": "tanhalf", "concentration": "log"}
+
+    # The log-density is ℓ = (1/ζ − 1) log 2 + 2 log Γ(1+1/ζ) − log π
+    # − log Γ(1+2/ζ) + (1/ζ) L with L = log(1 + cos(θ−μ)), evaluated as
+    # log 2 + 2 log|cos((θ−μ)/2)| to avoid the 1+cos cancellation near the
+    # antipode. Likelihood hazard documented in the regression plan: the
+    # density is exactly 0 at θ−μ = π for every ζ, where L, tan((θ−μ)/2)
+    # and all derivatives diverge. In practice this makes the tanhalf
+    # likelihood's generic multimodality bite hard — from the intercept-only
+    # null start the fit lands in a wrong basin on roughly half of random
+    # datasets. Mitigation (verified in the dev smoke harness): warm-start
+    # the location LP from a wrapped-normal pilot fit (whose density has no
+    # zeros), ζ from this family's own intercept-only ``fit``.
+
+    def dlogpdf(self, x, mu, zeta):
+        r"""First derivatives of ``logpdf`` w.r.t. the parameters (l1).
+
+        $$\frac{\partial\ell}{\partial\mu} = \frac{\tan((\theta-\mu)/2)}{\zeta},
+          \qquad
+          \frac{\partial\ell}{\partial\zeta} = \frac{-\log 2
+          - 2\psi(1+1/\zeta) + 2\psi(1+2/\zeta) - L}{\zeta^2},$$
+
+        with $\psi$ the digamma function (the ζ column matches the analytic
+        gradient used by ``fit``). Vectorizes over per-observation
+        ``mu``/``zeta`` arrays; returns a book-named dict.
+        """
+        x = np.asarray(x, dtype=float)
+        mu = np.asarray(mu, dtype=float)
+        zeta = np.asarray(zeta, dtype=float)
+        d = x - mu
+        t = np.tan(0.5 * d)
+        with np.errstate(divide="ignore"):  # L → −∞ at the antipode
+            L = np.log(2.0) + 2.0 * np.log(np.abs(np.cos(0.5 * d)))
+        inv = 1.0 / zeta
+        B = (
+            -np.log(2.0)
+            - 2.0 * digamma(1.0 + inv)
+            + 2.0 * digamma(1.0 + 2.0 * inv)
+            - L
+        )
+        return {"mu": t * inv, "zeta": B * inv * inv}
+
+    def d2logpdf(self, x, mu, zeta):
+        r"""Second derivatives of ``logpdf`` (l2) — unique unordered pairs.
+
+        $$\ell_{\mu\mu} = -\frac{1+t^2}{2\zeta},\quad
+          \ell_{\mu\zeta} = -\frac{t}{\zeta^2},\quad
+          \ell_{\zeta\zeta} = \frac{2\psi_1(1+1/\zeta)
+          - 4\psi_1(1+2/\zeta)}{\zeta^4} - \frac{2}{\zeta}\,\ell_\zeta,$$
+
+        with $t = \tan((\theta-\mu)/2)$ and $\psi_1$ the trigamma function.
+        """
+        x = np.asarray(x, dtype=float)
+        mu = np.asarray(mu, dtype=float)
+        zeta = np.asarray(zeta, dtype=float)
+        d = x - mu
+        t = np.tan(0.5 * d)
+        with np.errstate(divide="ignore"):
+            L = np.log(2.0) + 2.0 * np.log(np.abs(np.cos(0.5 * d)))
+        inv = 1.0 / zeta
+        B = (
+            -np.log(2.0)
+            - 2.0 * digamma(1.0 + inv)
+            + 2.0 * digamma(1.0 + 2.0 * inv)
+            - L
+        )
+        l_zeta = B * inv * inv
+        return {
+            ("mu", "mu"): -0.5 * (1.0 + t * t) * inv,
+            ("mu", "zeta"): -t * inv * inv,
+            ("zeta", "zeta"): (
+                2.0 * polygamma(1, 1.0 + inv)
+                - 4.0 * polygamma(1, 1.0 + 2.0 * inv)
+            )
+            * inv**4
+            - 2.0 * inv * l_zeta,
+        }
+
     def _argcheck(self, mu, zeta):
         try:
             mu_arr, zeta_arr = np.broadcast_arrays(mu, zeta)
@@ -1782,11 +2339,10 @@ def _moment_r(zeta):
         return float(result) if np.isscalar(zeta) else result
 
     def _pdf(self, x, mu, zeta):
-        return (
-            (2 ** (-1 + 1 / zeta) * (gamma(1 + 1 / zeta)) ** 2)
-            * (1 + np.cos(x - mu)) ** (1 / zeta)
-            / (np.pi * gamma(1 + 2 / zeta))
-        )
+        # exp of the gammaln log form: the previous raw-gamma assembly
+        # (2^{−1+1/ζ}Γ²(1+1/ζ)/(πΓ(1+2/ζ))·(1+cos φ)^{1/ζ}) returned nan
+        # for ζ ≲ 0.008 in-range — Γ(1+2/ζ) overflows at 2/ζ ≳ 170
+        return np.exp(self._logpdf(x, mu, zeta))
 
     def pdf(self, x, mu, zeta, *args, **kwargs):
         r"""
@@ -1815,6 +2371,83 @@ def pdf(self, x, mu, zeta, *args, **kwargs):
 
         return super().pdf(x, mu, zeta, *args, **kwargs)
 
+    def _logpdf(self, x, mu, zeta):
+        # (2/ζ − 1) log 2 + 2 log Γ(1+1/ζ) − log π − log Γ(1+2/ζ)
+        # + (2/ζ) log|cos(φ/2)| — the same form the regression l-derivatives
+        # differentiate (class comment above); the half-angle factorization
+        # avoids the 1+cos cancellation near the antipode, where the density
+        # has an honest zero for every ζ
+        half = 0.5 * (np.asarray(x, dtype=float) - mu)
+        with np.errstate(divide="ignore"):
+            log_cos = np.log(np.abs(np.cos(half)))
+        return (
+            (2.0 / zeta - 1.0) * np.log(2.0)
+            + 2.0 * gammaln(1.0 + 1.0 / zeta)
+            - gammaln(1.0 + 2.0 / zeta)
+            - np.log(np.pi)
+            + (2.0 / zeta) * log_cos
+        )
+
+    def logpdf(self, x, mu, zeta, *args, **kwargs):
+        r"""
+        Logarithm of the probability density function of the Cartwright
+        distribution,
+
+        $$
+        \log f(\theta) = \Big(\tfrac{2}{\zeta} - 1\Big)\log 2
+        + 2\log\Gamma\Big(1+\tfrac{1}{\zeta}\Big)
+        - \log\Gamma\Big(1+\tfrac{2}{\zeta}\Big) - \log\pi
+        + \tfrac{2}{\zeta}\log\Big|\cos\tfrac{\theta-\mu}{2}\Big|,
+        $$
+
+        finite for every $\zeta > 0$ (the raw-gamma density overflows for
+        $\zeta \lesssim 0.008$) and $-\infty$ only at the honest antipodal
+        zero.
+
+        Parameters
+        ----------
+        x : array_like
+            Points at which to evaluate the log-density.
+        mu : float
+            Mean direction, 0 <= mu <= 2*pi.
+        zeta : float
+            Shape parameter, zeta > 0.
+
+        Returns
+        -------
+        logpdf_values : array_like
+            Logarithm of the probability density function evaluated at `x`.
+        """
+        return super().logpdf(x, mu, zeta, *args, **kwargs)
+
+    def trig_moment(self, p: int = 1, *args, **kwargs) -> complex:
+        """Closed-form trigonometric moment (book §4.3.5 / J&S): with
+        s = 1/ζ the centered moments are the Γ-ratio
+        Γ(s+1)²/(Γ(s+1+p)Γ(s+1−p)), evaluated as the overflow-free finite
+        product Π_{k=1}^{p} (s−k+1)/(s+k) — whose p = 1 case s/(s+1)
+        equals the ``_moment_r`` log form by Legendre duplication."""
+        shape_args, non_shape_kwargs = self._separate_shape_parameters(
+            args, kwargs, "trig_moment"
+        )
+        call_kwargs = self._prepare_call_kwargs(non_shape_kwargs, "trig_moment")
+        mu, zeta = (float(np.asarray(v, dtype=float))
+                    for v in self._parse_args(*shape_args, **call_kwargs)[0])
+
+        if not np.isscalar(p):
+            raise ValueError("`p` must be an integer scalar.")
+        if int(round(p)) != p:
+            raise ValueError("`p` must be an integer.")
+        k = int(round(p))
+        if k == 0:
+            return complex(1.0, 0.0)
+        ak = abs(k)
+        s = 1.0 / zeta
+        alpha_p = 1.0
+        for j in range(1, ak + 1):
+            alpha_p *= (s - j + 1.0) / (s + j)
+        value = alpha_p * np.exp(1j * ak * mu)
+        return complex(np.conjugate(value)) if k < 0 else complex(value)
+
     @staticmethod
     def _cartwright_cumulative(phi, a, b, half_norm):
         phi_arr = np.asarray(phi, dtype=float)
@@ -1847,13 +2480,8 @@ def _cdf(self, x, mu, zeta):
         if flat.size == 0:
             return arr.astype(float)
 
-        mu_arr = np.asarray(mu, dtype=float)
-        zeta_arr = np.asarray(zeta, dtype=float)
-        if mu_arr.size != 1 or zeta_arr.size != 1:
-            raise ValueError("cartwright parameters must be scalar-valued.")
-
-        mu_val = float(mu_arr.reshape(-1)[0])
-        zeta_val = float(zeta_arr.reshape(-1)[0])
+        mu_val = _as_scalar_param(mu, "cartwright")
+        zeta_val = _as_scalar_param(zeta, "cartwright")
         if zeta_val <= 0.0:
             raise ValueError("`zeta` must be positive.")
 
@@ -1887,10 +2515,10 @@ def _cdf(self, x, mu, zeta):
 
         if arr.ndim == 0:
             value = float(cdf[0])
-            return 1.0 if np.isclose(float(wrapped), 2.0 * np.pi) else value
+            return 1.0 if float(wrapped) == 2.0 * np.pi else value
 
         result = cdf.reshape(arr.shape)
-        result[np.isclose(arr, 2.0 * np.pi)] = 1.0
+        result[arr == 2.0 * np.pi] = 1.0
         return result
 
     def cdf(self, x, mu, zeta, *args, **kwargs):
@@ -2243,7 +2871,7 @@ def grad(params):
 cartwright = cartwright_gen(name="cartwright")
 
 
-class wrapnorm_gen(CircularContinuous):
+class wrapnorm_gen(_RegressionReady, CircularContinuous):
     """Wrapped Normal Distribution
 
     ![wrapnorm](../images/circ-mod-wrapnorm.png)
@@ -2253,6 +2881,9 @@ class wrapnorm_gen(CircularContinuous):
     pdf(x, mu, rho)
         Probability density function.
 
+    logpdf(x, mu, rho)
+        Logarithm of the probability density function.
+
     cdf(x, mu, rho)
         Cumulative distribution function.
 
@@ -2273,9 +2904,15 @@ class wrapnorm_gen(CircularContinuous):
 
     Notes
     -----
-    Implementation based on Section 4.3.7 of Pewsey et al. (2014)
+    Implementation based on Section 4.3.7 of Pewsey et al. (2013)
     """
 
+    # --- regression overlay (Phase 1 contract; read by the regression engine
+    # only). Book names mu/rho are preserved; ρ is the mean resultant length,
+    # bounded in (0, 1), so its default link is logit. ---
+    param_roles = {"mu": "location", "rho": "concentration"}
+    default_links = {"location": "tanhalf", "concentration": "logit"}
+
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self._series_window_cache = {}
@@ -2292,12 +2929,41 @@ def _argcheck(self, mu, rho):
             & (rho_arr < 1.0)
         )
 
+    # Above this value the Fourier form's terms rho^(p^2) decay too slowly for
+    # a short series, and the partial sums cancel catastrophically in the
+    # tails (negative densities by rho ~ 0.99; relative tail noise already at
+    # rho = 0.9). The Gaussian-image form needs only |k| <= 2 images for
+    # rho > 0.35 and is positive term-by-term, so 0.8 sits comfortably inside
+    # both branches' exact zones.
+    _FOURIER_RHO_MAX = 0.8
+
     def _pdf(self, x, mu, rho):
-        return (
-            1
-            + 2
-            * np.sum([rho ** (p**2) * np.cos(p * (x - mu)) for p in range(1, 30)], 0)
-        ) / (2 * np.pi)
+        x_b, mu_b, rho_b = np.broadcast_arrays(
+            *(np.asarray(v, dtype=float) for v in (x, mu, rho))
+        )
+        shape = x_b.shape
+        xf = x_b.reshape(-1)
+        mf = mu_b.reshape(-1)
+        rf = rho_b.reshape(-1)
+        out = np.empty(xf.shape, dtype=float)
+
+        lo = rf <= self._FOURIER_RHO_MAX
+        if np.any(lo):
+            # Fourier form (book eq. 4.50); truncation error at p = 29 is
+            # rho^(900) <= 2e-82 for rho <= 0.8.
+            p = np.arange(1.0, 30.0)
+            d = xf[lo, None] - mf[lo, None]
+            series = np.sum(rf[lo, None] ** (p**2) * np.cos(p * d), axis=1)
+            out[lo] = (1.0 + 2.0 * series) / (2.0 * np.pi)
+        hi = ~lo
+        if np.any(hi):
+            # Gaussian-image form (book eq. 4.48) — the same representation
+            # the cdf path uses, so pdf == dF/dtheta at high concentration.
+            sigma = np.sqrt(-2.0 * np.log(np.clip(rf[hi], None, 1.0 - 1e-15)))
+            k = np.arange(-2.0, 3.0)
+            z = (xf[hi, None] - mf[hi, None] + 2.0 * np.pi * k) / sigma[:, None]
+            out[hi] = np.exp(-0.5 * z**2).sum(axis=1) * INV_SQRT_2PI / sigma
+        return out.reshape(shape)
 
     def pdf(self, x, mu, rho, *args, **kwargs):
         r"""
@@ -2307,7 +2973,10 @@ def pdf(self, x, mu, rho, *args, **kwargs):
         f(\theta) = \frac{1}{2\pi} \left(1 + 2\sum_{p=1}^{\infty} \rho^{p^2} \cos(p(\theta - \mu))\right)
         $$
 
-        , here we approximate the infinite sum by summing the first 30 terms.
+        evaluated via the Fourier form above for $\rho \le 0.8$ (29 terms,
+        truncation $\le 2 \times 10^{-82}$) and via the equivalent wrapped
+        Gaussian-image sum for $\rho > 0.8$, where the Fourier partial sums
+        lose accuracy.
 
         Parameters
         ----------
@@ -2316,7 +2985,7 @@ def pdf(self, x, mu, rho, *args, **kwargs):
         mu : float
             Mean direction, 0 <= mu <= 2*pi.
         rho : float
-            Shape parameter, 0 < rho <= 1.
+            Mean resultant length, 0 < rho < 1.
 
         Returns
         -------
@@ -2325,6 +2994,188 @@ def pdf(self, x, mu, rho, *args, **kwargs):
         """
         return super().pdf(x, mu, rho, *args, **kwargs)
 
+    def _logpdf(self, x, mu, rho):
+        x_b, mu_b, rho_b = np.broadcast_arrays(
+            *(np.asarray(v, dtype=float) for v in (x, mu, rho))
+        )
+        shape = x_b.shape
+        xf = x_b.reshape(-1)
+        mf = mu_b.reshape(-1)
+        rf = rho_b.reshape(-1)
+        out = np.empty(xf.shape, dtype=float)
+
+        lo = rf <= self._FOURIER_RHO_MAX
+        if np.any(lo):
+            # the Fourier density never underflows for ρ ≤ 0.8 (antipodal
+            # minimum ~2e-6), so log1p of the series is already exact
+            p = np.arange(1.0, 30.0)
+            d = xf[lo, None] - mf[lo, None]
+            series = np.sum(rf[lo, None] ** (p**2) * np.cos(p * d), axis=1)
+            out[lo] = np.log1p(2.0 * series) - np.log(2.0 * np.pi)
+        hi = ~lo
+        if np.any(hi):
+            # Gaussian-image branch in log space: logsumexp over the 5
+            # images keeps the antipodal tail finite (≈ −π²/(2σ²)) where
+            # the linear-space sum underflows from ρ ≈ 0.987
+            sigma = np.sqrt(-2.0 * np.log(np.clip(rf[hi], None, 1.0 - 1e-15)))
+            k = np.arange(-2.0, 3.0)
+            z = (xf[hi, None] - mf[hi, None] + 2.0 * np.pi * k) / sigma[:, None]
+            out[hi] = (
+                logsumexp(-0.5 * z**2, axis=1)
+                - np.log(sigma)
+                - 0.5 * np.log(2.0 * np.pi)
+            )
+        return out.reshape(shape)
+
+    def logpdf(self, x, mu, rho, *args, **kwargs):
+        r"""
+        Logarithm of the probability density function of the Wrapped Normal
+        distribution: ``log1p`` of the Fourier series for $\rho \le 0.8$ and
+        a log-sum-exp over the wrapped Gaussian images for $\rho > 0.8$, so
+        the log-density stays finite (antipodally $\approx -\pi^2/2\sigma^2$)
+        at concentrations where the linear-space density underflows.
+
+        Parameters
+        ----------
+        x : array_like
+            Points at which to evaluate the log-density.
+        mu : float
+            Mean direction, 0 <= mu <= 2*pi.
+        rho : float
+            Mean resultant length, 0 < rho < 1.
+
+        Returns
+        -------
+        logpdf_values : array_like
+            Logarithm of the probability density function evaluated at `x`.
+        """
+        return super().logpdf(x, mu, rho, *args, **kwargs)
+
+    def trig_moment(self, p: int = 1, *args, **kwargs) -> complex:
+        """Closed-form trigonometric moment (book §4.3.7):
+        m_p = ρ^{p²}·e^{ipμ}."""
+        shape_args, non_shape_kwargs = self._separate_shape_parameters(
+            args, kwargs, "trig_moment"
+        )
+        call_kwargs = self._prepare_call_kwargs(non_shape_kwargs, "trig_moment")
+        mu, rho = (float(np.asarray(v, dtype=float))
+                   for v in self._parse_args(*shape_args, **call_kwargs)[0])
+
+        if not np.isscalar(p):
+            raise ValueError("`p` must be an integer scalar.")
+        if int(round(p)) != p:
+            raise ValueError("`p` must be an integer.")
+        k = int(round(p))
+        if k == 0:
+            return complex(1.0, 0.0)
+        ak = abs(k)
+        value = (rho ** (ak * ak)) * np.exp(1j * ak * mu)
+        return complex(np.conjugate(value)) if k < 0 else complex(value)
+
+    # Derivative methods (the regression contract) reuse the same hybrid
+    # split as ``_pdf``, so l1/l2 differentiate exactly the density that
+    # ``logpdf`` evaluates. Fourier branch: term-wise series ratios. Gaussian
+    # branch: scores of a 5-component wrapped-image mixture as softmax-
+    # weighted moments m_j = Σ w_k z_k^j (the e^{-z²/2} weights are
+    # max-stabilized, so no underflow at high concentration), chained
+    # through σ(ρ) = √(−2 log ρ) with σ' = −1/(ρσ), σ'' = (σ²−1)/(ρ²σ³).
+
+    def _score_terms(self, x, mu, rho, second):
+        x_b, mu_b, rho_b = np.broadcast_arrays(
+            *(np.asarray(v, dtype=float) for v in (x, mu, rho))
+        )
+        shape = x_b.shape
+        xf = x_b.reshape(-1)
+        mf = mu_b.reshape(-1)
+        rf = rho_b.reshape(-1)
+        d1 = {k: np.empty(xf.shape, dtype=float) for k in ("mu", "rho")}
+        d2 = (
+            {k: np.empty(xf.shape, dtype=float)
+             for k in (("mu", "mu"), ("mu", "rho"), ("rho", "rho"))}
+            if second
+            else None
+        )
+
+        lo = rf <= self._FOURIER_RHO_MAX
+        if np.any(lo):
+            p = np.arange(1.0, 30.0)
+            d = xf[lo, None] - mf[lo, None]
+            r = rf[lo, None]
+            rp = r ** (p**2)
+            cs, sn = np.cos(p * d), np.sin(p * d)
+            S0 = 1.0 + 2.0 * np.sum(rp * cs, axis=1)
+            Smu = 2.0 * np.sum(p * rp * sn, axis=1)
+            Srho = 2.0 * np.sum(p**2 * r ** (p**2 - 1.0) * cs, axis=1)
+            lmu, lrho = Smu / S0, Srho / S0
+            d1["mu"][lo], d1["rho"][lo] = lmu, lrho
+            if second:
+                Smm = -2.0 * np.sum(p**2 * rp * cs, axis=1)
+                Smr = 2.0 * np.sum(p**3 * r ** (p**2 - 1.0) * sn, axis=1)
+                # the p = 1 term of S_ρρ has coefficient p²(p²−1) = 0; start
+                # at p = 2 so ρ^{p²−2} never sees a negative exponent
+                p2 = p[1:]
+                Srr = 2.0 * np.sum(
+                    p2**2 * (p2**2 - 1.0) * r ** (p2**2 - 2.0) * cs[:, 1:],
+                    axis=1,
+                )
+                d2[("mu", "mu")][lo] = Smm / S0 - lmu * lmu
+                d2[("mu", "rho")][lo] = Smr / S0 - lmu * lrho
+                d2[("rho", "rho")][lo] = Srr / S0 - lrho * lrho
+
+        hi = ~lo
+        if np.any(hi):
+            rh = np.clip(rf[hi], None, 1.0 - 1e-15)
+            sigma = np.sqrt(-2.0 * np.log(rh))
+            k = np.arange(-2.0, 3.0)
+            z = (xf[hi, None] - mf[hi, None] + 2.0 * np.pi * k) / sigma[:, None]
+            z2 = z * z
+            w = np.exp(-0.5 * (z2 - np.min(z2, axis=1, keepdims=True)))
+            w /= np.sum(w, axis=1, keepdims=True)
+            m1 = np.sum(w * z, axis=1)
+            m2 = np.sum(w * z2, axis=1)
+            l_sig = (m2 - 1.0) / sigma
+            sp = -1.0 / (rh * sigma)  # dσ/dρ
+            d1["mu"][hi] = m1 / sigma
+            d1["rho"][hi] = l_sig * sp
+            if second:
+                m3 = np.sum(w * z2 * z, axis=1)
+                m4 = np.sum(w * z2 * z2, axis=1)
+                s2 = sigma * sigma
+                l_mm = (m2 - 1.0 - m1 * m1) / s2
+                l_ms = (m3 - 3.0 * m1 - m1 * (m2 - 1.0)) / s2
+                l_ss = (m4 - 5.0 * m2 + 2.0 - (m2 - 1.0) ** 2) / s2
+                spp = (s2 - 1.0) / (rh * rh * sigma * s2)  # d²σ/dρ²
+                d2[("mu", "mu")][hi] = l_mm
+                d2[("mu", "rho")][hi] = l_ms * sp
+                d2[("rho", "rho")][hi] = l_ss * sp * sp + l_sig * spp
+
+        d1 = {k: v.reshape(shape) for k, v in d1.items()}
+        if second:
+            d2 = {k: v.reshape(shape) for k, v in d2.items()}
+        return d1, d2
+
+    def dlogpdf(self, x, mu, rho):
+        r"""First derivatives of ``logpdf`` w.r.t. the parameters (l1).
+
+        Fourier branch (ρ ≤ 0.8): with $S_0 = 1 + 2\sum_p \rho^{p^2}\cos p\phi$,
+
+        $$\frac{\partial\ell}{\partial\mu} =
+          \frac{2\sum_p p\,\rho^{p^2}\sin p\phi}{S_0},\qquad
+          \frac{\partial\ell}{\partial\rho} =
+          \frac{2\sum_p p^2\rho^{p^2-1}\cos p\phi}{S_0}.$$
+
+        Gaussian-image branch (ρ > 0.8): mixture scores via softmax-weighted
+        moments, chained through σ(ρ). Vectorizes over per-observation
+        ``mu``/``rho`` arrays; returns a book-named dict.
+        """
+        return self._score_terms(x, mu, rho, second=False)[0]
+
+    def d2logpdf(self, x, mu, rho):
+        r"""Second derivatives of ``logpdf`` (l2) — unique unordered pairs,
+        as ratio forms $f_{ab}/f - (f_a/f)(f_b/f)$ on the same hybrid split
+        as ``_pdf`` (see ``_score_terms``)."""
+        return self._score_terms(x, mu, rho, second=True)[1]
+
     @staticmethod
     def _wrapnorm_cdf_pdf(theta, mu_val, sigma, *, tol=1e-13, max_iter=500):
         theta_arr = np.asarray(theta, dtype=float)
@@ -2381,22 +3232,17 @@ def _cdf(self, x, mu, rho):
         if flat.size == 0:
             return arr.astype(float)
 
-        mu_arr = np.asarray(mu, dtype=float)
-        rho_arr = np.asarray(rho, dtype=float)
-        if mu_arr.size != 1 or rho_arr.size != 1:
-            raise ValueError("wrapnorm parameters must be scalar-valued.")
-
-        mu_val = float(mu_arr.reshape(-1)[0])
-        rho_val = float(rho_arr.reshape(-1)[0])
+        mu_val = _as_scalar_param(mu, "wrapnorm")
+        rho_val = _as_scalar_param(rho, "wrapnorm")
         two_pi = 2.0 * np.pi
 
         if rho_val <= 1e-12:
             uniform = flat / two_pi
             if arr.ndim == 0:
                 value = float(uniform[0])
-                return 1.0 if np.isclose(float(wrapped), two_pi) else value
+                return 1.0 if float(wrapped) == two_pi else value
             result = uniform.reshape(arr.shape)
-            result[np.isclose(arr, two_pi)] = 1.0
+            result[arr == two_pi] = 1.0
             return result
 
         rho_clipped = np.clip(rho_val, np.finfo(float).tiny, 1.0 - 1e-15)
@@ -2405,10 +3251,10 @@ def _cdf(self, x, mu, rho):
         cdf_flat, _ = self._wrapnorm_cdf_pdf(flat, mu_val, sigma)
         if arr.ndim == 0:
             value = float(cdf_flat.reshape(-1)[0])
-            return 1.0 if np.isclose(float(wrapped), two_pi) else value
+            return 1.0 if float(wrapped) == two_pi else value
 
         result = cdf_flat.reshape(arr.shape)
-        result[np.isclose(arr, two_pi)] = 1.0
+        result[arr == two_pi] = 1.0
         return result
 
     def cdf(self, x, mu, rho, *args, **kwargs):
@@ -2817,7 +3663,7 @@ def nll(params):
 wrapnorm = wrapnorm_gen(name="wrapnorm")
 
 
-class wrapcauchy_gen(CircularContinuous):
+class wrapcauchy_gen(_RegressionReady, CircularContinuous):
     """Wrapped Cauchy Distribution.
 
     ![wrapcauchy](../images/circ-mod-wrapcauchy.png)
@@ -2827,6 +3673,9 @@ class wrapcauchy_gen(CircularContinuous):
     pdf(x, mu, rho)
         Probability density function.
 
+    logpdf(x, mu, rho)
+        Logarithm of the probability density function (exact log1p form).
+
     cdf(x, mu, rho)
         Cumulative distribution function.
 
@@ -2841,9 +3690,142 @@ class wrapcauchy_gen(CircularContinuous):
 
     Notes
     -----
-    Implementation based on Section 4.3.6 of Pewsey et al. (2014).
+    Implementation based on Section 4.3.6 of Pewsey et al. (2013).
     """
 
+    # --- regression overlay (Phase 1 contract; read by the regression engine
+    # only). Book names mu/rho are preserved; ρ is the mean resultant length,
+    # bounded in (0, 1), so its default link is logit. ---
+    param_roles = {"mu": "location", "rho": "concentration"}
+    default_links = {"location": "tanhalf", "concentration": "logit"}
+
+    # The log-density splits as ℓ = log(1−ρ²) − log 2π − log D with
+    # D = 1 + ρ² − 2ρ cos(θ−μ). The derivative methods below differentiate
+    # −log D through the multivariate chain rule from D's (sparse) partial
+    # table — ∂μ cycles the trig terms; D is quadratic in ρ so D_ρρρ… = 0 —
+    # and add the ρ-only derivatives of log(1−ρ²). All pure numpy,
+    # broadcasting over per-observation parameter arrays.
+
+    def dlogpdf(self, x, mu, rho):
+        r"""First derivatives of ``logpdf`` w.r.t. the parameters (l1).
+
+        $$\frac{\partial\ell}{\partial\mu} = \frac{2\rho\sin(\theta-\mu)}{D},
+          \qquad
+          \frac{\partial\ell}{\partial\rho} = -\frac{2\rho}{1-\rho^2}
+          - \frac{2\rho - 2\cos(\theta-\mu)}{D}.$$
+
+        Vectorizes over per-observation ``mu``/``rho`` arrays. Returns a
+        book-named dict ``{"mu": …, "rho": …}``.
+        """
+        x = np.asarray(x, dtype=float)
+        mu = np.asarray(mu, dtype=float)
+        rho = np.asarray(rho, dtype=float)
+        d = x - mu
+        s, c = np.sin(d), np.cos(d)
+        w = 1.0 / ((1.0 - rho) ** 2 + 4.0 * rho * np.sin(0.5 * d) ** 2)
+        return {
+            "mu": 2.0 * rho * s * w,
+            "rho": -2.0 * rho / (1.0 - rho**2) - (2.0 * rho - 2.0 * c) * w,
+        }
+
+    def d2logpdf(self, x, mu, rho):
+        r"""Second derivatives of ``logpdf`` (l2), as *observed* derivatives —
+        only the unique unordered pairs, from ``(−log D)_{ab} = −D_{ab}/D +
+        D_a D_b/D²`` plus ``∂²_ρ\log(1-\rho^2) = -2(1+\rho^2)/(1-\rho^2)^2``.
+        """
+        x = np.asarray(x, dtype=float)
+        mu = np.asarray(mu, dtype=float)
+        rho = np.asarray(rho, dtype=float)
+        d = x - mu
+        s, c = np.sin(d), np.cos(d)
+        w = 1.0 / ((1.0 - rho) ** 2 + 4.0 * rho * np.sin(0.5 * d) ** 2)
+        Dm, Dr = -2.0 * rho * s, 2.0 * rho - 2.0 * c
+        w2 = w * w
+        return {
+            ("mu", "mu"): -2.0 * rho * c * w + Dm * Dm * w2,
+            ("mu", "rho"): 2.0 * s * w + Dm * Dr * w2,
+            ("rho", "rho"): -2.0 * (1.0 + rho**2) / (1.0 - rho**2) ** 2
+            - 2.0 * w
+            + Dr * Dr * w2,
+        }
+
+    def d3logpdf(self, x, mu, rho):
+        r"""Third derivatives of ``logpdf`` (l3) — unique unordered triples,
+        via ``(−log D)_{abc} = −D_{abc}/D + (D_{ab}D_c + D_{ac}D_b +
+        D_{bc}D_a)/D² − 2D_aD_bD_c/D³`` (terms with vanished ``D``-partials
+        dropped) and ``∂³_ρ\log(1-\rho^2) = -4\rho(3+\rho^2)/(1-\rho^2)^3``.
+        """
+        x = np.asarray(x, dtype=float)
+        mu = np.asarray(mu, dtype=float)
+        rho = np.asarray(rho, dtype=float)
+        d = x - mu
+        s, c = np.sin(d), np.cos(d)
+        w = 1.0 / ((1.0 - rho) ** 2 + 4.0 * rho * np.sin(0.5 * d) ** 2)
+        Dm, Dr = -2.0 * rho * s, 2.0 * rho - 2.0 * c
+        Dmm, Dmr, Drr = 2.0 * rho * c, -2.0 * s, 2.0
+        Dmmm, Dmmr = 2.0 * rho * s, 2.0 * c
+        w2, w3 = w * w, w * w * w
+        return {
+            ("mu", "mu", "mu"): -Dmmm * w
+            + 3.0 * Dmm * Dm * w2
+            - 2.0 * Dm**3 * w3,
+            ("mu", "mu", "rho"): -Dmmr * w
+            + (Dmm * Dr + 2.0 * Dmr * Dm) * w2
+            - 2.0 * Dm * Dm * Dr * w3,
+            ("mu", "rho", "rho"): (2.0 * Dmr * Dr + Drr * Dm) * w2
+            - 2.0 * Dm * Dr * Dr * w3,
+            ("rho", "rho", "rho"): -4.0 * rho * (3.0 + rho**2)
+            / (1.0 - rho**2) ** 3
+            + 3.0 * Drr * Dr * w2
+            - 2.0 * Dr**3 * w3,
+        }
+
+    def d4logpdf(self, x, mu, rho):
+        r"""Fourth derivatives of ``logpdf`` (l4) — unique unordered
+        quadruples, from the order-4 partition formula for ``−log D``
+        (``D``'s only nonzero quartic-relevant partials are
+        ``D_{μμμμ}, D_{μμμρ}``; everything with ≥2 ρ-derivatives of ``D``
+        beyond ``D_{ρρ}=2`` vanishes) and ``∂⁴_ρ\log(1-\rho^2) =
+        -12(1+6\rho^2+\rho^4)/(1-\rho^2)^4``. Completes the contract to
+        full-Newton depth (hea ``available_derivs = 2``).
+        """
+        x = np.asarray(x, dtype=float)
+        mu = np.asarray(mu, dtype=float)
+        rho = np.asarray(rho, dtype=float)
+        d = x - mu
+        s, c = np.sin(d), np.cos(d)
+        w = 1.0 / ((1.0 - rho) ** 2 + 4.0 * rho * np.sin(0.5 * d) ** 2)
+        Dm, Dr = -2.0 * rho * s, 2.0 * rho - 2.0 * c
+        Dmm, Dmr, Drr = 2.0 * rho * c, -2.0 * s, 2.0
+        Dmmm, Dmmr = 2.0 * rho * s, 2.0 * c
+        Dmmmm, Dmmmr = -2.0 * rho * c, 2.0 * s
+        w2, w3, w4 = w * w, w**3, w**4
+        return {
+            ("mu", "mu", "mu", "mu"): -Dmmmm * w
+            + (4.0 * Dmmm * Dm + 3.0 * Dmm * Dmm) * w2
+            - 12.0 * Dmm * Dm * Dm * w3
+            + 6.0 * Dm**4 * w4,
+            ("mu", "mu", "mu", "rho"): -Dmmmr * w
+            + (Dmmm * Dr + 3.0 * Dmmr * Dm + 3.0 * Dmm * Dmr) * w2
+            - 6.0 * (Dmm * Dm * Dr + Dmr * Dm * Dm) * w3
+            + 6.0 * Dm**3 * Dr * w4,
+            ("mu", "mu", "rho", "rho"): (
+                2.0 * Dmmr * Dr + Dmm * Drr + 2.0 * Dmr * Dmr
+            )
+            * w2
+            - 2.0 * (Dmm * Dr * Dr + Drr * Dm * Dm + 4.0 * Dmr * Dm * Dr) * w3
+            + 6.0 * Dm * Dm * Dr * Dr * w4,
+            ("mu", "rho", "rho", "rho"): 3.0 * Dmr * Drr * w2
+            - 6.0 * (Drr * Dm * Dr + Dmr * Dr * Dr) * w3
+            + 6.0 * Dm * Dr**3 * w4,
+            ("rho", "rho", "rho", "rho"): -12.0
+            * (1.0 + 6.0 * rho**2 + rho**4)
+            / (1.0 - rho**2) ** 4
+            + 3.0 * Drr * Drr * w2
+            - 12.0 * Drr * Dr * Dr * w3
+            + 6.0 * Dr**4 * w4,
+        }
+
     def _argcheck(self, mu, rho):
         try:
             mu_arr, rho_arr = np.broadcast_arrays(mu, rho)
@@ -2857,7 +3839,11 @@ def _argcheck(self, mu, rho):
         )
 
     def _pdf(self, x, mu, rho):
-        return (1 - rho**2) / (2 * np.pi * (1 + rho**2 - 2 * rho * np.cos(x - mu)))
+        # (1-rho)^2 + 4*rho*sin^2(d/2) == 1 + rho^2 - 2*rho*cos(d), but does
+        # not cancel at the peak as rho -> 1 (the naive form rounds to <= 0
+        # for rho > 1 - 1e-8).
+        denom = (1 - rho) ** 2 + 4 * rho * np.sin(0.5 * (x - mu)) ** 2
+        return (1 - rho) * (1 + rho) / (2 * np.pi * denom)
 
     def pdf(self, x, mu, rho, *args, **kwargs):
         r"""
@@ -2884,7 +3870,17 @@ def pdf(self, x, mu, rho, *args, **kwargs):
         return super().pdf(x, mu, rho, *args, **kwargs)
 
     def _logpdf(self, x, mu, rho):
-        return np.log(np.clip(self._pdf(x, mu, rho), 1e-16, None))
+        # exact log form on the same cancellation-free denominator as
+        # ``_pdf`` (P2): the previous log(clip(pdf, 1e-16)) floored the
+        # honest tail once ρ came within a few ulp of 1
+        denom = (1 - rho) ** 2 + 4 * rho * np.sin(0.5 * (x - mu)) ** 2
+        with np.errstate(divide="ignore"):
+            return (
+                np.log1p(-rho)
+                + np.log1p(rho)
+                - np.log(2.0 * np.pi)
+                - np.log(denom)
+            )
 
     def logpdf(self, x, mu, rho, *args, **kwargs):
         """
@@ -2906,19 +3902,34 @@ def logpdf(self, x, mu, rho, *args, **kwargs):
         """
         return super().logpdf(x, mu, rho, *args, **kwargs)
 
+    def trig_moment(self, p: int = 1, *args, **kwargs) -> complex:
+        """Closed-form trigonometric moment (book §4.3.6):
+        m_p = ρ^{p}·e^{ipμ}."""
+        shape_args, non_shape_kwargs = self._separate_shape_parameters(
+            args, kwargs, "trig_moment"
+        )
+        call_kwargs = self._prepare_call_kwargs(non_shape_kwargs, "trig_moment")
+        mu, rho = (float(np.asarray(v, dtype=float))
+                   for v in self._parse_args(*shape_args, **call_kwargs)[0])
+
+        if not np.isscalar(p):
+            raise ValueError("`p` must be an integer scalar.")
+        if int(round(p)) != p:
+            raise ValueError("`p` must be an integer.")
+        k = int(round(p))
+        if k == 0:
+            return complex(1.0, 0.0)
+        ak = abs(k)
+        value = (rho ** ak) * np.exp(1j * ak * mu)
+        return complex(np.conjugate(value)) if k < 0 else complex(value)
+
     def _cdf(self, x, mu, rho):
         wrapped = self._wrap_angles(x)
         arr = np.asarray(wrapped, dtype=float)
         flat = arr.reshape(-1)
 
-        mu_arr = np.asarray(mu, dtype=float)
-        if mu_arr.size != 1:
-            raise ValueError("wrapcauchy parameters must be scalar-valued.")
-        mu_val = float(mu_arr.reshape(-1)[0])
-        rho_arr = np.asarray(rho, dtype=float)
-        if rho_arr.size != 1:
-            raise ValueError("wrapcauchy parameters must be scalar-valued.")
-        rho_val = float(rho_arr.reshape(-1)[0])
+        mu_val = _as_scalar_param(mu, "wrapcauchy")
+        rho_val = _as_scalar_param(rho, "wrapcauchy")
         rho_val = np.clip(rho_val, np.finfo(float).tiny, 1.0 - 1e-15)
 
         if flat.size == 0:
@@ -2943,9 +3954,9 @@ def _cdf(self, x, mu, rho):
 
         if arr.ndim == 0:
             value = float(cdf[0])
-            return 1.0 if np.isclose(float(wrapped), 2.0 * np.pi) else value
+            return 1.0 if float(wrapped) == 2.0 * np.pi else value
         reshaped = cdf.reshape(arr.shape)
-        reshaped[np.isclose(arr, 2.0 * np.pi)] = 1.0
+        reshaped[arr == 2.0 * np.pi] = 1.0
         return reshaped
 
     def cdf(self, x, mu, rho, *args, **kwargs):
@@ -3184,14 +4195,14 @@ def nll(params):
             mu_param, rho_param = params
             if not (0.0 <= rho_param < 1.0):
                 return np.inf
-            denom = np.clip(1.0 + rho_param**2 - 2.0 * rho_param * np.cos(x - mu_param), 1e-15, None)
+            denom = (1.0 - rho_param) ** 2 + 4.0 * rho_param * np.sin(0.5 * (x - mu_param)) ** 2
             log_pdf = np.log1p(-rho_param**2) - np.log(2.0 * np.pi) - np.log(denom)
             value = -np.sum(w * log_pdf)
             return float(value)
 
         def grad(params):
             mu_param, rho_param = params
-            denom = np.clip(1.0 + rho_param**2 - 2.0 * rho_param * np.cos(x - mu_param), 1e-15, None)
+            denom = (1.0 - rho_param) ** 2 + 4.0 * rho_param * np.sin(0.5 * (x - mu_param)) ** 2
             cos_term = np.cos(x - mu_param)
             sin_term = np.sin(x - mu_param)
 
@@ -3258,7 +4269,7 @@ def grad(params):
 wrapcauchy = wrapcauchy_gen(name="wrapcauchy")
 
 
-class vonmises_gen(CircularContinuous):
+class vonmises_gen(_RegressionReady, CircularContinuous):
     """Von Mises Distribution
 
     ![vonmises](../images/circ-mod-vonmises.png)
@@ -3268,6 +4279,10 @@ class vonmises_gen(CircularContinuous):
     pdf(x, mu, kappa)
         Probability density function.
 
+    logpdf(x, mu, kappa)
+        Logarithm of the probability density function (scaled-Bessel
+        form, exact at every κ).
+
     cdf(x, mu, kappa)
         Cumulative distribution function.
 
@@ -3288,7 +4303,7 @@ class vonmises_gen(CircularContinuous):
 
     References
     ----------
-    - Section 4.3.8 of Pewsey et al. (2014)
+    - Section 4.3.8 of Pewsey et al. (2013)
 
     """
 
@@ -3300,7 +4315,7 @@ class vonmises_gen(CircularContinuous):
     mu : float
         The mean direction of the distribution (0 <= mu <= 2*pi).
     kappa : float
-        The concentration parameter of the distribution (kappa > 0).
+        The concentration parameter of the distribution (kappa >= 0; kappa = 0 is the circular uniform limit).
 
     Returns
     -------
@@ -3308,11 +4323,112 @@ class vonmises_gen(CircularContinuous):
         The frozen distribution instance with fixed parameters.
     """
 
+    # --- regression overlay (Phase 1 contract; read by the regression engine
+    # only). Book names mu/kappa are preserved; roles attach the default links. ---
+    param_roles = {"mu": "location", "kappa": "concentration"}
+    default_links = {"location": "tanhalf", "concentration": "log"}
+
     def __call__(self, *args, **kwds):
         return self.freeze(*args, **kwds)
 
     __call__.__doc__ = _freeze_doc
 
+    def dlogpdf(self, x, mu, kappa):
+        r"""First derivatives of ``logpdf`` w.r.t. the parameters (l1).
+
+        For the von Mises log-density
+        ``ℓ = κ cos(θ − μ) − log(2π I_0(κ))``:
+
+        $$\frac{\partial\ell}{\partial\mu} = \kappa\sin(\theta-\mu),\qquad
+          \frac{\partial\ell}{\partial\kappa} = \cos(\theta-\mu) - A_1(\kappa).$$
+
+        Vectorizes over per-observation ``mu``/``kappa`` arrays. Returns a
+        book-named dict ``{"mu": …, "kappa": …}``.
+        """
+        x = np.asarray(x, dtype=float)
+        mu = np.asarray(mu, dtype=float)
+        kappa = np.asarray(kappa, dtype=float)
+        d = x - mu
+        return {
+            "mu": kappa * np.sin(d),
+            "kappa": np.cos(d) - A1(kappa),
+        }
+
+    def d2logpdf(self, x, mu, kappa):
+        r"""Second derivatives of ``logpdf`` (l2), as *observed* (not Fisher)
+        derivatives — only the unique unordered pairs:
+
+        $$\partial^2_{\mu\mu}\ell = -\kappa\cos(\theta-\mu),\quad
+          \partial^2_{\mu\kappa}\ell = \sin(\theta-\mu),\quad
+          \partial^2_{\kappa\kappa}\ell = -A_1'(\kappa).$$
+
+        (The von Mises Fisher information for ``μ`` is ``κ A_1(κ) =
+        −E[∂²_{μμ}ℓ]``; ``CLRegression`` uses that expected form for Fisher
+        scoring. The contract exposes the honest observed derivatives, which is
+        what a general-likelihood Newton step — and hea's ``gam.fit5`` — want.)
+        """
+        x = np.asarray(x, dtype=float)
+        mu = np.asarray(mu, dtype=float)
+        kappa = np.asarray(kappa, dtype=float)
+        d = x - mu
+        return {
+            ("mu", "mu"): -kappa * np.cos(d),
+            ("mu", "kappa"): np.sin(d),
+            ("kappa", "kappa"): -A1prime(kappa),
+        }
+
+    def d3logpdf(self, x, mu, kappa):
+        r"""Third derivatives of ``logpdf`` (l3) — unique unordered triples.
+        ``∂_μ`` cycles the trig terms (``∂_μ cos(θ−μ) = sin(θ−μ)``,
+        ``∂_μ sin(θ−μ) = −cos(θ−μ)``); the κ-only direction differentiates
+        ``−A_1``:
+
+        $$\partial^3_{\mu\mu\mu}\ell = -\kappa\sin(\theta-\mu),\quad
+          \partial^3_{\mu\mu\kappa}\ell = -\cos(\theta-\mu),\quad
+          \partial^3_{\mu\kappa\kappa}\ell = 0,\quad
+          \partial^3_{\kappa\kappa\kappa}\ell = -A_1''(\kappa).$$
+
+        With l1/l2 this is the depth hea's gradient-outer Newton needs
+        (``available_derivs = 1``); see ``d4logpdf`` for the full-Newton tier.
+        """
+        x = np.asarray(x, dtype=float)
+        mu = np.asarray(mu, dtype=float)
+        kappa = np.asarray(kappa, dtype=float)
+        d = x - mu
+        return {
+            ("mu", "mu", "mu"): -kappa * np.sin(d),
+            ("mu", "mu", "kappa"): -np.cos(d),
+            ("mu", "kappa", "kappa"): np.zeros(
+                np.broadcast_shapes(x.shape, mu.shape, kappa.shape)
+            ),
+            ("kappa", "kappa", "kappa"): -A1prime2(kappa),
+        }
+
+    def d4logpdf(self, x, mu, kappa):
+        r"""Fourth derivatives of ``logpdf`` (l4) — unique unordered quadruples:
+
+        $$\partial^4_{\mu\mu\mu\mu}\ell = \kappa\cos(\theta-\mu),\quad
+          \partial^4_{\mu\mu\mu\kappa}\ell = -\sin(\theta-\mu),\quad
+          \partial^4_{\kappa\kappa\kappa\kappa}\ell = -A_1'''(\kappa),$$
+
+        and the remaining mixed quadruples vanish (``ℓ`` is linear in κ apart
+        from ``−log I_0(κ)``, so any term with ≥2 κ-derivatives and ≥1
+        μ-derivative is zero). This completes the contract to the depth hea's
+        full outer Newton uses (``available_derivs = 2``).
+        """
+        x = np.asarray(x, dtype=float)
+        mu = np.asarray(mu, dtype=float)
+        kappa = np.asarray(kappa, dtype=float)
+        d = x - mu
+        zeros = np.zeros(np.broadcast_shapes(x.shape, mu.shape, kappa.shape))
+        return {
+            ("mu", "mu", "mu", "mu"): kappa * np.cos(d),
+            ("mu", "mu", "mu", "kappa"): -np.sin(d),
+            ("mu", "mu", "kappa", "kappa"): zeros,
+            ("mu", "kappa", "kappa", "kappa"): zeros,
+            ("kappa", "kappa", "kappa", "kappa"): -A1prime3(kappa),
+        }
+
     def _argcheck(self, mu, kappa):
         try:
             mu_arr, kappa_arr = np.broadcast_arrays(mu, kappa)
@@ -3321,11 +4437,13 @@ def _argcheck(self, mu, kappa):
         return (
             (mu_arr >= 0.0)
             & (mu_arr <= 2.0 * np.pi)
-            & (kappa_arr > 0.0)
+            & (kappa_arr >= 0.0)
         )
 
     def _pdf(self, x, mu, kappa):
-        return np.exp(kappa * np.cos(x - mu)) / (2 * np.pi * i0(kappa))
+        # exponentially-scaled form: e^{κ(cosφ−1)}/(2π·i0e(κ)) — the naive
+        # e^{κcosφ}/I₀(κ) pair overflows to nan/inf for κ ≥ 713
+        return np.exp(kappa * (np.cos(x - mu) - 1.0)) / (2 * np.pi * i0e(kappa))
 
     def pdf(self, x, mu, kappa, *args, **kwargs):
         r"""
@@ -3342,7 +4460,7 @@ def pdf(self, x, mu, kappa, *args, **kwargs):
         mu : float
             The mean direction of the distribution (0 <= mu <= 2*pi).
         kappa : float
-            The concentration parameter of the distribution (kappa > 0).
+            The concentration parameter of the distribution (kappa >= 0; kappa = 0 is the circular uniform limit).
 
         Returns
         -------
@@ -3352,7 +4470,10 @@ def pdf(self, x, mu, kappa, *args, **kwargs):
         return super().pdf(x, mu, kappa, *args, **kwargs)
 
     def _logpdf(self, x, mu, kappa):
-        return kappa * np.cos(x - mu) - np.log(2 * np.pi * i0(kappa))
+        # log(2πI₀(κ)) = log(2π·i0e(κ)) + κ, folded into κ(cosφ − 1) so the
+        # log-density stays finite at every κ (κ = 800 at the mode is ≈ +2.4,
+        # not −inf)
+        return kappa * (np.cos(x - mu) - 1.0) - np.log(2 * np.pi * i0e(kappa))
 
     def logpdf(self, x, mu, kappa, *args, **kwargs):
         """
@@ -3366,7 +4487,7 @@ def logpdf(self, x, mu, kappa, *args, **kwargs):
         mu : float
             The mean direction of the distribution (0 <= mu <= 2*pi).
         kappa : float
-            The concentration parameter of the distribution (kappa > 0).
+            The concentration parameter of the distribution (kappa >= 0; kappa = 0 is the circular uniform limit).
 
         Returns
         -------
@@ -3375,6 +4496,30 @@ def logpdf(self, x, mu, kappa, *args, **kwargs):
         """
         return super().logpdf(x, mu, kappa, *args, **kwargs)
 
+    def trig_moment(self, p: int = 1, *args, **kwargs) -> complex:
+        """Closed-form trigonometric moment (book §4.3.8):
+        m_p = (I_p(κ)/I₀(κ))·e^{ipμ}, evaluated with the exponentially
+        scaled ``ive`` so the Bessel ratio is exact at every κ (the raw
+        ``iv`` pair overflows from κ ≈ 713)."""
+        shape_args, non_shape_kwargs = self._separate_shape_parameters(
+            args, kwargs, "trig_moment"
+        )
+        call_kwargs = self._prepare_call_kwargs(non_shape_kwargs, "trig_moment")
+        mu, kappa = (float(np.asarray(v, dtype=float))
+                     for v in self._parse_args(*shape_args, **call_kwargs)[0])
+
+        if not np.isscalar(p):
+            raise ValueError("`p` must be an integer scalar.")
+        if int(round(p)) != p:
+            raise ValueError("`p` must be an integer.")
+        k = int(round(p))
+        if k == 0:
+            return complex(1.0, 0.0)
+        ak = abs(k)
+        ratio = float(ive(ak, kappa) / ive(0, kappa))
+        value = ratio * np.exp(1j * ak * mu)
+        return complex(np.conjugate(value)) if k < 0 else complex(value)
+
     def _cdf(self, x, mu, kappa):
         wrapped = self._wrap_angles(x)
         arr = np.asarray(wrapped, dtype=float)
@@ -3399,55 +4544,36 @@ def _cdf(self, x, mu, kappa):
             uniform = flat / two_pi
             if arr.ndim == 0:
                 value = float(uniform[0])
-                return 1.0 if np.isclose(float(wrapped), two_pi) else value
+                return 1.0 if float(wrapped) == two_pi else value
             result = uniform.reshape(arr.shape)
-            result[np.isclose(arr, two_pi)] = 1.0
+            result[arr == two_pi] = 1.0
             return result
 
-        denom = i0(kappa_val)
-        if not np.isfinite(denom) or denom == 0.0:
-            return self._cdf_from_pdf(x, mu, kappa)
-
-        phi = (flat - mu_val + np.pi) % two_pi - np.pi
-        base_phi = (-mu_val + np.pi) % two_pi - np.pi
-
-        term_sum = np.zeros_like(phi)
-        term_base = 0.0
-        tol = 1e-12
-        max_terms = 500
-        converged = False
-
-        for n in range(1, max_terms + 1):
-            coeff = iv(n, kappa_val) / (denom * n)
-            if not np.isfinite(coeff):
-                continue
-
-            term = coeff * np.sin(n * phi)
-            term_sum += term
-            term_base += coeff * np.sin(n * base_phi)
-
-            max_term = np.max(np.abs(term))
-            if max_term < tol and abs(coeff) < tol:
-                converged = True
-                break
-
-        if not converged:
-            return self._cdf_from_pdf(x, mu, kappa)
-
-        cdf_raw = 0.5 + phi / two_pi + (1.0 / np.pi) * term_sum
-        base_val = 0.5 + base_phi / two_pi + (1.0 / np.pi) * term_base
-
-        forward = np.clip(cdf_raw - base_val, 0.0, 1.0)
-        backward = np.clip(base_val - cdf_raw, 0.0, 1.0)
-        cdf = np.where(phi >= base_phi, forward, 1.0 - backward)
-        cdf = np.clip(cdf, 0.0, 1.0)
+        # Exact feature-scale GL ladder on the centered kernel — the von
+        # Mises is the ψ → 0 member of the Jones–Pewsey clan
+        # (h = κ cos φ exactly in `_jp_score_terms`), so the P1-C cdf
+        # machinery applies verbatim: the integrand e^{κ(cos φ − 1)} ≤ 1
+        # never overflows at any κ, and the 1/√κ peak panels resolve every
+        # representable concentration. This retires both defects of the
+        # former Fourier–Bessel series path (raw iv/i0 overflowed at
+        # κ ≥ 713, the 500-term cap missed for κ ≳ 3000 — both fell back
+        # to per-point quadrature, ~200 ms per call).
+        phi_c = (flat - mu_val) % two_pi
+        c0 = (-mu_val) % two_pi
+        h_q, _ = _jp_cum01(phi_c, kappa_val, 0.0, want_skew=False)
+        h_0, _ = _jp_cum01(np.array([c0]), kappa_val, 0.0, want_skew=False)
+        diff = h_q - float(h_0[0])
+        # wrap is decided by the anchor *positions*, never by the sign of
+        # the mass between them — in a concentrated dead zone that mass is
+        # float dust of either sign while the true cdf is 0 or 1.
+        cdf = np.clip(np.where(phi_c >= c0, diff, diff + 1.0), 0.0, 1.0)
 
         if arr.ndim == 0:
             value = float(cdf[0])
-            return 1.0 if np.isclose(float(wrapped), two_pi) else value
+            return 1.0 if float(wrapped) == two_pi else value
 
         result = cdf.reshape(arr.shape)
-        result[np.isclose(arr, two_pi)] = 1.0
+        result[arr == two_pi] = 1.0
         return result
 
     def cdf(self, x, mu, kappa, *args, **kwargs):
@@ -3455,17 +4581,15 @@ def cdf(self, x, mu, kappa, *args, **kwargs):
         Cumulative distribution function of the Von Mises distribution.
 
         $$
-        F(\theta) = \frac{1}{2 \pi I_0(\kappa)}\int_{0}^{\theta} e^{\kappa \cos(\theta - \mu)} dx
+        F(\theta) = \frac{1}{2 \pi I_0(\kappa)}\int_{0}^{\theta} e^{\kappa \cos(t - \mu)} dt
         $$
 
-        The CDF is evaluated via its Fourier-Bessel series expansion,
-        $$
-        F(\theta) = \frac{1}{2} + \frac{\theta - \mu}{2\pi}
-        + \frac{1}{\pi}\sum_{n=1}^{\infty} \frac{I_n(\kappa)}{I_0(\kappa)\,n}
-        \sin\bigl(n(\theta - \mu)\bigr),
-        $$
-        truncated adaptively for numerical stability and re-normalised to the
-        $[0, 2\pi)$ support.
+        The CDF is evaluated exactly by composite Gauss–Legendre quadrature
+        on the feature-scale panel ladder of the centered kernel
+        ``e^{κ(cos φ − 1)} ≤ 1`` (cached edge cumulatives plus one partial
+        panel per query) — overflow-free and quadrature-exact at every
+        representable concentration, including the regression log-link
+        regime ``κ ≫ 700`` where Bessel-ratio series fail.
 
         Parameters
         ----------
@@ -3474,7 +4598,7 @@ def cdf(self, x, mu, kappa, *args, **kwargs):
         mu : float
             The mean direction of the distribution (0 <= mu <= 2*pi).
         kappa : float
-            The concentration parameter of the distribution (kappa > 0).
+            The concentration parameter of the distribution (kappa >= 0; kappa = 0 is the circular uniform limit).
 
         Returns
         -------
@@ -3515,77 +4639,20 @@ def _ppf(self, q, mu, kappa):
             result[interior] = (two_pi * q_int) % two_pi
             return result.reshape(q_arr.shape)
 
-        eps = 1e-15
-        q_clipped = np.clip(q_int, eps, 1.0 - eps)
-
-        theta = (mu_val + two_pi * (q_clipped - 0.5)) % two_pi
-        if kappa_val < 0.3:
-            theta = (two_pi * q_clipped) % two_pi
-        elif kappa_val > 5.0:
-            normal_guess = mu_val + ndtri(q_clipped) / np.sqrt(kappa_val)
-            normal_guess = np.mod(normal_guess, two_pi)
-            blend = 0.5 if kappa_val < 20.0 else 0.8
-            theta = np.mod(blend * normal_guess + (1.0 - blend) * (two_pi * q_clipped), two_pi)
-
-        L = np.zeros_like(theta)
-        H = np.full_like(theta, two_pi)
-
-        tol_cdf = 1e-12
-        tol_theta = 1e-10
-        max_iter = 6
-
-        theta_curr = theta.copy()
-        for _ in range(max_iter):
-            cdf_vals = np.asarray(self.cdf(theta_curr, mu_val, kappa_val), dtype=float)
-            pdf_vals = np.exp(kappa_val * np.cos(theta_curr - mu_val)) / (2.0 * np.pi * i0(kappa_val))
-            delta = cdf_vals - q_clipped
-
-            L = np.where(delta <= 0.0, theta_curr, L)
-            H = np.where(delta > 0.0, theta_curr, H)
-
-            converged = (np.abs(delta) <= tol_cdf) & ((H - L) <= tol_theta)
-            if np.all(converged):
-                break
-
-            denom = np.where(pdf_vals > 1e-15, pdf_vals, 1e-15)
-            step = np.clip(delta / denom, -np.pi, np.pi)
-            theta_next = theta_curr - step
-            midpoint = 0.5 * (L + H)
-            theta_next = np.where((theta_next <= L) | (theta_next >= H), midpoint, theta_next)
-            theta_next = np.mod(theta_next, two_pi)
-            theta_curr = theta_next
-
-        delta = np.asarray(self.cdf(theta_curr, mu_val, kappa_val), dtype=float) - q_clipped
-        mask = (np.abs(delta) > tol_cdf) | ((H - L) > tol_theta)
-        if np.any(mask):
-            theta_b = theta_curr.copy()
-            L_b = L.copy()
-            H_b = H.copy()
-            for _ in range(30):
-                if not np.any(mask):
-                    break
-                mid = 0.5 * (L_b + H_b)
-                mid_vals = np.asarray(self.cdf(mid, mu_val, kappa_val), dtype=float)
-                delta_mid = mid_vals - q_clipped
-                take_upper = (delta_mid > 0.0) & mask
-                take_lower = (~take_upper) & mask
-                H_b = np.where(take_upper, mid, H_b)
-                L_b = np.where(take_lower, mid, L_b)
-                theta_b = np.where(mask, mid, theta_b)
-                mask = mask & (np.abs(delta_mid) > tol_cdf)
-            theta_curr = np.where(mask, 0.5 * (L_b + H_b), theta_b)
-
-        result[interior] = np.mod(theta_curr, two_pi)
+        # Centered-angle ladder solve (the ψ = 0 member of the JP clan):
+        # quantile-table inverse init + bracket-safeguarded Newton on the
+        # exact GL-ladder cdf — see `_jp_ppf_ladder`.
+        result[interior] = _jp_ppf_ladder(q_int, mu_val, kappa_val, 0.0)
         return result.reshape(q_arr.shape)
 
-
     def ppf(self, q, mu, kappa, *args, **kwargs):
         """
         Percent-point function (inverse of the CDF) of the Von Mises distribution.
 
-        The quantile is obtained by inverting the analytic Fourier–Bessel series
-        using a safeguarded Newton iteration with the exact von Mises PDF as the
-        slope, followed by a bisection polish.
+        Quantiles solve the exact Gauss–Legendre ladder CDF in the centered
+        angle by bracket-safeguarded Newton, initialized from the sampler's
+        quantile-table inverse, so ``ppf`` stays in exact sync with ``cdf``
+        at every representable concentration.
 
         Parameters
         ----------
@@ -3594,7 +4661,7 @@ def ppf(self, q, mu, kappa, *args, **kwargs):
         mu : float
             The mean direction of the distribution (0 <= mu <= 2*pi).
         kappa : float
-            The concentration parameter of the distribution (kappa > 0).
+            The concentration parameter of the distribution (kappa >= 0; kappa = 0 is the circular uniform limit).
 
         Returns
         -------
@@ -3685,9 +4752,6 @@ def rvs(self, size=None, random_state=None, *args, **kwargs):
         # Call the private _rvs method
         return self._rvs(mu, kappa, size=size, random_state=random_state)
 
-    def support(self, *args, **kwargs):
-        return (0, 2 * np.pi)
-
     def mean(self, *args, **kwargs):
         """
         Circular mean of the Von Mises distribution.
@@ -3696,8 +4760,13 @@ def mean(self, *args, **kwargs):
         -------
         mean : float
             The circular mean direction (in radians), equal to `mu`.
+            At the uniform limit ``kappa = 0`` (admitted per the book)
+            the mean resultant length is 0 and no mean direction exists —
+            returns ``nan``, matching the base-class convention.
         """
-        (mu, _) = self._parse_args(*args, **kwargs)[0]
+        (mu, kappa) = self._parse_args(*args, **kwargs)[0]
+        if np.isclose(A1(kappa), 0.0, atol=1e-12):
+            return float("nan")
         return mu
 
     def median(self, *args, **kwargs):
@@ -3708,8 +4777,13 @@ def median(self, *args, **kwargs):
         -------
         median : float
             The circular median direction (in radians), equal to `mu`.
+            At the uniform limit ``kappa = 0`` falls back to the
+            base-class linearized convention ``ppf(0.5)`` (= π).
         """
-        return self.mean(*args, **kwargs)
+        (mu, kappa) = self._parse_args(*args, **kwargs)[0]
+        if np.isclose(A1(kappa), 0.0, atol=1e-12):
+            return super().median(*args, **kwargs)
+        return mu
 
     def var(self, *args, **kwargs):
         """
@@ -3721,7 +4795,7 @@ def var(self, *args, **kwargs):
             The circular variance, derived from `kappa`.
         """
         (_, kappa) = self._parse_args(*args, **kwargs)[0]
-        return 1 - i1(kappa) / i0(kappa)
+        return 1 - A1(kappa)
 
     def std(self, *args, **kwargs):
         """
@@ -3730,11 +4804,13 @@ def std(self, *args, **kwargs):
         Returns
         -------
         std : float
-            The circular standard deviation, derived from `kappa`.
+            The circular standard deviation, derived from `kappa`;
+            ``inf`` at the uniform limit ``kappa = 0`` (R = 0).
         """
         (_, kappa) = self._parse_args(*args, **kwargs)[0]
-        r = i1(kappa) / i0(kappa)
-
+        r = A1(kappa)
+        if np.isclose(r, 0.0, atol=1e-12):
+            return float("inf")
         return np.sqrt(-2 * np.log(r))
 
     def entropy(self, *args, **kwargs):
@@ -3746,8 +4822,12 @@ def entropy(self, *args, **kwargs):
         entropy : float
             The entropy of the distribution.
         """
+        # H = log(2πI₀(κ)) − κ·A₁(κ), in scaled form log(2π·i0e(κ)) +
+        # κ(1 − A₁(κ)). (The previous expression −log I₀ + κA₁ had the I₀
+        # sign flipped and dropped the log 2π term — methods-parity review
+        # P1 audit, 2026-06-11; e.g. κ = 2 gave 0.572 instead of 1.266.)
         (_, kappa) = self._parse_args(*args, **kwargs)[0]
-        return -np.log(i0(kappa)) + (kappa * i1(kappa)) / i0(kappa)
+        return np.log(2 * np.pi * i0e(kappa)) + kappa * (1 - A1(kappa))
 
     def _nnlf(self, theta, data):
         """
@@ -3838,67 +4918,800 @@ def nll(params):
                 return np.inf
             cos_term = np.cos(x - mu_param)
             sum_cos = np.sum(w * cos_term)
-            log_i0_val = np.log(i0(kappa_param))
+            log_i0_val = kappa_param + np.log(i0e(kappa_param))
             return float(
                 -kappa_param * sum_cos + w_sum * (np.log(2.0 * np.pi) + log_i0_val)
             )
 
-        def grad(params):
-            mu_param, kappa_param = params
-            cos_term = np.cos(x - mu_param)
-            sin_term = np.sin(x - mu_param)
-            sum_sin = np.sum(w * sin_term)
-            sum_cos = np.sum(w * cos_term)
-            ratio = i1(kappa_param) / i0(kappa_param)
-            g_mu = kappa_param * sum_sin
-            g_kappa = -sum_cos + w_sum * ratio
-            return np.array([g_mu, g_kappa], dtype=float)
+        def grad(params):
+            mu_param, kappa_param = params
+            cos_term = np.cos(x - mu_param)
+            sin_term = np.sin(x - mu_param)
+            sum_sin = np.sum(w * sin_term)
+            sum_cos = np.sum(w * cos_term)
+            ratio = A1(kappa_param)
+            g_mu = kappa_param * sum_sin
+            g_kappa = -sum_cos + w_sum * ratio
+            return np.array([g_mu, g_kappa], dtype=float)
+
+        if method_key == "moments":
+            mu_hat = self._wrap_direction(mu_mom)
+            kappa_hat = kappa_mom
+            info = {
+                "method": "moments",
+                "loglik": float(-nll((mu_hat, kappa_hat))),
+                "n_effective": float(n_eff),
+                "converged": True,
+            }
+        else:
+            bounds = [(0.0, 2.0 * np.pi), (1e-9, 1e6)]
+            init = np.array([mu_mom, kappa_mom], dtype=float)
+            result = minimize(
+                nll,
+                init,
+                method=optimizer,
+                jac=grad,
+                bounds=bounds,
+                **kwargs,
+            )
+            if not result.success:
+                raise RuntimeError(
+                    f"vonmises.fit(method='mle') failed: {result.message}"
+                )
+            mu_hat = self._wrap_direction(float(result.x[0]))
+            kappa_hat = float(np.clip(result.x[1], 1e-9, 1e6))
+            info = {
+                "method": "mle",
+                "loglik": float(-result.fun),
+                "n_effective": float(n_eff),
+                "converged": bool(result.success),
+                "nit": result.nit,
+                "grad_norm": float(np.linalg.norm(result.jac))
+                if getattr(result, "jac", None) is not None
+                else np.nan,
+                "optimizer": optimizer,
+            }
+
+        estimates = (mu_hat, kappa_hat)
+        if return_info:
+            return estimates, info
+        return estimates
+
+
+vonmises = vonmises_gen(name="vonmises")
+
+
+def _pn_bvn_cdf(h, k, rho, s=None):
+    r"""Standard bivariate-normal cdf ``Φ₂(h, k; ρ)`` via Owen (1956).
+
+    ``Φ₂ = ½[Φ(h) + Φ(k)] − T(h, a_h) − T(k, a_k) − δ`` with
+    ``a_h = (k − ρh)/(h√(1−ρ²))`` (and symmetrically ``a_k``), where ``T``
+    is Owen's T function and ``δ = ½`` iff ``hk < 0`` or
+    (``hk = 0`` and ``h + k < 0``). Singular cells are patched explicitly:
+    ``√(1−ρ²) = 0`` (comonotone/antimonotone) and ``h = k = 0``
+    (``¼ + atan2(ρ, √(1−ρ²))/2π``, i.e. ``¼ + arcsin ρ / 2π``). When
+    exactly one of ``h, k`` is zero the Owen term is its limit
+    ``T(0, ±∞) = ±¼`` with the sign of the *other* argument — riding IEEE
+    division here is wrong, because a caller passing ``-0.0`` flips the
+    infinity's sign while δ (which compares ``== 0``) cannot compensate,
+    producing O(½) errors.
+
+    ``s`` lets the caller supply ``√(1−ρ²)`` exactly when it is known in a
+    cancellation-free form. The projected-normal wedge cdf passes
+    ``ρ = −cos θ, s = |sin θ|``: near ``θ = π`` the rounded ρ collapses to
+    exactly 1.0 over a ~1e-8-wide stretch of θ (``1 − ζ²/2`` rounds to 1
+    for ``ζ < √eps``), so deriving ``s`` from ρ would flatten the cdf
+    there, while ``|sin θ| = ζ`` keeps full precision.
+
+    Vectorized; accuracy ~1e-15 against ``scipy.stats.multivariate_normal``
+    (Owen's T itself is Patefield–Tandy).
+    """
+    if s is None:
+        s = np.sqrt(np.clip(1.0 - np.asarray(rho, dtype=float) ** 2, 0.0, None))
+    h, k, rho, s = np.broadcast_arrays(
+        np.asarray(h, dtype=float),
+        np.asarray(k, dtype=float),
+        np.asarray(rho, dtype=float),
+        np.asarray(s, dtype=float),
+    )
+    with np.errstate(divide="ignore", invalid="ignore"):
+        a_h = (k - rho * h) / (h * s)
+        a_k = (h - rho * k) / (k * s)
+    t_h = np.where(
+        h == 0.0,
+        0.25 * np.sign(k),
+        owens_t(h, np.where(np.isnan(a_h), 0.0, a_h)),
+    )
+    t_k = np.where(
+        k == 0.0,
+        0.25 * np.sign(h),
+        owens_t(k, np.where(np.isnan(a_k), 0.0, a_k)),
+    )
+    delta = np.where((h * k < 0) | ((h * k == 0) & (h + k < 0)), 0.5, 0.0)
+    val = 0.5 * (ndtr(h) + ndtr(k)) - t_h - t_k - delta
+    val = np.where((s == 0.0) & (rho > 0.0), ndtr(np.minimum(h, k)), val)
+    val = np.where(
+        (s == 0.0) & (rho <= 0.0),
+        np.clip(ndtr(h) + ndtr(k) - 1.0, 0.0, None),
+        val,
+    )
+    both_zero = (h == 0.0) & (k == 0.0) & (s > 0.0)
+    if np.any(both_zero):
+        val = np.where(
+            both_zero,
+            0.25 + np.arctan2(rho, s) / (2.0 * np.pi),
+            val,
+        )
+    return np.clip(val, 0.0, 1.0)
+
+
+class projectednormal_gen(_RegressionReady, CircularContinuous):
+    r"""Projected Normal (angular Gaussian) Distribution ``PN₂(μ, I)``.
+
+    The direction of a bivariate normal: ``Θ = atan2(X₂, X₁)`` with
+    ``X ~ N₂((mu1, mu2), I₂)``. Parameterized by the *Cartesian* mean
+    components — the mean direction is ``atan2(mu2, mu1)``, ``‖μ‖`` acts as
+    the concentration, and ``‖μ‖ = 0`` is the circular uniform. The
+    identity-covariance form is the Presnell et al. (1998) regression
+    workhorse: both components are modelled linearly in covariates, so the
+    regression overlay tags both with role ``"location"`` and identity links
+    (two linear predictors in the hea bridge).
+
+    With ``t = μ₁cos θ + μ₂sin θ`` and ``s = −μ₁sin θ + μ₂cos θ`` the density
+    factorizes,
+
+    $$f(\theta) = \varphi(s)\,[\varphi(t) + t\,\Phi(t)],$$
+
+    so every mixed (t, s) derivative of ``log f`` vanishes and the l1..l4
+    contract methods stay closed-form in one scalar function
+    ``G(t) = \log(\varphi(t) + t\Phi(t))`` plus a rotation by θ.
+
+    Methods
+    -------
+    pdf(x, mu1, mu2)
+        Probability density function (closed form).
+
+    logpdf(x, mu1, mu2)
+        Logarithm of the probability density function (closed form).
+
+    cdf(x, mu1, mu2)
+        Cumulative distribution function (closed form: bivariate-normal
+        wedge probability via Owen's T).
+
+    ppf(q, mu1, mu2)
+        Percent-point function (bracket-safeguarded Newton on the
+        closed-form CDF).
+
+    rvs(mu1, mu2, size=None, random_state=None)
+        Random variates by direct projection of bivariate normal draws.
+
+    fit(data, method="mle", ...)
+        Maximum-likelihood estimates of ``(mu1, mu2)``.
+
+    Notes
+    -----
+    General (non-identity) covariance is intentionally out of scope: it is
+    not identifiable up to scale, and the regression literature fixes Σ = I.
+
+    References
+    ----------
+    Presnell, B., Morrison, S. P., & Littell, R. C. (1998). Projected
+    multivariate linear models for directional data. *JASA* 93(443).
+    """
+
+    # --- regression overlay (Phase 1 contract): one role, two parameters —
+    # the documented one-to-many case. Both LPs use the identity link. ---
+    param_roles = {"mu1": "location", "mu2": "location"}
+    default_links = {"location": "identity"}
+
+    @staticmethod
+    def _t_s(x, mu1, mu2):
+        """Rotated coordinates: t = μᵀu(θ) (radial), s = μᵀu⊥(θ) (tangent)."""
+        c, s_ = np.cos(x), np.sin(x)
+        return mu1 * c + mu2 * s_, mu2 * c - mu1 * s_
+
+    @staticmethod
+    def _mills_inv(t):
+        """``R(t) = φ(t)/Φ(t)``, stable for all t via ``log_ndtr``
+        (t → −∞: R → |t|; t → +∞: R → 0)."""
+        return np.exp(-0.5 * t * t - 0.5 * np.log(2.0 * np.pi) - log_ndtr(t))
+
+    def _argcheck(self, mu1, mu2):
+        try:
+            mu1_arr, mu2_arr = np.broadcast_arrays(mu1, mu2)
+        except ValueError:
+            return False
+        return np.isfinite(mu1_arr) & np.isfinite(mu2_arr)
+
+    def _logpdf(self, x, mu1, mu2):
+        x = np.asarray(x, dtype=float)
+        mu1 = np.asarray(mu1, dtype=float)
+        mu2 = np.asarray(mu2, dtype=float)
+        t, s = self._t_s(x, mu1, mu2)
+        # log f = log φ(s) + log(φ(t) + tΦ(t)); the bracket equals Φ(t)(t + R)
+        # with R = φ/Φ, and t + R > 0 always — stable at both t extremes.
+        return (
+            -0.5 * s * s
+            - 0.5 * np.log(2.0 * np.pi)
+            + log_ndtr(t)
+            + np.log(t + self._mills_inv(t))
+        )
+
+    def _pdf(self, x, mu1, mu2):
+        return np.exp(self._logpdf(x, mu1, mu2))
+
+    def pdf(self, x, mu1, mu2, *args, **kwargs):
+        r"""
+        Probability density function of the Projected Normal distribution.
+
+        $$
+        f(\theta) = \varphi(s)\,[\varphi(t) + t\,\Phi(t)], \qquad
+        t = \mu_1\cos\theta + \mu_2\sin\theta,\;
+        s = -\mu_1\sin\theta + \mu_2\cos\theta,
+        $$
+
+        where $\varphi$/$\Phi$ are the standard normal pdf/cdf.
+
+        Parameters
+        ----------
+        x : array_like
+            Points at which to evaluate the probability density function.
+        mu1 : float
+            First Cartesian mean component (any real).
+        mu2 : float
+            Second Cartesian mean component (any real).
+
+        Returns
+        -------
+        pdf_values : array_like
+            Probability density function evaluated at `x`.
+        """
+        return super().pdf(x, mu1, mu2, *args, **kwargs)
+
+    def logpdf(self, x, mu1, mu2, *args, **kwargs):
+        """
+        Logarithm of the probability density function (closed form).
+
+        Parameters
+        ----------
+        x : array_like
+            Points at which to evaluate the log-PDF.
+        mu1, mu2 : float
+            Cartesian mean components (any reals).
+
+        Returns
+        -------
+        logpdf_values : array_like
+            Logarithm of the probability density function evaluated at `x`.
+        """
+        return super().logpdf(x, mu1, mu2, *args, **kwargs)
+
+    def _pdf_and_slope(self, x, mu1, mu2):
+        """Closed pdf and its θ-derivative: with ℓ(θ) = log f,
+        dℓ/dθ = s·(t + G'(t)) where G' = 1/(t + R), so f' = f·s·(t + G')."""
+        t, s = self._t_s(x, mu1, mu2)
+        f = np.exp(self._logpdf(x, mu1, mu2))
+        return f, f * s * (t + 1.0 / (t + self._mills_inv(t)))
+
+    def _wedge_cdf(self, theta, mu1, mu2):
+        r"""Closed-form cdf on already-wrapped angles in ``[0, 2π]``.
+
+        ``{Θ ≤ θ}`` is the wedge between the rays at angles 0 and θ, i.e.
+        for θ ∈ [0, π] the intersection of the half-planes ``{X₂ ≥ 0}`` and
+        ``{X₁ sin θ − X₂ cos θ ≥ 0}``; both events are linear in the
+        bivariate normal X, so
+
+        ``F(θ) = Φ₂(μ₂, μ₁ sin θ − μ₂ cos θ; −cos θ)``,
+
+        and for θ ∈ (π, 2π] the complement wedge gives
+        ``F(θ) = 1 − Φ₂(−μ₂, −(μ₁ sin θ − μ₂ cos θ); −cos θ)``.
+
+        Within ``1e-5`` of the corners θ ∈ {0, π, 2π} the Owen formula is
+        replaced by the exact second-order Taylor expansion of F: doubles
+        cannot represent ``1 ∓ cos θ`` below ~√eps while Φ₂'s
+        ρ-sensitivity diverges like ``1/√(1−ρ²)`` there, which would
+        otherwise dent the local slope by O(pdf) over a ~1e-8-wide stretch
+        (Taylor error ≤ W³|f″|/6 ~ 1e-13; seam quantization ~1e-11).
+        """
+        theta_b, mu1_b, mu2_b = np.broadcast_arrays(
+            np.asarray(theta, dtype=float),
+            np.asarray(mu1, dtype=float),
+            np.asarray(mu2, dtype=float),
+        )
+        shape = theta_b.shape
+        th = theta_b.reshape(-1).astype(float)
+        m1 = mu1_b.reshape(-1).astype(float)
+        m2 = mu2_b.reshape(-1).astype(float)
+
+        sin_t, cos_t = np.sin(th), np.cos(th)
+        k = m1 * sin_t - m2 * cos_t
+        rho = -cos_t
+        s = np.abs(sin_t)
+        val = np.empty_like(th)
+        low_m = th <= np.pi
+        if np.any(low_m):
+            val[low_m] = _pn_bvn_cdf(m2[low_m], k[low_m], rho[low_m], s[low_m])
+        high_m = ~low_m
+        if np.any(high_m):
+            val[high_m] = 1.0 - _pn_bvn_cdf(
+                np.negative(m2[high_m]), -k[high_m], rho[high_m], s[high_m]
+            )
+
+        corner_w = 1e-5
+        two_pi = 2.0 * np.pi
+        near_zero = th <= corner_w
+        near_pi = np.abs(th - np.pi) <= corner_w
+        near_two_pi = (two_pi - th) <= corner_w
+        if np.any(near_zero) or np.any(near_two_pi):
+            mask = near_zero | near_two_pi
+            f0, fp0 = self._pdf_and_slope(0.0, m1[mask], m2[mask])
+            zeta = np.where(near_zero[mask], th[mask], th[mask] - two_pi)
+            base = np.where(near_zero[mask], 0.0, 1.0)
+            val[mask] = np.clip(
+                base + zeta * f0 + 0.5 * zeta * zeta * fp0, 0.0, 1.0
+            )
+        if np.any(near_pi):
+            f_pi, fp_pi = self._pdf_and_slope(np.pi, m1[near_pi], m2[near_pi])
+            base = _pn_bvn_cdf(
+                m2[near_pi],
+                m1[near_pi] * np.sin(np.pi) + m2[near_pi],
+                1.0,
+                np.sin(np.pi),
+            )
+            zeta = th[near_pi] - np.pi
+            val[near_pi] = np.clip(
+                base + zeta * f_pi + 0.5 * zeta * zeta * fp_pi, 0.0, 1.0
+            )
+
+        return val.reshape(shape)
+
+    def _cdf(self, x, mu1, mu2):
+        wrapped = self._wrap_angles(x)
+        arr = np.asarray(wrapped, dtype=float)
+        if arr.size == 0:
+            return arr.astype(float)
+
+        mu1_arr = np.asarray(mu1, dtype=float)
+        mu2_arr = np.asarray(mu2, dtype=float)
+        cdf_vals = self._wedge_cdf(arr, mu1_arr, mu2_arr)
+
+        # _wrap_angles already snaps inputs within eps·2π of the upper
+        # endpoint to exactly 2π, so only exact equality is pinned here —
+        # an isclose() with default rtol would swallow honest upper-tail
+        # values (e.g. F = 1 − 1e-6 lives ~6e-6 below 2π).
+        two_pi = 2.0 * np.pi
+        if arr.ndim == 0:
+            value = float(cdf_vals)
+            return 1.0 if float(wrapped) == two_pi else value
+        result = np.asarray(cdf_vals, dtype=float)
+        result[arr == two_pi] = 1.0
+        return result
+
+    def cdf(self, x, mu1, mu2, *args, **kwargs):
+        r"""
+        Cumulative distribution function of the Projected Normal
+        distribution (closed form).
+
+        ``{Θ ≤ θ}`` is a wedge of the plane, so the CDF is a bivariate
+        normal orthant probability evaluated via Owen's T function:
+
+        $$
+        F(\theta) = \Phi_2\!\left(\mu_2,\;
+        \mu_1\sin\theta - \mu_2\cos\theta;\; -\cos\theta\right),
+        \qquad \theta \in [0, \pi],
+        $$
+
+        and $F(\theta) = 1 - \Phi_2(-\mu_2, -(\mu_1\sin\theta -
+        \mu_2\cos\theta); -\cos\theta)$ for $\theta \in (\pi, 2\pi]$.
+
+        Parameters
+        ----------
+        x : array_like
+            Points at which to evaluate the CDF.
+        mu1 : float
+            First Cartesian mean component (any real).
+        mu2 : float
+            Second Cartesian mean component (any real).
+
+        Returns
+        -------
+        cdf_values : array_like
+            CDF evaluated at `x`.
+        """
+        return super().cdf(x, mu1, mu2, *args, **kwargs)
+
+    def _ppf(self, q, mu1, mu2):
+        q_arr = np.asarray(q, dtype=float)
+        mu1_b, mu2_b, q_b = np.broadcast_arrays(
+            np.asarray(mu1, dtype=float), np.asarray(mu2, dtype=float), q_arr
+        )
+        flat = q_b.reshape(-1).astype(float)
+        m1 = mu1_b.reshape(-1).astype(float)
+        m2 = mu2_b.reshape(-1).astype(float)
+        two_pi = 2.0 * np.pi
+
+        if flat.size == 0:
+            return q_arr.astype(float)
+
+        def _finish(arr):
+            reshaped = arr.reshape(q_b.shape)
+            if q_arr.ndim == 0:
+                return float(reshaped)
+            return reshaped
+
+        result = np.full_like(flat, np.nan, dtype=float)
+        valid = np.isfinite(flat)
+        close_zero = valid & (flat <= 0.0)
+        close_one = valid & (flat >= 1.0)
+        result[close_zero] = 0.0
+        result[close_one] = two_pi
+
+        interior = valid & ~(close_zero | close_one)
+        if not np.any(interior):
+            return _finish(result)
+
+        q_sub = flat[interior]
+        m1_sub = m1[interior]
+        m2_sub = m2[interior]
+
+        gamma_sub = np.hypot(m1_sub, m2_sub)
+        uniform = gamma_sub <= 1e-12
+        if np.all(uniform):
+            result[interior] = two_pi * q_sub
+            return _finish(result)
+
+        # Bracket each quantile on a coarse grid of the closed cdf, then
+        # polish with bracket-safeguarded Newton (pdf is the derivative).
+        grid = np.linspace(0.0, two_pi, 33)
+        scalar_params = bool(
+            np.all(m1_sub == m1_sub[0]) and np.all(m2_sub == m2_sub[0])
+        )
+        if scalar_params:
+            f_grid = self._wedge_cdf(grid, m1_sub[0], m2_sub[0])
+            idx = np.clip(np.sum(f_grid[:, None] <= q_sub[None, :], axis=0), 1, 32)
+            f_lo = f_grid[idx - 1]
+            f_hi = f_grid[idx]
+        else:
+            f_grid = self._wedge_cdf(
+                grid[:, None], m1_sub[None, :], m2_sub[None, :]
+            )
+            idx = np.clip(np.sum(f_grid <= q_sub[None, :], axis=0), 1, 32)
+            cols = np.arange(q_sub.size)
+            f_lo = f_grid[idx - 1, cols]
+            f_hi = f_grid[idx, cols]
+        lower = grid[idx - 1]
+        upper = grid[idx]
+        span = np.clip(f_hi - f_lo, 1e-300, None)
+        theta_curr = lower + (upper - lower) * np.clip(
+            (q_sub - f_lo) / span, 0.0, 1.0
+        )
+
+        tol = 1e-15
+        max_iter = 12
+        delta = self._wedge_cdf(theta_curr, m1_sub, m2_sub) - q_sub
+        lower = np.where(delta <= 0.0, theta_curr, lower)
+        upper = np.where(delta > 0.0, theta_curr, upper)
+        act = np.flatnonzero(np.abs(delta) > tol)
+        for _ in range(max_iter):
+            if not act.size:
+                break
+            th_a = theta_curr[act]
+            lo_a = lower[act]
+            hi_a = upper[act]
+            pdf_a = np.exp(self._logpdf(th_a, m1_sub[act], m2_sub[act]))
+            step = np.clip(
+                delta[act] / np.clip(pdf_a, 1e-300, None), -np.pi, np.pi
+            )
+            th_n = th_a - step
+            th_n = np.where(
+                (th_n <= lo_a) | (th_n >= hi_a), 0.5 * (lo_a + hi_a), th_n
+            )
+            d_n = self._wedge_cdf(th_n, m1_sub[act], m2_sub[act]) - q_sub[act]
+            theta_curr[act] = th_n
+            delta[act] = d_n
+            lower[act] = np.where(d_n <= 0.0, th_n, lo_a)
+            upper[act] = np.where(d_n > 0.0, th_n, hi_a)
+            act = act[np.abs(d_n) > tol]
+
+        if act.size:
+            # bisection cleanup, compressed to the unconverged cells
+            lo_u = lower[act]
+            hi_u = upper[act]
+            m1_u = m1_sub[act]
+            m2_u = m2_sub[act]
+            q_u = q_sub[act]
+            for _ in range(60):
+                if np.all(hi_u - lo_u <= 1e-15):
+                    break
+                mid = 0.5 * (lo_u + hi_u)
+                go_up = self._wedge_cdf(mid, m1_u, m2_u) <= q_u
+                lo_u = np.where(go_up, mid, lo_u)
+                hi_u = np.where(go_up, hi_u, mid)
+            theta_curr[act] = 0.5 * (lo_u + hi_u)
+
+        theta_curr = np.where(uniform, two_pi * q_sub, theta_curr)
+        theta_curr = np.clip(theta_curr, 0.0, two_pi)
+        endpoint_mask = theta_curr >= (two_pi - 1e-12)
+        if np.any(endpoint_mask):
+            theta_curr = np.where(
+                endpoint_mask, np.nextafter(two_pi, 0.0), theta_curr
+            )
+        result[interior] = theta_curr
+        return _finish(result)
+
+    def ppf(self, q, mu1, mu2, *args, **kwargs):
+        """
+        Percent-point function (inverse CDF) of the Projected Normal
+        distribution.
+
+        Quantiles are found by inverting the closed-form Owen's-T CDF with
+        a bracket-safeguarded Newton iteration (the closed-form PDF is the
+        derivative), so ``ppf`` stays in exact sync with ``cdf``.
+
+        Parameters
+        ----------
+        q : array_like
+            Quantiles to evaluate (values in ``[0, 1]``).
+        mu1 : float
+            First Cartesian mean component (any real).
+        mu2 : float
+            Second Cartesian mean component (any real).
+
+        Returns
+        -------
+        ppf_values : array_like
+            Angles in ``[0, 2π)`` such that ``cdf(angle) = q``.
+        """
+        return super().ppf(q, mu1, mu2, *args, **kwargs)
+
+    def trig_moment(self, p: int = 1, *args, **kwargs) -> complex:
+        """First trigonometric moment in closed form (offset-normal
+        resultant): with γ = ‖μ‖,
+        m₁ = √(π/8)·γ·e^{−γ²/4}[I₀(γ²/4) + I₁(γ²/4)]·e^{i·atan2(μ₂, μ₁)},
+        evaluated with the scaled ``ive`` (the e^{−γ²/4} factor cancels
+        exactly). Higher |p| fall back to the quadrature base method."""
+        if np.isscalar(p) and int(round(p)) == p and abs(int(round(p))) <= 1:
+            shape_args, non_shape_kwargs = self._separate_shape_parameters(
+                args, kwargs, "trig_moment"
+            )
+            call_kwargs = self._prepare_call_kwargs(non_shape_kwargs, "trig_moment")
+            mu1, mu2 = (float(np.asarray(v, dtype=float))
+                        for v in self._parse_args(*shape_args, **call_kwargs)[0])
+            k = int(round(p))
+            if k == 0:
+                return complex(1.0, 0.0)
+            g2 = mu1 * mu1 + mu2 * mu2
+            R = np.sqrt(np.pi / 8.0) * np.sqrt(g2) * (
+                ive(0, 0.25 * g2) + ive(1, 0.25 * g2)
+            )
+            value = R * np.exp(1j * np.arctan2(mu2, mu1))
+            return complex(np.conjugate(value)) if k < 0 else complex(value)
+        return super().trig_moment(p, *args, **kwargs)
+
+    # --- l1..l4 (the regression contract). ℓ(t, s) = log φ(s) + G(t)
+    # separates, so the s-direction contributes ℓ_s = −s, ℓ_ss = −1 and
+    # nothing at higher order; the t-direction is G', G'', G''', G'''' with
+    # G' = 1/(t + R) and P ≡ R/(t + R); the rotation back to (μ₁, μ₂) only
+    # mixes in powers of cos θ / sin θ. ---
+
+    def dlogpdf(self, x, mu1, mu2):
+        r"""First derivatives of ``logpdf`` w.r.t. the parameters (l1).
+
+        $$\partial_{\mu_1}\ell = G'(t)\cos\theta + s\sin\theta,\qquad
+          \partial_{\mu_2}\ell = G'(t)\sin\theta - s\cos\theta,$$
+
+        with ``G'(t) = Φ(t)/(φ(t) + tΦ(t)) = 1/(t + R(t))``. Vectorizes over
+        per-observation ``mu1``/``mu2`` arrays; returns a book-named dict.
+        """
+        x = np.asarray(x, dtype=float)
+        mu1 = np.asarray(mu1, dtype=float)
+        mu2 = np.asarray(mu2, dtype=float)
+        c, s_ = np.cos(x), np.sin(x)
+        t, s = self._t_s(x, mu1, mu2)
+        g1 = 1.0 / (t + self._mills_inv(t))
+        return {
+            "mu1": g1 * c + s * s_,
+            "mu2": g1 * s_ - s * c,
+        }
+
+    def d2logpdf(self, x, mu1, mu2):
+        r"""Second derivatives of ``logpdf`` (l2) — unique unordered pairs.
+
+        In rotated coordinates the Hessian is ``diag(G''(t), −1)`` with
+        ``G'' = P − G'²`` and ``P = R/(t+R) = φ/(φ + tΦ)``; rotating back:
+
+        $$\partial^2_{\mu_1\mu_1}\ell = G''c^2 - \tilde s^2,\quad
+          \partial^2_{\mu_1\mu_2}\ell = (G'' + 1)\,c\tilde s,\quad
+          \partial^2_{\mu_2\mu_2}\ell = G''\tilde s^2 - c^2,$$
+
+        where ``c = cos θ``, ``s̃ = sin θ``.
+        """
+        x = np.asarray(x, dtype=float)
+        mu1 = np.asarray(mu1, dtype=float)
+        mu2 = np.asarray(mu2, dtype=float)
+        c, s_ = np.cos(x), np.sin(x)
+        t, _ = self._t_s(x, mu1, mu2)
+        R = self._mills_inv(t)
+        g1 = 1.0 / (t + R)
+        g2 = R * g1 - g1 * g1
+        return {
+            ("mu1", "mu1"): g2 * c * c - s_ * s_,
+            ("mu1", "mu2"): (g2 + 1.0) * c * s_,
+            ("mu2", "mu2"): g2 * s_ * s_ - c * c,
+        }
+
+    def d3logpdf(self, x, mu1, mu2):
+        r"""Third derivatives of ``logpdf`` (l3) — unique unordered triples.
+
+        Only the radial direction survives at third order
+        (``∂³_s log φ(s) = 0``), so every entry is ``G'''(t)`` times the
+        matching power of ``cos θ``/``sin θ``:
+        ``G''' = −tP − 3PG' + 2G'³``.
+        """
+        x = np.asarray(x, dtype=float)
+        mu1 = np.asarray(mu1, dtype=float)
+        mu2 = np.asarray(mu2, dtype=float)
+        c, s_ = np.cos(x), np.sin(x)
+        t, _ = self._t_s(x, mu1, mu2)
+        R = self._mills_inv(t)
+        g1 = 1.0 / (t + R)
+        P = R * g1
+        g3 = -t * P - 3.0 * P * g1 + 2.0 * g1**3
+        return {
+            ("mu1", "mu1", "mu1"): g3 * c**3,
+            ("mu1", "mu1", "mu2"): g3 * c * c * s_,
+            ("mu1", "mu2", "mu2"): g3 * c * s_ * s_,
+            ("mu2", "mu2", "mu2"): g3 * s_**3,
+        }
+
+    def d4logpdf(self, x, mu1, mu2):
+        r"""Fourth derivatives of ``logpdf`` (l4) — unique unordered
+        quadruples: ``G''''(t)`` times powers of ``cos θ``/``sin θ``, with
+        ``G'''' = P(t² − 1) + 4tPG' + 12PG'² − 3P² − 6G'⁴``. Completes the
+        contract to full-Newton depth (hea ``available_derivs = 2``).
+        """
+        x = np.asarray(x, dtype=float)
+        mu1 = np.asarray(mu1, dtype=float)
+        mu2 = np.asarray(mu2, dtype=float)
+        c, s_ = np.cos(x), np.sin(x)
+        t, _ = self._t_s(x, mu1, mu2)
+        R = self._mills_inv(t)
+        g1 = 1.0 / (t + R)
+        P = R * g1
+        g4 = (
+            P * (t * t - 1.0)
+            + 4.0 * t * P * g1
+            + 12.0 * P * g1 * g1
+            - 3.0 * P * P
+            - 6.0 * g1**4
+        )
+        return {
+            ("mu1", "mu1", "mu1", "mu1"): g4 * c**4,
+            ("mu1", "mu1", "mu1", "mu2"): g4 * c**3 * s_,
+            ("mu1", "mu1", "mu2", "mu2"): g4 * c * c * s_ * s_,
+            ("mu1", "mu2", "mu2", "mu2"): g4 * c * s_**3,
+            ("mu2", "mu2", "mu2", "mu2"): g4 * s_**4,
+        }
+
+    def _rvs(self, mu1, mu2, size=None, random_state=None):
+        rng = self._init_rng(random_state)
+
+        mu1_arr = np.asarray(mu1, dtype=float)
+        mu2_arr = np.asarray(mu2, dtype=float)
+        if mu1_arr.size != 1 or mu2_arr.size != 1:
+            raise ValueError("projectednormal parameters must be scalar-valued.")
+        mu1_val = float(mu1_arr.reshape(-1)[0])
+        mu2_val = float(mu2_arr.reshape(-1)[0])
+
+        shape = ()
+        if size is not None:
+            if np.isscalar(size):
+                shape = (int(size),)
+            else:
+                shape = tuple(int(dim) for dim in np.atleast_1d(size))
+
+        z1 = rng.standard_normal(size=shape)
+        z2 = rng.standard_normal(size=shape)
+        theta = np.mod(np.arctan2(mu2_val + z2, mu1_val + z1), 2.0 * np.pi)
+
+        if np.ndim(theta) == 0:
+            return float(theta)
+        return theta.reshape(shape)
+
+    def fit(
+        self,
+        data,
+        *,
+        weights=None,
+        method="mle",
+        return_info=False,
+        optimizer="L-BFGS-B",
+        **kwargs,
+    ):
+        """
+        Estimate ``(mu1, mu2)`` for the projected normal distribution.
+
+        Parameters
+        ----------
+        data : array_like
+            Sample angles (radians). Values are wrapped to ``[0, 2π)``
+            internally.
+        weights : array_like, optional
+            Non-negative weights broadcastable to ``data``.
+        method : {"mle"}, optional
+            Only maximum likelihood is provided (alias: "numerical"): the
+            direction of μ̂ has a closed form, but ``‖μ‖`` does not, so the
+            weighted log-likelihood is maximised directly with the analytic
+            score (``dlogpdf``) as gradient, initialised from the circular
+            mean direction and a von Mises-scale concentration.
+        return_info : bool, optional
+            If True, also return a diagnostic dictionary.
+        optimizer : str, optional
+            Optimiser passed to ``scipy.optimize.minimize``.
+        **kwargs :
+            Additional keyword arguments forwarded to the optimiser.
+        """
+        kwargs = self._clean_loc_scale_kwargs(kwargs, caller="fit")
+        x = self._wrap_angles(np.asarray(data, dtype=float))
+        if x.size == 0:
+            raise ValueError("`data` must contain at least one observation.")
+
+        if weights is None:
+            w = np.ones_like(x, dtype=float)
+        else:
+            w = np.asarray(weights, dtype=float)
+            if np.any(w < 0):
+                raise ValueError("`weights` must be non-negative.")
+            w = np.broadcast_to(w, x.shape).astype(float, copy=False)
+
+        w_sum = float(np.sum(w))
+        if not np.isfinite(w_sum) or w_sum <= 0:
+            raise ValueError("Sum of weights must be positive.")
+        n_eff = w_sum**2 / np.sum(w**2)
+
+        method_key = {"numerical": "mle"}.get(method.lower(), method.lower())
+        if method_key != "mle":
+            raise ValueError("`method` must be 'mle' (alias: 'numerical').")
+        if "algorithm" in kwargs:
+            optimizer = kwargs.pop("algorithm")
+
+        mu_dir, r_bar = circ_mean_and_r(alpha=x, w=w)
+        if not np.isfinite(mu_dir):
+            mu_dir = 0.0
+        gamma0 = max(A1inv(float(np.clip(r_bar, 0.0, 1.0 - 1e-9))), 1e-3)
+        init = np.array(
+            [gamma0 * np.cos(mu_dir), gamma0 * np.sin(mu_dir)], dtype=float
+        )
+
+        def nll(params):
+            return float(-np.sum(w * self._logpdf(x, params[0], params[1])))
 
-        if method_key == "moments":
-            mu_hat = self._wrap_direction(mu_mom)
-            kappa_hat = kappa_mom
-            info = {
-                "method": "moments",
-                "loglik": float(-nll((mu_hat, kappa_hat))),
-                "n_effective": float(n_eff),
-                "converged": True,
-            }
-        else:
-            bounds = [(0.0, 2.0 * np.pi), (1e-9, 1e6)]
-            init = np.array([mu_mom, kappa_mom], dtype=float)
-            result = minimize(
-                nll,
-                init,
-                method=optimizer,
-                jac=grad,
-                bounds=bounds,
-                **kwargs,
+        def grad(params):
+            score = self.dlogpdf(x, params[0], params[1])
+            return -np.array(
+                [np.sum(w * score["mu1"]), np.sum(w * score["mu2"])], dtype=float
             )
-            if not result.success:
-                raise RuntimeError(
-                    f"vonmises.fit(method='mle') failed: {result.message}"
-                )
-            mu_hat = self._wrap_direction(float(result.x[0]))
-            kappa_hat = float(np.clip(result.x[1], 1e-9, 1e6))
+
+        result = minimize(nll, init, method=optimizer, jac=grad, **kwargs)
+        if not result.success:
+            raise RuntimeError(f"projectednormal.fit(method='mle') failed: {result.message}")
+        mu1_hat, mu2_hat = (float(v) for v in result.x)
+
+        estimates = (mu1_hat, mu2_hat)
+        if return_info:
             info = {
                 "method": "mle",
                 "loglik": float(-result.fun),
                 "n_effective": float(n_eff),
                 "converged": bool(result.success),
                 "nit": result.nit,
-                "grad_norm": float(np.linalg.norm(result.jac))
-                if getattr(result, "jac", None) is not None
-                else np.nan,
                 "optimizer": optimizer,
             }
-
-        estimates = (mu_hat, kappa_hat)
-        if return_info:
             return estimates, info
         return estimates
 
 
-vonmises = vonmises_gen(name="vonmises")
+projectednormal = projectednormal_gen(name="projectednormal")
 
 
 class vonmises_flattopped_gen(CircularContinuous):
@@ -3915,22 +5728,37 @@ class vonmises_flattopped_gen(CircularContinuous):
     pdf(x, mu, kappa, nu)
         Probability density function.
 
+    logpdf(x, mu, kappa, nu)
+        Logarithm of the probability density function.
+
     cdf(x, mu, kappa, nu)
         Cumulative distribution function.
 
+    ppf(q, mu, kappa, nu)
+        Percent-point function (inverse CDF) through the cached table's
+        monotone inverse.
+
+    rvs(mu, kappa, nu, size=None, random_state=None)
+        Random variates by inverse transform on the cached table.
+
+    fit(data, *, weights=None, method="mle", ...)
+        Estimate ``(mu, kappa, nu)`` by moments seed + maximum likelihood.
+
     Note
     ----
     Parameters must be scalar; cached normalization tables are built per parameter set.
-    Implementation based on Section 4.3.10 of Pewsey et al. (2014)
+    Implementation based on Section 4.3.10 of Pewsey et al. (2013)
     """
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self._vmft_table_cache = {}
-        self._vmft_sampler_cache = {}
 
-    def _validate_params(self, mu, kappa, nu):
-        mu_arr, kappa_arr, nu_arr = np.broadcast_arrays(mu, kappa, nu)
+    def _argcheck(self, mu, kappa, nu):
+        try:
+            mu_arr, kappa_arr, nu_arr = np.broadcast_arrays(mu, kappa, nu)
+        except ValueError:
+            return False
         return (
             (mu_arr >= 0.0)
             & (mu_arr <= 2.0 * np.pi)
@@ -3940,16 +5768,9 @@ def _validate_params(self, mu, kappa, nu):
             & (nu_arr <= 1.0)
         )
 
-    def _argcheck(self, mu, kappa, nu):
-        try:
-            return self._validate_params(mu, kappa, nu)
-        except ValueError:
-            return False
-
     def _clear_normalization_cache(self):
         super()._clear_normalization_cache()
         self._vmft_table_cache = {}
-        self._vmft_sampler_cache = {}
 
     def _pdf(self, x, mu, kappa, nu):
         x_arr = np.asarray(x, dtype=float)
@@ -4018,6 +5839,55 @@ def pdf(self, x, mu, kappa, nu, *args, **kwargs):
         nu_val = _vmft_ensure_scalar(nu, "nu")
         return super().pdf(x, mu_val, kappa_val, nu_val, *args, **kwargs)
 
+    def _logpdf(self, x, mu, kappa, nu):
+        # the same log-space assembly ``_pdf`` exponentiates (log-kernel +
+        # cached table log-normalizer), returned before the exp so the
+        # antipodal tail stays finite at concentrations where the density
+        # underflows (κ ≳ 360 for ν = 0)
+        x_arr = np.asarray(x, dtype=float)
+        mu_val = _vmft_ensure_scalar(mu, "mu")
+        kappa_val = float(np.clip(_vmft_ensure_scalar(kappa, "kappa"), 0.0, _VMFT_KAPPA_UPPER))
+        nu_val = _vmft_ensure_scalar(nu, "nu")
+
+        if not np.isfinite(mu_val) or not np.isfinite(kappa_val) or not np.isfinite(nu_val):
+            return np.full_like(x_arr, np.nan, dtype=float)
+
+        if kappa_val <= _VMFT_KAPPA_TOL:
+            return np.full_like(x_arr, -np.log(2.0 * np.pi), dtype=float)
+
+        table = self._get_vmft_table(kappa_val, nu_val)
+        phi = ((x_arr - mu_val + np.pi) % (2.0 * np.pi)) - np.pi
+        return kappa_val * np.cos(phi + nu_val * np.sin(phi)) + table["log_normalizer"]
+
+    def logpdf(self, x, mu, kappa, nu, *args, **kwargs):
+        r"""
+        Logarithm of the probability density function of the flat-topped
+        von Mises distribution: the log-kernel
+        $\kappa\cos(\phi + \nu\sin\phi)$ plus the cached log-normalizer —
+        finite across the advertised parameter range, including tails where
+        the density itself underflows.
+
+        Parameters
+        ----------
+        x : array_like
+            Points at which to evaluate the log-density.
+        mu : float
+            Mean direction, 0 <= mu <= 2*pi.
+        kappa : float
+            Concentration parameter, 0 <= kappa <= 1e3.
+        nu : float
+            Shape parameter, -1 <= nu <= 1.
+
+        Returns
+        -------
+        logpdf_values : array_like
+            Logarithm of the probability density function evaluated at `x`.
+        """
+        mu_val = _vmft_ensure_scalar(mu, "mu")
+        kappa_val = float(np.clip(_vmft_ensure_scalar(kappa, "kappa"), 0.0, _VMFT_KAPPA_UPPER))
+        nu_val = _vmft_ensure_scalar(nu, "nu")
+        return super().logpdf(x, mu_val, kappa_val, nu_val, *args, **kwargs)
+
     def _cdf(self, x, mu, kappa, nu):
         wrapped = self._wrap_angles(x)
         arr = np.asarray(wrapped, dtype=float)
@@ -4220,36 +6090,23 @@ def _rvs(self, mu, kappa, nu, size=None, random_state=None):
                 return float(samples)
             return samples
 
+        # Inverse-transform through the spectral cdf table: the centered
+        # quantile function maps U(0,1) draws straight to angles (the μ
+        # shift is absorbed by uniformity), one vectorized Pchip evaluation
+        # per call. This replaced the curvature-matched rejection sampler,
+        # whose von Mises envelope κ_e = κ(1+ν)² under-covers a flat-topped
+        # target's shoulders — the acceptance multiplier exploded with κ
+        # for ν ≠ 0 (12 s for 5 draws at κ = 5, ν = 0.5; an effective hang
+        # from κ ≈ 100; methods-parity P1 follow-up).
         table = self._get_vmft_table(kappa_val, nu_val)
-        sampler_params = self._get_vmft_sampler_params(kappa_val, nu_val)
-        kappa_env = sampler_params["kappa_env"]
-        log_env_norm = sampler_params["log_env_norm"]
-        log_multiplier = sampler_params["log_multiplier"]
-
-        samples = np.empty(total, dtype=float)
-        filled = 0
-        batch_base = max(8, min(4 * total, 4096))
-
-        while filled < total:
-            batch = min(batch_base, total - filled) if filled > 0 else batch_base
-            proposals = rng.vonmises(mu_val, kappa_env, size=batch)
-            phi = ((proposals - mu_val + np.pi) % two_pi) - np.pi
-
-            log_target = kappa_val * np.cos(phi + nu_val * np.sin(phi)) + table["log_normalizer"]
-            log_env = kappa_env * np.cos(phi) - log_env_norm
-            log_accept = log_target - log_env - log_multiplier
-
-            accept_mask = np.log(rng.random(size=batch)) <= log_accept
-            if not np.any(accept_mask):
-                continue
-
-            accepted = proposals[accept_mask]
-            take = min(accepted.size, total - filled)
-            samples[filled : filled + take] = accepted[:take]
-            filled += take
-
-        samples = np.mod(samples, two_pi)
-        samples = samples.reshape(shape)
+        inv_interp = table["inv_cdf_interp"]
+        u = rng.random(size=total)
+        if inv_interp is not None:
+            phi = np.clip(np.asarray(inv_interp(u), dtype=float), -np.pi, np.pi)
+        else:  # degenerate single-knot cdf: effectively uniform
+            phi = (u - 0.5) * two_pi
+
+        samples = np.mod(mu_val + phi, two_pi).reshape(shape)
         if samples.ndim == 0:
             return float(samples)
         return samples
@@ -4258,14 +6115,15 @@ def rvs(self, mu=None, kappa=None, nu=None, size=None, random_state=None):
         r"""
         Draw random variates from the flat-topped von Mises distribution.
 
-        Sampling uses an acceptance–rejection scheme with a curvature-matched
-        von Mises envelope. Writing $\phi = \theta - \mu$ and matching the
-        curvature at the mode yields a proposal concentration
-        $\kappa_e = \kappa(1+\nu)^2$ (clipped to a small positive value). The
-        envelope constant $M \ge \sup_\phi f(\phi)/g(\phi)$ is precomputed on
-        the same spectral grid used for `cdf`, so once calibrated the
-        sampler draws each variate with a single von Mises proposal followed by
-        a scalar acceptance test.
+        Sampling is by inverse transform through the same spectral cdf table
+        that serves `cdf`/`ppf`: uniform draws are pushed through the
+        monotone inverse-cdf interpolant of the centered density and shifted
+        by $\mu$ — one vectorized evaluation per call, with cost independent
+        of $\kappa$ and $\nu$. (An earlier acceptance–rejection scheme with
+        a curvature-matched von Mises envelope degraded catastrophically for
+        $\nu \ne 0$ at large $\kappa$: matching the mode curvature
+        under-covers a flat-topped density's shoulders, so the envelope
+        constant grew without bound.)
 
         Parameters
         ----------
@@ -4551,35 +6409,6 @@ def _get_vmft_table(self, kappa, nu, grid_size=None):
             self._vmft_table_cache[key] = table
         return table
 
-    def _get_vmft_sampler_params(self, kappa, nu):
-        key = (float(kappa), float(nu))
-        params = self._vmft_sampler_cache.get(key)
-        if params is not None:
-            return params
-
-        table = self._get_vmft_table(kappa, nu)
-        kappa_env = float(np.clip(kappa * (1.0 + nu) ** 2, _VMFT_ENV_MIN_KAPPA, _VMFT_KAPPA_UPPER))
-
-        log_env_norm = (
-            np.log(2.0 * np.pi)
-            + np.log(i0e(kappa_env))
-            + kappa_env
-        )
-        log_env_pdf = kappa_env * np.cos(table["phi"]) - log_env_norm
-        log_ratio = np.log(table["pdf"]) - log_env_pdf
-        log_multiplier = float(np.max(log_ratio))
-        multiplier = float(np.exp(log_multiplier) * (1.0 + 5e-12))
-
-        params = {
-            "kappa_env": kappa_env,
-            "log_env_norm": float(log_env_norm),
-            "log_multiplier": float(np.log(multiplier)),
-            "multiplier": multiplier,
-        }
-        self._vmft_sampler_cache[key] = params
-        return params
-
-
 vonmises_flattopped = vonmises_flattopped_gen(name="vonmises_flattopped")
 
 ##############################################
@@ -4628,11 +6457,12 @@ def _vmft_build_table(kappa, nu, grid_size):
 
     cdf_interp = PchipInterpolator(phi, cumulative, extrapolate=True)
 
-    unique_vals, unique_idx = np.unique(cumulative, return_index=True)
-    if unique_vals.size >= 2:
-        inv_interp = PchipInterpolator(unique_vals, phi[unique_idx], extrapolate=True)
-    else:
-        inv_interp = None
+    knots = _inverse_cdf_knots(phi, cumulative)
+    inv_interp = (
+        PchipInterpolator(knots[0], knots[1], extrapolate=True)
+        if knots is not None
+        else None
+    )
 
     return {
         "phi": phi,
@@ -4674,7 +6504,7 @@ def _vmft_ensure_scalar(value, name):
     )
 
 
-class jonespewsey_gen(CircularContinuous):
+class jonespewsey_gen(_RegressionReady, CircularContinuous):
     """Jones-Pewsey Distribution
 
     ![jonespewsey](../images/circ-mod-jonespewsey.png)
@@ -4684,23 +6514,97 @@ class jonespewsey_gen(CircularContinuous):
     pdf(x, mu, kappa, psi)
         Probability density function.
 
+    logpdf(x, mu, kappa, psi)
+        Logarithm of the probability density function.
+
     cdf(x, mu, kappa, psi)
         Cumulative distribution function.
 
+    ppf(q, mu, kappa, psi)
+        Percent-point function (inverse CDF).
+
+    rvs(mu, kappa, psi, size=None, random_state=None)
+        Random variates by inverse transform on the kernel quantile table.
+
+    fit(data, *, weights=None, method="mle", ...)
+        Estimate ``(mu, kappa, psi)`` by moments or maximum likelihood.
 
     Note
     ----
-    Parameters must be scalar; cached normalisation tables are built per parameter set.
-    Implementation based on Section 4.3.9 of Pewsey et al. (2014)
+    Scalar parameters use cached normalisation tables; ``pdf``/``logpdf`` also
+    accept per-observation parameter arrays (the regression contract),
+    normalised through the shared log-space Gauss–Legendre ladder
+    (``_jp_log_c_vec``). Other methods (cdf, rvs, …) remain scalar-only.
+    Implementation based on Section 4.3.9 of Pewsey et al. (2013)
     """
 
+    # --- regression overlay (Phase 1 contract; read by the regression engine
+    # only). Book names mu/kappa/psi are preserved; ψ ∈ ℝ indexes the family
+    # shape (−1 wrapped Cauchy, 0 von Mises, +1 cardioid), so it rides an
+    # identity link. l1/l2 split as kernel terms (`_jp_score_terms`, exact
+    # closed forms) minus log-normalizer moments (`_jp_logZ_moments_vec`, one
+    # Gauss–Legendre sweep per unique (κ, ψ) pair). Note the derivatives stay
+    # exact below _JP_KAPPA_TOL while ``logpdf`` flattens to the uniform
+    # value there — the value gap is O(κ) ≤ 1e-3 relative, and live scores in
+    # that corner let the optimizer escape it. ---
+    param_roles = {"mu": "location", "kappa": "concentration", "psi": "shape"}
+    default_links = {
+        "location": "tanhalf",
+        "concentration": "log",
+        "shape": "identity",
+    }
+
+    def dlogpdf(self, x, mu, kappa, psi):
+        r"""First derivatives of ``logpdf`` w.r.t. the parameters (l1).
+
+        With ``h`` the log-kernel and ``Z`` the normalizer:
+
+        $$\frac{\partial\ell}{\partial\mu} = -h_\phi,\qquad
+          \frac{\partial\ell}{\partial\kappa} = h_\kappa -
+          \mathbb{E}[h_\kappa],\qquad
+          \frac{\partial\ell}{\partial\psi} = h_\psi - \mathbb{E}[h_\psi],$$
+
+        the expectations under the JP density itself (one quadrature per
+        unique (κ, ψ); see ``_jp_logZ_moments``). Vectorizes over
+        per-observation parameter arrays; returns a book-named dict.
+        """
+        x = np.asarray(x, dtype=float)
+        mu_b, kappa_b, psi_b = (
+            np.asarray(v, dtype=float) for v in (mu, kappa, psi)
+        )
+        t = _jp_score_terms(x - mu_b, kappa_b, psi_b, second=False)
+        dk, dp, *_ = _jp_logZ_moments_vec(kappa_b, psi_b)
+        return {"mu": -t["hphi"], "kappa": t["hk"] - dk, "psi": t["hp"] - dp}
+
+    def d2logpdf(self, x, mu, kappa, psi):
+        r"""Second derivatives of ``logpdf`` (l2) — unique unordered pairs:
+        kernel terms minus the log-normalizer's covariance-form second
+        moments, ``∂²log Z/∂θ_a∂θ_b = E[h_{ab} + h_a h_b] − E[h_a]E[h_b]``
+        (μ never enters Z)."""
+        x = np.asarray(x, dtype=float)
+        mu_b, kappa_b, psi_b = (
+            np.asarray(v, dtype=float) for v in (mu, kappa, psi)
+        )
+        t = _jp_score_terms(x - mu_b, kappa_b, psi_b, second=True)
+        _, _, dkk, dkp, dpp = _jp_logZ_moments_vec(kappa_b, psi_b)
+        return {
+            ("mu", "mu"): t["hphiphi"],
+            ("mu", "kappa"): -t["hphik"],
+            ("mu", "psi"): -t["hphip"],
+            ("kappa", "kappa"): t["hkk"] - dkk,
+            ("kappa", "psi"): t["hkp"] - dkp,
+            ("psi", "psi"): t["hpp"] - dpp,
+        }
+
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self._sampler_cache = {}
         self._series_cache = {}
 
-    def _validate_params(self, mu, kappa, psi):
-        mu_arr, kappa_arr, psi_arr = np.broadcast_arrays(mu, kappa, psi)
+    def _argcheck(self, mu, kappa, psi):
+        try:
+            mu_arr, kappa_arr, psi_arr = np.broadcast_arrays(mu, kappa, psi)
+        except ValueError:
+            return False
         return (
             (mu_arr >= 0.0)
             & (mu_arr <= 2.0 * np.pi)
@@ -4709,16 +6613,27 @@ def _validate_params(self, mu, kappa, psi):
             & np.isfinite(psi_arr)
         )
 
-    def _argcheck(self, mu, kappa, psi):
-        try:
-            return self._validate_params(mu, kappa, psi)
-        except ValueError:
-            return False
-
     def _pdf(self, x, mu, kappa, psi):
         x = np.asarray(x, dtype=float)
-        kappa_scalar = _jp_ensure_scalar(kappa, "kappa")
-        psi_scalar = _jp_ensure_scalar(psi, "psi")
+        kappa_scalar = _jp_as_scalar(kappa)
+        psi_scalar = _jp_as_scalar(psi)
+
+        if kappa_scalar is None or psi_scalar is None:
+            # Per-observation (κ_i, ψ_i) — the regression contract path
+            # (concentration smoothing on a shape family). Assembled in log
+            # space: the raw kernel peaks at e^κ and overflows for κ ≳ 709
+            # (methods-parity P1). Uniform/von-Mises reductions applied
+            # element-wise to match the scalar branch exactly.
+            mu_b, kappa_b, psi_b = np.broadcast_arrays(
+                *(np.asarray(a, dtype=float) for a in (mu, kappa, psi))
+            )
+            phi = x - mu_b
+            logc = _jp_log_c_vec(kappa_b, psi_b)
+            h = _jp_score_terms(phi, kappa_b, psi_b, second=False)["h"]
+            vm = np.abs(psi_b) < _JP_PSI_TOL
+            h = np.where(vm, kappa_b * np.cos(phi), h)
+            dens = np.exp(h + logc)
+            return np.where(kappa_b < _JP_KAPPA_TOL, 1.0 / (2.0 * np.pi), dens)
 
         if not np.isfinite(kappa_scalar) or not np.isfinite(psi_scalar):
             return np.full_like(x, np.nan, dtype=float)
@@ -4726,18 +6641,18 @@ def _pdf(self, x, mu, kappa, psi):
         if abs(kappa_scalar) < _JP_KAPPA_TOL:
             return np.full_like(x, 1.0 / (2.0 * np.pi), dtype=float)
 
-        normalizer = self._get_cached_normalizer(
-            lambda: _c_jonespewsey(mu, kappa_scalar, psi_scalar),
-            mu,
+        log_c = self._get_cached_normalizer(
+            lambda: _jp_log_c(kappa_scalar, psi_scalar),
             kappa_scalar,
             psi_scalar,
         )
-        self._c = normalizer
+        self._c = float(np.exp(log_c))  # legacy attribute (write-only)
 
         if abs(psi_scalar) < _JP_PSI_TOL:
-            return normalizer * np.exp(kappa_scalar * np.cos(x - mu))
+            return np.exp(kappa_scalar * np.cos(x - mu) + log_c)
 
-        return normalizer * _kernel_jonespewsey(x, mu, kappa_scalar, psi_scalar)
+        h = _jp_score_terms(x - mu, kappa_scalar, psi_scalar, second=False)["h"]
+        return np.exp(h + log_c)
 
     def pdf(self, x, mu, kappa, psi, *args, **kwargs):
         r"""
@@ -4772,6 +6687,106 @@ def pdf(self, x, mu, kappa, psi, *args, **kwargs):
         """
         return super().pdf(x, mu, kappa, psi, *args, **kwargs)
 
+    def _logpdf(self, x, mu, kappa, psi):
+        # the log-space assembly ``_pdf`` exponentiates, returned before the
+        # exp (P2): h from ``_jp_score_terms`` is stable at any κ and the
+        # log-normalizer already exists, so the antipodal tail stays finite
+        # (e.g. ≈ −1200 at κ=600, ψ=0.1) where the density underflows — and
+        # finite at the deep ψ < 0 mode where the density overflows to inf.
+        # Mirrors ``_pdf`` branch-for-branch, incl. the per-observation
+        # regression path.
+        x = np.asarray(x, dtype=float)
+        kappa_scalar = _jp_as_scalar(kappa)
+        psi_scalar = _jp_as_scalar(psi)
+
+        if kappa_scalar is None or psi_scalar is None:
+            mu_b, kappa_b, psi_b = np.broadcast_arrays(
+                *(np.asarray(a, dtype=float) for a in (mu, kappa, psi))
+            )
+            phi = x - mu_b
+            logc = _jp_log_c_vec(kappa_b, psi_b)
+            h = _jp_score_terms(phi, kappa_b, psi_b, second=False)["h"]
+            vm = np.abs(psi_b) < _JP_PSI_TOL
+            h = np.where(vm, kappa_b * np.cos(phi), h)
+            return np.where(
+                kappa_b < _JP_KAPPA_TOL, -np.log(2.0 * np.pi), h + logc
+            )
+
+        if not np.isfinite(kappa_scalar) or not np.isfinite(psi_scalar):
+            return np.full_like(x, np.nan, dtype=float)
+
+        if abs(kappa_scalar) < _JP_KAPPA_TOL:
+            return np.full_like(x, -np.log(2.0 * np.pi), dtype=float)
+
+        log_c = self._get_cached_normalizer(
+            lambda: _jp_log_c(kappa_scalar, psi_scalar),
+            kappa_scalar,
+            psi_scalar,
+        )
+
+        if abs(psi_scalar) < _JP_PSI_TOL:
+            return kappa_scalar * np.cos(x - mu) + log_c
+
+        h = _jp_score_terms(x - mu, kappa_scalar, psi_scalar, second=False)["h"]
+        return h + log_c
+
+    def logpdf(self, x, mu, kappa, psi, *args, **kwargs):
+        r"""
+        Logarithm of the probability density function of the Jones-Pewsey
+        distribution: the stable log-kernel
+        $h(\theta; \kappa, \psi) = \tfrac{1}{\psi}\log(\cosh\kappa\psi +
+        \sinh\kappa\psi\cos(\theta-\mu))$ plus the log normalizing constant,
+        assembled entirely in log space — finite in underflowed tails and at
+        deep $\psi < 0$ modes whose density exceeds the double range.
+
+        Accepts per-observation parameter arrays like ``pdf`` (the
+        regression contract).
+
+        Parameters
+        ----------
+        x : array_like
+            Points at which to evaluate the log-density.
+        mu : float
+            Mean direction, 0 <= mu <= 2*pi.
+        kappa : float
+            Concentration parameter, kappa >= 0.
+        psi : float
+            Shape parameter.
+
+        Returns
+        -------
+        logpdf_values : array_like
+            Logarithm of the probability density function evaluated at `x`.
+        """
+        return super().logpdf(x, mu, kappa, psi, *args, **kwargs)
+
+    def trig_moment(self, p: int = 1, *args, **kwargs) -> complex:
+        """Trigonometric moment via the exact ladder cosine moments: the
+        centered JP law is even, so m_p = ᾱ_p·e^{ipμ} with ᾱ_p from
+        ``_jp_cos_moment`` — our own evaluation of the off-cut Legendre
+        ratio P_{1/ψ}-type/(2π P_{1/ψ})(cosh κψ) through its
+        Mehler–Dirichlet integral representation on the feature-scale
+        ladder (scipy's ``lpmv`` is the on-cut Ferrers function and
+        silently fails off the cut; see ``_jp_log_c``). Exact at any
+        concentration; vM and uniform limits closed-form."""
+        shape_args, non_shape_kwargs = self._separate_shape_parameters(
+            args, kwargs, "trig_moment"
+        )
+        call_kwargs = self._prepare_call_kwargs(non_shape_kwargs, "trig_moment")
+        mu, kappa, psi = (float(np.asarray(v, dtype=float))
+                          for v in self._parse_args(*shape_args, **call_kwargs)[0])
+
+        if not np.isscalar(p):
+            raise ValueError("`p` must be an integer scalar.")
+        if int(round(p)) != p:
+            raise ValueError("`p` must be an integer.")
+        k = int(round(p))
+        if k == 0:
+            return complex(1.0, 0.0)
+        ak = abs(k)
+        value = _jp_cos_moment(kappa, psi, ak) * np.exp(1j * ak * mu)
+        return complex(np.conjugate(value)) if k < 0 else complex(value)
+
     def _cdf(self, x, mu, kappa, psi):
         wrapped = self._wrap_angles(x)
         arr = np.asarray(wrapped, dtype=float)
@@ -4792,17 +6807,27 @@ def _cdf(self, x, mu, kappa, psi):
         if abs(psi_val) < _JP_PSI_TOL:
             return vonmises.cdf(arr, mu=mu_val, kappa=kappa_val)
 
-        try:
-            n_idx, coeffs = self._jp_get_series(kappa_val, psi_val)
-        except Exception:  # pragma: no cover - defensive fallback
-            cdf_vals = self._cdf_from_pdf(arr, mu_val, kappa_val, psi_val)
-            return np.asarray(cdf_vals, dtype=float).reshape(arr.shape)
-
         phi_start = (-mu_val) % two_pi
         phi_end = (flat - mu_val) % two_pi
 
-        H_start = float(self._jp_series_cumulative(np.array([phi_start]), n_idx, coeffs)[0])
-        H_end = self._jp_series_cumulative(phi_end, n_idx, coeffs)
+        if _jp_cdf_use_ladder(kappa_val, psi_val):
+            # deep ψ < 0: exact ladder cumulative (P1-C) — the series
+            # grid cannot resolve the spike there
+            H_start = float(
+                _jp_cum01(
+                    np.array([phi_start]), kappa_val, psi_val, want_skew=False
+                )[0][0]
+            )
+            H_end = _jp_cum01(phi_end, kappa_val, psi_val, want_skew=False)[0]
+        else:
+            try:
+                n_idx, coeffs = self._jp_get_series(kappa_val, psi_val)
+            except Exception:  # pragma: no cover - defensive fallback
+                cdf_vals = self._cdf_from_pdf(arr, mu_val, kappa_val, psi_val)
+                return np.asarray(cdf_vals, dtype=float).reshape(arr.shape)
+
+            H_start = float(self._jp_series_cumulative(np.array([phi_start]), n_idx, coeffs)[0])
+            H_end = self._jp_series_cumulative(phi_end, n_idx, coeffs)
 
         cdf = np.where(
             phi_end >= phi_start,
@@ -4826,7 +6851,11 @@ def cdf(self, x, mu, kappa, psi, *args, **kwargs):
         Coefficients are cached per parameter set and the routine falls back to
         numerical quadrature only when the series becomes unstable,
         reproducing the von Mises limit as $\psi \to 0$ and the uniform limit
-        as $\kappa \to 0$.
+        as $\kappa \to 0$. For deep $\psi < 0$ spikes beyond the series
+        resolution (roughly $\kappa\psi \lesssim -3$) the routine switches to
+        an exact composite Gauss--Legendre cumulative on the same
+        feature-scale ladder that serves the normalizer and the sampler, so
+        CDF values stay correct at any representable spike depth.
 
         Parameters
         ----------
@@ -4876,6 +6905,13 @@ def _ppf(self, q, mu, kappa, psi):
                 elif abs(psi_val) < _JP_PSI_TOL:
                     vm = vonmises(kappa=kappa_val, mu=mu_val)
                     theta_vals[interior] = vm.ppf(q_clipped)
+                elif _jp_cdf_use_ladder(kappa_val, psi_val):
+                    # deep ψ < 0: table-initialized exact solve (P1-C) —
+                    # the generic Newton scaffold cannot land inside a
+                    # sub-resolution spike from a uniform start
+                    theta_vals[interior] = _jp_ppf_ladder(
+                        q_clipped, mu_val, kappa_val, psi_val
+                    )
                 else:
                     theta_curr = two_pi * q_clipped
                     L = np.zeros_like(theta_curr)
@@ -4956,6 +6992,9 @@ def ppf(self, q, mu, kappa, psi, *args, **kwargs):
         slope.  Bracketing and bisection polishing guarantee convergence on the
         circular interval [0, 2π] while the implementation switches to the
         closed-form von Mises or uniform solutions in their respective limits.
+        For deep ψ < 0 spikes the solve runs in the centered angle against the
+        exact ladder CDF instead, initialized by the sampler's quantile-table
+        inverse, which lands inside the spike at any representable depth.
 
         Parameters
         ----------
@@ -4997,37 +7036,25 @@ def _rvs(self, mu, kappa, psi, size=None, random_state=None):
         if abs(psi_val) < _JP_PSI_TOL:
             return vonmises.rvs(mu=mu_val, kappa=kappa_val, size=size_tuple or None, random_state=rng)
 
-        kappa_env, envelope_const = self._jp_sampler_envelope(mu_val, kappa_val, psi_val)
-        samples = np.empty(total, dtype=float)
-        filled = 0
-
-        while filled < total:
-            remaining = total - filled
-            proposals = vonmises.rvs(
-                mu=mu_val,
-                kappa=kappa_env,
-                size=remaining,
-                random_state=rng,
-            )
-            target_vals = self.pdf(proposals, mu_val, kappa_val, psi_val)
-            proposal_vals = vonmises.pdf(proposals, mu=mu_val, kappa=kappa_env)
-            ratio = np.where(proposal_vals > 0.0, target_vals / (envelope_const * proposal_vals), 0.0)
-            u = rng.uniform(0.0, 1.0, size=remaining)
-            accept = ratio >= u
-            n_accept = int(np.sum(accept))
-            if n_accept > 0:
-                samples[filled:filled + n_accept] = proposals[accept][:n_accept]
-                filled += n_accept
-
+        # Inverse transform on the ladder-graded kernel table — exact at
+        # any spike depth, one vectorized pass, no rejection loop. (The
+        # previous von Mises rejection envelope was calibrated on a
+        # 2048-point uniform grid, which cannot see the ψ < 0 spike once
+        # it is narrower than ~6e-3: draws were distributionally wrong
+        # from κψ ≈ −6 — KS p ~ 1e-113 at (κ=8, ψ=−1) — and the
+        # acceptance rate collapsed into an effective hang by κψ ≈ −50.)
+        phi_draws = _jp_sample_table(kappa_val, psi_val, total, rng)
+        samples = np.mod(mu_val + phi_draws, two_pi)
         return samples.reshape(size_tuple)
 
     def rvs(self, mu, kappa, psi, size=None, random_state=None):
         r"""
         Draw random variates from the Jones-Pewsey distribution.
 
-        A von Mises envelope is tuned to the target density via local curvature
-        matching and a grid-based optimisation, yielding an acceptance-rejection
-        sampler that is both exact and efficient across the parameter space.
+        Sampling is by inverse transform through a cached quantile table of
+        the centered kernel on a feature-scale-graded grid (the same
+        break-point ladder that serves the normalizer), so cost and
+        correctness are independent of how narrow the ψ < 0 spike gets.
 
         Parameters
         ----------
@@ -5045,29 +7072,6 @@ def rvs(self, mu, kappa, psi, size=None, random_state=None):
         """
         return super().rvs(mu, kappa, psi, size=size, random_state=random_state)
 
-    def _jp_sampler_envelope(self, mu, kappa, psi):
-        key = (float(np.mod(mu, 2.0 * np.pi)), float(kappa), float(psi))
-        cached = self._sampler_cache.get(key)
-        if cached is not None:
-            return cached
-
-        kappa_env = _jp_effective_kappa(kappa, psi)
-        phi_grid = np.linspace(0.0, 2.0 * np.pi, 2048, endpoint=False)
-        theta_grid = np.mod(mu + phi_grid, 2.0 * np.pi)
-
-        target_vals = self.pdf(theta_grid, mu, kappa, psi)
-        log_target = np.log(np.clip(target_vals, np.finfo(float).tiny, None))
-
-        kappa_env, envelope_const = _optimize_vonmises_envelope(
-            theta_grid,
-            log_target,
-            mu,
-            max(kappa_env, 1e-6),
-        )
-
-        self._sampler_cache[key] = (kappa_env, envelope_const)
-        return kappa_env, envelope_const
-
     def _jp_get_series(self, kappa, psi, max_harmonics=256, grid_size=4096):
         key = (float(kappa), float(psi))
         cached = self._series_cache.get(key)
@@ -5299,163 +7303,619 @@ def nll(params):
 
 _JP_KAPPA_TOL = 1e-3
 _JP_PSI_TOL = 1e-6
-_JP_MIN_BASE = np.finfo(float).tiny
-_JP_MAX_EXP_ARGUMENT = 350.0  # guard for exp overflow
 
 
-def _jp_ensure_scalar(value, name):
+def _jp_as_scalar(value):
+    """The single value of an effectively-scalar parameter, else ``None``
+    (meaning genuinely per-observation)."""
     arr = np.asarray(value, dtype=float)
-    if arr.ndim == 0:
-        return float(arr)
-    if arr.size == 1:
+    if arr.ndim == 0 or arr.size == 1:
         return float(arr.reshape(()))
     unique = np.unique(arr)
-    if unique.size == 1:
-        return float(unique[0])
-    raise ValueError(
-        f"Jones-Pewsey parameter '{name}' must be scalar; "
-        "vectorised parameters are not supported because normalization tables are cached per parameter."
-    )
+    return float(unique[0]) if unique.size == 1 else None
+
 
+def _jp_ensure_scalar(value, name):
+    scalar = _jp_as_scalar(value)
+    if scalar is None:
+        raise ValueError(
+            f"Jones-Pewsey parameter '{name}' must be scalar for this method; "
+            "per-observation parameter arrays are supported only by pdf/logpdf."
+        )
+    return scalar
+
+
+def _jp_feature_scales(kappa, psi):
+    """The two length scales of the JP kernel in its own angle: the peak
+    curvature width 1/√κ_eff at φ = 0 (the ψ < 0 spike) and the antipodal
+    near-kink scale 2e^{−|κψ|} at φ = π, where g ≈ e^A cos²(φ/2) + e^{−A}
+    crosses over (a √-type feature the kernel and the moment integrands
+    inherit). Returns ``(w_peak, w_anti)``, clipped to [1e-320, 1].
+
+    The lower clip is a pure positivity guard at the denormal floor (the
+    width formula underflows to 0 beyond |κψ| ≈ 745, which would hang the
+    geometric rung loop) — it must NOT be a resolution floor: under the
+    fixed-panel GL ladder any floor above the true feature scale silently
+    truncates the ladder and loses the ψ < 0 spike that carries all of the
+    mass. An earlier 1e-13 floor — harmless for the adaptive quadrature it
+    was written for — made the normalizer wrong beyond |κψ| ≈ 33 and
+    garbage by |κψ| ≈ 40 (caught against the exact ψ = −1 identity
+    Z ≡ 2π); a 1e-300 floor still broke |κψ| ≳ 690. Rungs below ~1e-320
+    only resolve because the peak sits at φ = 0, where doubles are
+    denormally dense — which is also why the asymmetric family must
+    integrate in its *unwarped* angle (see ``_jp_log_c_asym``)."""
+    A = kappa * psi
+    if abs(A) < 1e-12:
+        keff = max(kappa, 1e-12)
+        w_peak = float(np.clip(1.0 / np.sqrt(keff), 1e-320, 1.0))
+    else:
+        with np.errstate(over="ignore"):  # expm1 → inf is fine: w_peak
+            # lands on the 1e-320 denormal guard either way
+            if A >= 0.0:
+                log_keff = np.log(-np.expm1(-2.0 * A)) - np.log(2.0 * abs(psi))
+            else:
+                log_keff = np.log(np.expm1(-2.0 * A)) - np.log(2.0 * abs(psi))
+            w_peak = float(np.clip(np.exp(-0.5 * log_keff), 1e-320, 1.0))
+    w_anti = float(np.clip(2.0 * np.exp(-abs(A)), 1e-320, 1.0))
+    return w_peak, w_anti
+
+
+def _gl_panels_from_edges(edges):
+    """Composite 24-point Gauss–Legendre nodes/weights over consecutive
+    panel ``edges`` (sorted). Returns ``(nodes, weights)``."""
+    xi, wgl = _JP_GL_XW
+    nodes, wts = [], []
+    for a, b in zip(edges[:-1], edges[1:]):
+        mid, hw = 0.5 * (a + b), 0.5 * (b - a)
+        nodes.append(mid + hw * xi)
+        wts.append(hw * wgl)
+    return np.concatenate(nodes), np.concatenate(wts)
+
+
+def _jp_ladder_edges(kappa, psi):
+    """Break-point ladder over the half circle [0, π]: rungs grow
+    geometrically (decade steps) from *both* ends at the
+    ``_jp_feature_scales`` of the peak (φ = 0) and the antipodal near-kink
+    (φ = π), so every panel spans at most one decade of its feature scale.
+    Returns the sorted edge array."""
+    w_peak, w_anti = _jp_feature_scales(kappa, psi)
+    edge_set = {0.0, float(np.pi)}
+    r = w_peak
+    while r < np.pi:
+        edge_set.add(r)
+        r *= 10.0
+    r = w_anti
+    while r < np.pi:
+        edge_set.add(float(np.pi) - r)
+        r *= 10.0
+    return np.asarray(sorted(edge_set))
+
+
+def _jp_gl_panels(kappa, psi):
+    """[0, π] composite Gauss–Legendre nodes/weights on the JP break-point
+    ladder (the kernel is even in φ, so a half-circle sweep suffices) —
+    24-point GL per ``_jp_ladder_edges`` panel is at quadrature precision.
+    Returns ``(nodes, weights)``.
+    """
+    return _gl_panels_from_edges(_jp_ladder_edges(kappa, psi))
+
+
+@lru_cache(maxsize=1024)
+def _jp_quantile_table(kappa: float, psi: float):
+    """Centered sampling table for the JP kernel law: density and cdf knots
+    on a ladder-graded grid (32 log-spaced points per feature-scale decade,
+    mirrored about 0), so the ψ < 0 spike is resolved at any representable
+    depth — the failure mode of the retired von Mises rejection envelope,
+    whose 2048-point uniform calibration grid never saw spikes narrower
+    than ~6e-3 (wrong draws from κψ ≈ −6, hangs by −50). Within-knot cells
+    the density is taken as linear, so inverse-transform draws sample
+    *exactly* from the piecewise-linear density through true knot values
+    (knot spacing ≤ 7% of the local scale ⇒ cdf bias ≪ KS resolution).
+    Returns ``(phi, cdf, dens)`` with cdf[0] = 0, cdf[-1] = 1.
+    """
+    edges = _jp_ladder_edges(kappa, psi)
 
-def _jp_kernel_base(phi, kappa, psi):
+    half = [0.0]
+    for a, b in zip(edges[:-1], edges[1:]):
+        if a == 0.0:  # the flat-topped innermost cell: linear subdivision
+            half.extend(np.linspace(a, b, 33)[1:])
+        else:
+            half.extend(np.geomspace(a, b, 33)[1:])
+    half = np.asarray(half, dtype=float)
+    phi = np.concatenate([-half[::-1], half[1:]])
+
+    h = _jp_log_kernel(phi, kappa, psi)
+    dens = np.exp(h - kappa)  # ∝ density; peak value exactly 1
+    seg = 0.5 * (dens[1:] + dens[:-1]) * np.diff(phi)
+    cdf = np.concatenate([[0.0], np.cumsum(seg)])
+    total = max(float(cdf[-1]), np.finfo(float).tiny)
+    return phi, cdf / total, dens / total
+
+
+def _jp_table_invert(u, kappa, psi):
+    """Invert the ``_jp_quantile_table`` cdf at probabilities ``u``: locate
+    the cell by searchsorted, then invert the cell's quadratic cdf (linear
+    density) in closed form. Returns centered angles in [−π, π] — exact for
+    the table's piecewise-linear density at any representable spike depth.
+    Serves the sampler (with uniform draws) and the deep-cdf ppf branch
+    (with target probabilities, as the Newton initialization)."""
+    phi, cdf, dens = _jp_quantile_table(float(kappa), float(psi))
+    u = np.asarray(u, dtype=float)
+    idx = np.clip(np.searchsorted(cdf, u, side="right") - 1, 0, len(phi) - 2)
+    du = u - cdf[idx]
+    dx = phi[idx + 1] - phi[idx]
+    a = 0.5 * (dens[idx + 1] - dens[idx]) * dx
+    b = dens[idx] * dx
+    # solve a t² + b t = du for the position fraction t ∈ [0, 1]
+    lin = np.abs(a) <= 1e-12 * np.maximum(np.abs(b), np.finfo(float).tiny)
+    with np.errstate(invalid="ignore", divide="ignore"):
+        disc = np.sqrt(np.maximum(b * b + 4.0 * a * du, 0.0))
+        t_quad = (disc - b) / np.where(lin, 1.0, 2.0 * a)
+        t_lin = du / np.maximum(b, np.finfo(float).tiny)
+    t = np.clip(np.where(lin, t_lin, t_quad), 0.0, 1.0)
+    return phi[idx] + t * dx
+
+
+def _jp_sample_table(kappa, psi, total, rng):
+    """Draw ``total`` centered angles from the JP kernel law by inverse
+    transform on ``_jp_quantile_table`` (see ``_jp_table_invert``)."""
+    return _jp_table_invert(rng.random(total), kappa, psi)
+
+
+# --- deep-spike cdf/ppf branch (methods-parity P1-C) --------------------------
+# The series cdf path (4096-point coefficient grid, ≤ 256 harmonics) cannot
+# represent ψ < 0 spikes much narrower than the harmonic cap resolves:
+# probed 2026-06-11, its cdf error is ≤ 4e-11 at κψ = −3 for ψ ∈
+# [−2.5, −0.3] but reaches 1.5e-4 by κψ = −4 and 1.0 (at spike-interior
+# points) by −7. Below the gate the cdf is evaluated exactly instead, by
+# composite Gauss–Legendre on the same feature-scale ladder that serves the
+# normalizer and the sampler: cached cumulatives at the ladder edges plus a
+# 24-point partial panel per query.
+
+_JP_CDF_DEEP_A = -3.0  # κψ at/below which the series path is retired …
+_JP_CDF_DEEP_W = 0.06  # … or the peak feature scale that forces it
+
+
+def _jp_cdf_use_ladder(kappa, psi):
+    """True where the JP-clan cdf/ppf must leave the series / uniform-grid
+    path for the exact ladder branch: ψ < 0 with the spike near or below
+    the series resolution (the gate sits where the series is still clean,
+    so both branches agree to ~1e-10 at the boundary)."""
+    if psi >= 0.0 or kappa < _JP_KAPPA_TOL or abs(psi) < _JP_PSI_TOL:
+        return False
+    if kappa * psi <= _JP_CDF_DEEP_A:
+        return True
+    w_peak, _ = _jp_feature_scales(kappa, psi)
+    return w_peak < _JP_CDF_DEEP_W
+
+
+def _jp_log_kernel(phi, kappa, psi):
+    """The JP log-kernel ``h(φ; κ, ψ)`` alone — the ladder integrand and
+    moment paths need no scores, and the full `_jp_score_terms` bundle
+    costs ~15 array ops where one or four suffice. Branch-for-branch
+    bitwise-identical to ``_jp_score_terms(...)["h"]``: ``ψ = 0`` is the
+    von Mises member ``h = κ cos φ`` (equal to the cumulant series at
+    A = 0), ``|κψ| < _JP_A_SMALL`` the cumulant series, else the
+    overflow-safe ``logaddexp`` form."""
     phi = np.asarray(phi, dtype=float)
-    if abs(psi) < _JP_PSI_TOL:
-        return np.exp(kappa * np.cos(phi))
-
+    if psi == 0.0:
+        return kappa * np.cos(phi)
     A = kappa * psi
-    cos_phi = np.cos(phi)
-
-    cosh_A = np.cosh(A)
-    sinh_A = np.sinh(A)
-    if not np.isfinite(cosh_A) or not np.isfinite(sinh_A):
-        # Fallback to stable exponential representation
-        if A >= 0:
-            exp_A = np.exp(np.clip(A, None, _JP_MAX_EXP_ARGUMENT))
-            exp_negA = np.exp(np.clip(-A, -_JP_MAX_EXP_ARGUMENT, None))
-        else:
-            exp_A = np.exp(np.clip(A, -_JP_MAX_EXP_ARGUMENT, None))
-            exp_negA = np.exp(np.clip(-A, None, _JP_MAX_EXP_ARGUMENT))
-        cosh_A = 0.5 * (exp_A + exp_negA)
-        sinh_A = 0.5 * (exp_A - exp_negA)
-
-    base = cosh_A + sinh_A * cos_phi
-    base = np.clip(base, _JP_MIN_BASE, None)
-    return np.power(base, 1.0 / psi)
+    if abs(A) < _JP_A_SMALL:
+        s = np.sin(phi)
+        c = np.cos(phi)
+        s2 = s * s
+        k3 = -2.0 * c * s2
+        k4 = s2 * (4.0 - 6.0 * s2)
+        return kappa * (c + s2 * A / 2.0 + k3 * A * A / 6.0 + k4 * A**3 / 24.0)
+    half = 0.5 * phi
+    with np.errstate(divide="ignore"):
+        lp = 2.0 * np.log(np.abs(np.cos(half)))
+        lq = 2.0 * np.log(np.abs(np.sin(half)))
+    return np.logaddexp(A + lp, -A + lq) / psi
+
+
+@lru_cache(maxsize=1024)
+def _jp_cdf_ladder(kappa: float, psi: float):
+    """Half-line cumulative tables for the centered JP kernel law: GL-exact
+    cumulatives of e^{h−κ} (base) and e^{h−κ} sin t (the sine-skew moment)
+    at the ``_jp_ladder_edges`` panel edges on [0, π]. Returns
+    ``(edges, cum_base, cum_skew)`` with ``cum_*[0] = 0``."""
+    edges = _jp_ladder_edges(kappa, psi)
+    nodes, wts = _gl_panels_from_edges(edges)
+    h = _jp_log_kernel(nodes, kappa, psi)
+    e = wts * np.exp(h - kappa)
+    n_gl = _JP_GL_XW[0].size
+    base = e.reshape(edges.size - 1, n_gl).sum(axis=1)
+    skew = (e * np.sin(nodes)).reshape(edges.size - 1, n_gl).sum(axis=1)
+    cum_base = np.concatenate([[0.0], np.cumsum(base)])
+    cum_skew = np.concatenate([[0.0], np.cumsum(skew)])
+    return edges, cum_base, cum_skew
+
+
+def _jp_half_cum(x, kappa, psi, want_skew=True):
+    """R(x) = ∫₀ˣ e^{h−κ} dt and S(x) = ∫₀ˣ e^{h−κ} sin t dt for x ∈
+    [0, π]: cached edge cumulatives plus a 24-point partial panel
+    [edge_k, x] per query — quadrature-exact at any spike depth. With
+    ``want_skew=False`` skips the sine moment and returns ``(R, None)``."""
+    edges, cum_base, cum_skew = _jp_cdf_ladder(kappa, psi)
+    x = np.clip(np.asarray(x, dtype=float).reshape(-1), 0.0, np.pi)
+    k = np.clip(np.searchsorted(edges, x, side="right") - 1, 0, edges.size - 2)
+    hw = 0.5 * (x - edges[k])
+    xi_gl, w_gl = _JP_GL_XW
+    nodes = (edges[k] + hw)[:, None] + hw[:, None] * xi_gl[None, :]
+    h = _jp_log_kernel(nodes, kappa, psi)
+    e = np.exp(h - kappa)
+    base = cum_base[k] + hw * (e @ w_gl)
+    if not want_skew:
+        return base, None
+    skew = cum_skew[k] + hw * ((e * np.sin(nodes)) @ w_gl)
+    return base, skew
+
+
+def _jp_cum01(phi, kappa, psi, want_skew=True):
+    """Exact H(φ) = ∫₀^φ f_c dt and J(φ) = ∫₀^φ f_c sin t dt over
+    φ ∈ [0, 2π) for the centered normalized JP kernel law f_c (drop-in
+    replacements for the series cumulative and skew integral in the deep
+    regime). The kernel is even, so both fold onto the half-line tables:
+    H = R(φ)/Z for φ ≤ π and 1 − R(2π−φ)/Z above; J = S(min(φ, 2π−φ))/Z.
+    Beyond κψ ≈ −740 (spike below the smallest denormal) the tiny-floor
+    guard makes values best-effort, never nan — same boundary as the
+    normalizer. With ``want_skew=False`` returns ``(H, None)``."""
+    phi = np.asarray(phi, dtype=float).reshape(-1)
+    upper = phi > np.pi
+    x = np.where(upper, 2.0 * np.pi - phi, phi)
+    R, S = _jp_half_cum(x, kappa, psi, want_skew=want_skew)
+    _, cum_base, _ = _jp_cdf_ladder(kappa, psi)
+    ztot = max(2.0 * float(cum_base[-1]), np.finfo(float).tiny)
+    H = np.where(upper, 1.0 - R / ztot, R / ztot)
+    return H, (None if S is None else S / ztot)
+
+
+def _jp_solve_quantile(target, x0, lo, hi, cdf_fn, pdf_fn, tol=1e-13,
+                       max_iter=100):
+    """Vectorized safeguarded Newton on a monotone cdf: solve
+    cdf_fn(x) = target from init ``x0`` with brackets [lo, hi] (bisection
+    midpoint wherever the Newton step is non-finite or leaves the
+    bracket). Convergence is on the cdf residual — in dead-flat tails the
+    bracket midpoint is the honest answer and the iteration cap bounds the
+    cost."""
+    x = np.clip(np.asarray(x0, dtype=float), lo, hi)
+    target = np.asarray(target, dtype=float)
+    L = np.full_like(x, lo)
+    H = np.full_like(x, hi)
+    for _ in range(max_iter):
+        d = cdf_fn(x) - target
+        L = np.where(d <= 0.0, x, L)
+        H = np.where(d > 0.0, x, H)
+        done = np.abs(d) <= tol
+        if np.all(done):
+            break
+        p = pdf_fn(x)
+        with np.errstate(divide="ignore", invalid="ignore"):
+            xn = x - d / p
+        xn = np.where(np.isfinite(xn) & (xn > L) & (xn < H), xn,
+                      0.5 * (L + H))
+        x = np.where(done, x, xn)
+    return x
 
 
-def _jp_effective_kappa(kappa, psi):
+def _jp_ppf_ladder(q, mu, kappa, psi):
+    """Deep-regime JP quantiles: solve F_c(ζ) = u in the centered angle
+    (spike at ζ = 0, where doubles are dense) with the exact ladder cdf,
+    initialized by the sampler's quantile-table inverse — which lands
+    inside the spike at any representable depth. ``q`` must be interior."""
+    two_pi = 2.0 * np.pi
+    H_start, _ = _jp_cum01(np.array([(-mu) % two_pi]), kappa, psi, want_skew=False)
+    u_t = (np.asarray(q, dtype=float) + float(H_start[0]) + 0.5) % 1.0
+    zeta0 = _jp_table_invert(u_t, kappa, psi)
+    _, cum_base, _ = _jp_cdf_ladder(kappa, psi)
+    ztot = max(2.0 * float(cum_base[-1]), np.finfo(float).tiny)
+
+    def cdf_fn(z):
+        R, _ = _jp_half_cum(np.abs(z), kappa, psi, want_skew=False)
+        return 0.5 + np.sign(z) * R / ztot
+
+    def pdf_fn(z):
+        h = _jp_log_kernel(z, kappa, psi)
+        return np.exp(h - kappa) / ztot
+
+    zeta = _jp_solve_quantile(u_t, zeta0, -np.pi, np.pi, cdf_fn, pdf_fn)
+    return np.mod(mu + zeta, two_pi)
+
+
+def _jp_ppf_ladder_sineskewed(q, xi, kappa, psi, lmbd):
+    """Deep-regime sine-skewed JP quantiles: same centered-angle solve as
+    ``_jp_ppf_ladder`` against F_c(ζ) + λ(S(|ζ|) − S(π))/Z. The init from
+    the unskewed kernel table is skew-blind, which the Newton polish
+    absorbs (inside a deep spike sin ζ ≈ 0, so the skew factor is ≈ 1
+    exactly where the init has to be precise)."""
+    two_pi = 2.0 * np.pi
+    H_s, J_s = _jp_cum01(np.array([(-xi) % two_pi]), kappa, psi)
+    _, cum_base, cum_skew = _jp_cdf_ladder(kappa, psi)
+    ztot = max(2.0 * float(cum_base[-1]), np.finfo(float).tiny)
+    sn_pi = float(cum_skew[-1])
+    g_start = float(H_s[0]) + lmbd * float(J_s[0])
+    g_c0 = 0.5 - lmbd * sn_pi / ztot
+    u_t = (np.asarray(q, dtype=float) + g_start + g_c0) % 1.0
+    zeta0 = _jp_table_invert(u_t, kappa, psi)
+
+    def cdf_fn(z):
+        R, S = _jp_half_cum(np.abs(z), kappa, psi)
+        return 0.5 + np.sign(z) * R / ztot + lmbd * (S - sn_pi) / ztot
+
+    def pdf_fn(z):
+        h = _jp_log_kernel(z, kappa, psi)
+        return np.exp(h - kappa) * (1.0 + lmbd * np.sin(z)) / ztot
+
+    zeta = _jp_solve_quantile(u_t, zeta0, -np.pi, np.pi, cdf_fn, pdf_fn)
+    return np.mod(xi + zeta, two_pi)
+
+
+@lru_cache(maxsize=4096)
+def _jp_log_c(kappa: float, psi: float) -> float:
+    """log of the Jones–Pewsey normalizing constant, log c(κ, ψ) =
+    −log ∫ kernel dφ (μ-invariant).
+
+    Same preference order as the historical linear-space normalizer
+    (uniform and von Mises reductions first; the Legendre closed form
+    ``1/(2π P_{1/ψ}(cosh κψ))`` stays banned — scipy's ``lpmv`` is the
+    *Ferrers* function, domain |x| ≤ 1, and off the cut it silently
+    returns garbage for non-integer degree: plausible near z = 1, −1e63
+    by z ≈ 10, nan beyond. The off-cut route via
+    ``hyp2f1(−ν, ν+1; 1; (1−z)/2)`` was probed 2026-06-11: ~1e-13 in the
+    moderate band but linear-space — overflows at the e^709 wall for
+    ψ > 0 and internally from κψ ≈ −30 for ψ < 0. This GL ladder *is*
+    the same function — the kernel integral is its Mehler–Dirichlet-type
+    representation — evaluated in log space at any depth), but evaluated
+    **entirely in log space** with the kernel's peak value e^κ factored
+    out: the raw kernel
+    maximum is exp(κ) for every ψ, so any linear-space evaluation turns
+    the whole JP clan's pdf into nan for κ ≳ 709 (methods-parity P1).
+    The general branch integrates e^{h−κ} ≤ 1 by composite Gauss–Legendre
+    on ``_jp_gl_panels`` — the same engine as the regression moment
+    machinery (``_jp_logZ_moments``), which retired the per-call adaptive
+    quadrature this replaced.
+    """
+    if kappa < _JP_KAPPA_TOL:
+        return float(-np.log(2.0 * np.pi))
     if abs(psi) < _JP_PSI_TOL:
-        return max(kappa, 1e-6)
-    A = kappa * psi
-    with np.errstate(over="ignore"):
-        factor = 1.0 - np.exp(-2.0 * A)
-    kappa_eff = factor / (2.0 * psi)
-    if not np.isfinite(kappa_eff) or kappa_eff <= 0.0:
-        return max(kappa, 1e-6)
-    return float(kappa_eff)
-
-
-def _log_vonmises_pdf(theta, mu, kappa):
-    theta = np.asarray(theta, dtype=float)
-    if kappa < 1e-8:
-        return np.full_like(theta, -np.log(2.0 * np.pi), dtype=float)
-    diff = theta - mu
-    log_i0 = kappa + np.log(i0e(kappa))
-    return kappa * np.cos(diff) - (np.log(2.0 * np.pi) + log_i0)
-
-
-def _optimize_vonmises_envelope(theta, log_target, mu, initial_guess, *, max_iter=3):
-    min_kappa = 1e-6
-    max_kappa = 1e4
-    best_kappa = max(initial_guess, min_kappa)
-    log_M_best = np.inf
-
-    def evaluate(candidates):
-        nonlocal best_kappa, log_M_best
-        for kappa_env in candidates:
-            kappa_env = float(np.clip(kappa_env, min_kappa, max_kappa))
-            log_proposal = _log_vonmises_pdf(theta, mu, kappa_env)
-            log_ratio = log_target - log_proposal
-            log_ratio_max = float(np.max(log_ratio))
-            if not np.isfinite(log_ratio_max):
-                continue
-            if log_ratio_max < log_M_best:
-                log_M_best = log_ratio_max
-                best_kappa = kappa_env
-
-    candidate_pool = np.array(
-        [
-            initial_guess,
-            max(initial_guess * 0.5, min_kappa),
-            initial_guess * 2.0,
-            max(initial_guess * 0.25, min_kappa),
-            initial_guess * 4.0,
-            0.5,
-            1.0,
-            max(initial_guess, 1.5),
-            max(initial_guess, 3.0),
-        ],
-        dtype=float,
+        return float(-(np.log(2.0 * np.pi * i0e(kappa)) + kappa))
+    nodes, wts = _jp_gl_panels(kappa, psi)
+    h = _jp_log_kernel(nodes, kappa, psi)
+    # kernel even in φ with peak h(0) = κ exactly. The tiny-floor guard
+    # only engages beyond |κψ| ≈ 740 with ψ < 0, where the spike is below
+    # float resolution even at φ = 0 (the distribution is numerically a
+    # point mass): values there are best-effort, never nan.
+    integral = 2.0 * np.sum(wts * np.exp(h - kappa))
+    return float(-(kappa + np.log(max(integral, np.finfo(float).tiny))))
+
+
+def _jp_log_c_vec(kappa, psi):
+    """Per-observation ``_jp_log_c(κ_i, ψ_i)`` — element-wise mirror of the
+    scalar preference order, evaluated once per unique ``(κ, ψ)`` pair (the
+    lru cache on the scalar makes repeated regression iterations cheap)."""
+    kappa, psi = np.broadcast_arrays(
+        np.asarray(kappa, dtype=float), np.asarray(psi, dtype=float)
     )
-    candidate_pool = np.unique(np.clip(candidate_pool, min_kappa, max_kappa))
-    evaluate(candidate_pool)
-
-    for _ in range(max_iter):
-        span = np.linspace(best_kappa * 0.5, best_kappa * 1.5, num=7)
-        span = np.clip(span, min_kappa, max_kappa)
-        evaluate(span)
-
-    log_M_best = float(log_M_best)
-    K = float(best_kappa)
-    M = float(np.exp(log_M_best + np.log1p(0.02)))
-    return K, max(M, 1.01)
-
-
-def _kernel_jonespewsey(x, mu, kappa, psi):
-    phi = np.asarray(x, dtype=float) - mu
-    return _jp_kernel_base(phi, kappa, psi)
-
-
-def _c_jonespewsey(mu, kappa, psi):
+    out = np.full(kappa.shape, -np.log(2.0 * np.pi))
+    live = kappa >= _JP_KAPPA_TOL
+    vm = live & (np.abs(psi) < _JP_PSI_TOL)
+    if np.any(vm):
+        out[vm] = -(np.log(2.0 * np.pi * i0e(kappa[vm])) + kappa[vm])
+    gen = live & ~vm
+    if not np.any(gen):
+        return out
+    pairs, inverse = np.unique(
+        np.stack([kappa[gen], psi[gen]], axis=1), axis=0, return_inverse=True
+    )
+    vals = np.array([_jp_log_c(float(k), float(p)) for k, p in pairs])
+    out[gen] = vals[inverse]
+    return out
+
+
+@lru_cache(maxsize=8192)
+def _jp_cos_moment(kappa: float, psi: float, q: int) -> float:
+    """q-th cosine moment ᾱ_q = E[cos(qΦ)] of the *centered* JP law (the
+    sine moments vanish — the kernel is even). One Gauss–Legendre sweep on
+    the feature-scale ladder as a normalizer-free ratio, so it is exact at
+    any concentration; the vM and uniform reductions are closed-form.
+    Serves the sine-skewed JP trig moments (book §4.3.11), which are exact
+    combinations of the base moments ᾱ_{p−1}, ᾱ_p, ᾱ_{p+1}."""
+    if q == 0:
+        return 1.0
     if kappa < _JP_KAPPA_TOL:
-        return 1.0 / (2.0 * np.pi)
-
+        return 0.0
     if abs(psi) < _JP_PSI_TOL:
-        return 1.0 / (2.0 * np.pi * i0(kappa))
+        return float(ive(q, kappa) / ive(0, kappa))
+    nodes, wts = _jp_gl_panels(kappa, psi)
+    e = wts * np.exp(_jp_log_kernel(nodes, kappa, psi) - kappa)
+    denom = max(float(np.sum(e)), np.finfo(float).tiny)
+    return float(np.sum(np.cos(q * nodes) * e) / denom)
+
+
+# --- Jones–Pewsey regression derivatives (the l1/l2 contract) ----------------
+# Everything below differentiates the JP log-kernel h(φ; κ, ψ) =
+# (1/ψ) log(cosh κψ + sinh κψ cos φ) and the log-normalizer log Z(κ, ψ); the
+# distribution methods assemble them into per-observation score/Hessian
+# entries. Derivatives are exact down to κ → 0 (no _JP_KAPPA_TOL flat spot:
+# the uniform reduction in ``_pdf`` is a value-only shortcut, and keeping the
+# scores smooth there preserves the gradient signal the optimizer needs to
+# leave the near-uniform corner).
+
+_JP_A_SMALL = 1e-4  # |κψ| below this switches the ψ-direction to series
+_JP_LOG_CAP = 250.0  # cap on log Ṽ; see _jp_score_terms
+
+
+def _jp_score_terms(phi, kappa, psi, second=True):
+    r"""Stable per-observation derivatives of the JP log-kernel
+    ``h(φ; κ, ψ) = (1/ψ) log(cosh κψ + sinh κψ cos φ)``.
+
+    All quantities derive from overflow-safe primitives of the exact
+    decomposition ``g = e^A cos²(φ/2) + e^{−A} sin²(φ/2)`` (A = κψ):
+
+    - ``lg = log g`` via ``logaddexp``;
+    - ``arg = A + ½(lp − lq)`` so that ``T := g_A/g = tanh(arg)`` and
+      ``∂T/∂A = sech²(arg)`` — and, since ``∂arg/∂φ = −1/sin φ``,
+      ``∂T/∂φ = −sech²(arg)/sin φ`` (→ 0 at φ ≡ 0, π);
+    - ``Ṽ := sinh(A)/(ψ g) = κ·sinhc(A)/g ≥ 0`` in log form, so that
+      ``h_φ = −Ṽ sin φ`` and ``h_φφ = −Ṽ cos φ − ψ (Ṽ sin φ)²``.
+
+    The ψ-direction uses the cumulant view: ``log g = K(A)``, the cgf of a
+    ±1 variable with ``P(+1) = cos²(φ/2)`` and cumulants κ₁ = c, κ₂ = s²,
+    κ₃ = −2cs², κ₄ = s²(4−6s²), κ₅ = −8cs²(1−3s²) (c = cos φ, s = sin φ):
+
+    $$h_ψ = κ²\frac{AT − K}{A²},\qquad
+      h_{ψψ} = κ³\frac{A²\,\mathrm{sech}²(arg) − 2AT + 2K}{A³},\qquad
+      h_{φψ} = κ²\frac{A\,T_φ + ψ\,Ṽ\sinφ}{A²}.$$
+
+    These cancel catastrophically as A → 0, so ``|A| < _JP_A_SMALL``
+    switches to the cumulant series (agreement ≲1e-9 at the boundary,
+    checked by the dev harness). log Ṽ is capped at ``_JP_LOG_CAP`` so the
+    0·∞ antipode corner (|κψ| beyond ~350) degrades to large-but-finite
+    scores instead of NaN — far outside any optimizer-recoverable region.
+
+    Returns a dict with the log-kernel ``h`` and first derivatives
+    ``hphi``, ``hk``, ``hp``; with ``second=True`` adds ``hphiphi``,
+    ``hphik``, ``hphip``, ``hkk``, ``hkp``, ``hpp``.
+    """
+    phi_b, kappa_b, psi_b = np.broadcast_arrays(
+        np.asarray(phi, dtype=float),
+        np.asarray(kappa, dtype=float),
+        np.asarray(psi, dtype=float),
+    )
+    A = kappa_b * psi_b
+    absA = np.abs(A)
+    half = 0.5 * phi_b
+    s, c = np.sin(phi_b), np.cos(phi_b)
+    s2 = s * s
+    with np.errstate(divide="ignore", invalid="ignore"):
+        lp = 2.0 * np.log(np.abs(np.cos(half)))
+        lq = 2.0 * np.log(np.abs(np.sin(half)))
+        logk = np.log(kappa_b)  # −inf at κ = 0 (uniform: Ṽ = 0)
+        logabss = np.log(np.abs(s))  # −inf at φ ≡ 0, π
+    lg = np.logaddexp(A + lp, -A + lq)
+    arg = A + 0.5 * (lp - lq)
+    T = np.tanh(arg)
+    a2 = np.exp(-2.0 * np.abs(arg))
+    sech2 = 4.0 * a2 / (1.0 + a2) ** 2
+    small = absA < _JP_A_SMALL
+    with np.errstate(divide="ignore", invalid="ignore"):
+        lsinhc = np.where(
+            small,
+            A * A / 6.0,
+            absA + np.log1p(-np.exp(-2.0 * absA)) - np.log(2.0 * absA),
+        )
+    lVt = np.minimum(logk + lsinhc - lg, _JP_LOG_CAP)
+    Vt = np.exp(lVt)
+    sVt = np.sign(s) * np.exp(logabss + lVt)  # Ṽ·sin φ, 0·∞-safe
+
+    # cumulants of the ±1 cgf K(A) at this φ
+    k3 = -2.0 * c * s2
+    k4 = s2 * (4.0 - 6.0 * s2)
+    k5 = -8.0 * c * s2 * (1.0 - 3.0 * s2)
+
+    with np.errstate(divide="ignore", invalid="ignore"):
+        h = np.where(
+            small,
+            kappa_b
+            * (c + s2 * A / 2.0 + k3 * A * A / 6.0 + k4 * A**3 / 24.0),
+            lg / psi_b,
+        )
+        W = np.where(
+            small,
+            s2 / 2.0 + k3 * A / 3.0 + k4 * A * A / 8.0 + k5 * A**3 / 30.0,
+            (A * T - lg) / (A * A),
+        )
+    out = {
+        "h": h,
+        "hphi": -sVt,
+        "hk": T,
+        "hp": kappa_b * kappa_b * W,
+    }
+    if not second:
+        return out
+
+    with np.errstate(divide="ignore", invalid="ignore"):
+        Tphi = np.where(s == 0.0, 0.0, -sech2 / s)
+        Wp = np.where(
+            small,
+            k3 / 3.0 + k4 * A / 4.0 + k5 * A * A / 10.0,
+            (A * A * sech2 - 2.0 * A * T + 2.0 * lg) / A**3,
+        )
+        Wphi = np.where(
+            small,
+            s * c
+            + 2.0 * s * (1.0 - 3.0 * c * c) * A / 3.0
+            + s * c * (1.0 - 3.0 * s2) * A * A,
+            (A * Tphi + psi_b * sVt) / (A * A),
+        )
+    out.update(
+        {
+            "hphiphi": -c * Vt - psi_b * sVt * sVt,
+            "hphik": Tphi,
+            "hphip": kappa_b * kappa_b * Wphi,
+            "hkk": psi_b * sech2,
+            "hkp": kappa_b * sech2,
+            "hpp": kappa_b**3 * Wp,
+        }
+    )
+    return out
 
-    constant = _jp_legendre_normalizer(kappa, psi)
-    if np.isfinite(constant) and 1e-12 <= constant <= 1e6:
-        return constant
 
-    integral = quad_vec(
-        _kernel_jonespewsey,
-        a=-np.pi,
-        b=np.pi,
-        args=(mu, kappa, psi),
-        epsabs=1e-10,
-        epsrel=1e-10,
-    )[0]
-    return 1.0 / integral
+_JP_GL_XW = roots_legendre(24)
 
 
-def _jp_legendre_normalizer(kappa, psi):
-    try:
-        nu = 1.0 / psi
-    except ZeroDivisionError:
-        return np.nan
+@lru_cache(maxsize=4096)
+def _jp_logZ_moments(kappa: float, psi: float):
+    """First and second (κ, ψ)-derivatives of ``log Z(κ, ψ)`` with
+    ``Z = ∫ kernel dφ``, as kernel-weighted moments:
 
-    A = kappa * psi
-    z = np.cosh(A)
-    try:
-        legendre = lpmv(0, nu, z)
-    except ValueError:
-        return np.nan
+        ∂log Z/∂θ_a  = E[h_a],
+        ∂²log Z/∂θ_a∂θ_b = E[h_ab + h_a h_b] − E[h_a]E[h_b],
 
-    if not np.isfinite(legendre) or legendre <= 0.0:
-        return np.nan
+    the expectations taken under the JP density itself. Evaluated by
+    composite 24-point Gauss–Legendre on the two-ended break-point ladder
+    of ``_jp_gl_panels`` (for ψ > 0 the hard feature is the antipodal
+    near-kink — g ≈ e^A cos²(φ/2) + e^{−A} crosses over at π − φ ≈
+    2e^{−|A|}, which the kernel *and* the moment integrands (T swings
+    1 → −1 there) inherit; a coarse panel straddling it is only ~1e-7
+    accurate). One node sweep serves all five integrands — the "one
+    numeric expectation per unique parameter tuple" cost of the plan,
+    cached per (κ, ψ) so ``dlogpdf``/``d2logpdf`` within one ``ll()``
+    evaluation share the work.
 
-    return 1.0 / (2.0 * np.pi * legendre)
+    Returns ``(dk, dp, dkk, dkp, dpp)``.
+    """
+    nodes, wts = _jp_gl_panels(kappa, psi)
+    t = _jp_score_terms(nodes, kappa, psi, second=True)
+    e = np.exp(t["h"] - float(np.max(t["h"]))) * wts
+    Z = max(float(np.sum(e)), np.finfo(float).tiny)
+
+    def m(v):
+        return float(np.sum(e * v)) / Z
+
+    dk, dp = m(t["hk"]), m(t["hp"])
+    dkk = m(t["hkk"] + t["hk"] * t["hk"]) - dk * dk
+    dkp = m(t["hkp"] + t["hk"] * t["hp"]) - dk * dp
+    dpp = m(t["hpp"] + t["hp"] * t["hp"]) - dp * dp
+    return dk, dp, dkk, dkp, dpp
+
+
+def _jp_logZ_moments_vec(kappa, psi):
+    """Element-wise ``_jp_logZ_moments`` over per-observation (κ_i, ψ_i),
+    evaluated once per unique pair. Returns five arrays broadcast to the
+    common parameter shape: ``(dk, dp, dkk, dkp, dpp)``."""
+    kappa, psi = np.broadcast_arrays(
+        np.asarray(kappa, dtype=float), np.asarray(psi, dtype=float)
+    )
+    pairs, inverse = np.unique(
+        np.stack([kappa.ravel(), psi.ravel()], axis=1), axis=0,
+        return_inverse=True,
+    )
+    vals = np.array([_jp_logZ_moments(float(k), float(p)) for k, p in pairs])
+    out = vals[inverse].reshape(kappa.shape + (5,))
+    return tuple(np.moveaxis(out, -1, 0))
 
 
 ###########################
@@ -5463,7 +7923,7 @@ def _jp_legendre_normalizer(kappa, psi):
 ###########################
 
 
-class jonespewsey_sineskewed_gen(CircularContinuous):
+class jonespewsey_sineskewed_gen(_RegressionReady, CircularContinuous):
     r"""Sine-Skewed Jones-Pewsey Distribution
 
     The Sine-Skewed Jones-Pewsey distribution is a circular distribution defined on $[0, 2\pi)$
@@ -5476,18 +7936,117 @@ class jonespewsey_sineskewed_gen(CircularContinuous):
     pdf(x, xi, kappa, psi, lmbd)
         Probability density function.
 
+    logpdf(x, xi, kappa, psi, lmbd)
+        Logarithm of the probability density function.
+
     cdf(x, xi, kappa, psi, lmbd)
         Cumulative distribution function.
 
+    ppf(q, xi, kappa, psi, lmbd)
+        Percent-point function (inverse CDF).
+
+    rvs(xi, kappa, psi, lmbd, size=None, random_state=None)
+        Random variates (base-JP draws with sine-skew rejection).
+
+    fit(data, *, weights=None, method="mle", ...)
+        Estimate ``(xi, kappa, psi, lmbd)`` by moments or maximum likelihood.
 
     Note
     ----
-    Parameters must be scalar; cached normalisation tables are built per parameter set.
-    Implementation based on Section 4.3.11 of Pewsey et al. (2014)
+    Scalar parameters use cached normalisation tables; ``pdf``/``logpdf`` also
+    accept per-observation parameter arrays (the regression contract). Other
+    methods (cdf, rvs, …) remain scalar-only.
+    Implementation based on Section 4.3.11 of Pewsey et al. (2013)
     """
 
-    def _validate_params(self, xi, kappa, psi, lmbd):
-        xi_arr, kappa_arr, psi_arr, lmbd_arr = np.broadcast_arrays(xi, kappa, psi, lmbd)
+    # --- regression overlay (Phase 1 contract; read by the regression engine
+    # only). Book names xi/kappa/psi/lmbd are preserved. The sine-skew factor
+    # 1 + λ sin(θ−ξ) leaves the JP normalizer c(κ, ψ) untouched, so a λ
+    # linear predictor costs nothing beyond its trivial derivative terms —
+    # this is the recommended asymmetric response family of the regression
+    # plan. λ ∈ (−1, 1) rides the tanh link. Caveat (book §4.3.11): once
+    # λ ≠ 0, ξ is the *mode anchor*, not the mean direction — the engine's
+    # fitted-direction report inherits that reading. At |λ| → 1 the density
+    # touches 0 where λ sin(θ−ξ) = −1 (logpdf → −∞; the link keeps λ
+    # interior). ---
+    param_roles = {
+        "xi": "location",
+        "kappa": "concentration",
+        "psi": "shape",
+        "lmbd": "skewness",
+    }
+    default_links = {
+        "location": "tanhalf",
+        "concentration": "log",
+        "shape": "identity",
+        "skewness": "tanh",
+    }
+
+    def dlogpdf(self, x, xi, kappa, psi, lmbd):
+        r"""First derivatives of ``logpdf`` w.r.t. the parameters (l1).
+
+        The log-density is ``log Q + h − log Z`` with ``Q = 1 + λ sin φ``,
+        ``φ = θ − ξ`` and ``h``/``Z`` the Jones–Pewsey kernel/normalizer:
+
+        $$\ell_\xi = -\frac{\lambda\cos\phi}{Q} - h_\phi,\quad
+          \ell_\kappa = h_\kappa - \mathbb{E}[h_\kappa],\quad
+          \ell_\psi = h_\psi - \mathbb{E}[h_\psi],\quad
+          \ell_\lambda = \frac{\sin\phi}{Q}.$$
+
+        Vectorizes over per-observation parameter arrays; returns a
+        book-named dict.
+        """
+        x = np.asarray(x, dtype=float)
+        xi_b, kappa_b, psi_b, lmbd_b = (
+            np.asarray(v, dtype=float) for v in (xi, kappa, psi, lmbd)
+        )
+        phi = x - xi_b
+        s, c = np.sin(phi), np.cos(phi)
+        Q = 1.0 + lmbd_b * s
+        t = _jp_score_terms(phi, kappa_b, psi_b, second=False)
+        dk, dp, *_ = _jp_logZ_moments_vec(kappa_b, psi_b)
+        return {
+            "xi": -lmbd_b * c / Q - t["hphi"],
+            "kappa": t["hk"] - dk,
+            "psi": t["hp"] - dp,
+            "lmbd": s / Q,
+        }
+
+    def d2logpdf(self, x, xi, kappa, psi, lmbd):
+        r"""Second derivatives of ``logpdf`` (l2) — unique unordered pairs.
+        The skew factor contributes only to the (ξ, λ) block (its normalizer
+        is λ- and ξ-free), so the κ/ψ entries are exactly the Jones–Pewsey
+        ones and the (κ, λ), (ψ, λ) cross terms vanish identically."""
+        x = np.asarray(x, dtype=float)
+        xi_b, kappa_b, psi_b, lmbd_b = (
+            np.asarray(v, dtype=float) for v in (xi, kappa, psi, lmbd)
+        )
+        phi = x - xi_b
+        s, c = np.sin(phi), np.cos(phi)
+        Q = 1.0 + lmbd_b * s
+        Q2 = Q * Q
+        t = _jp_score_terms(phi, kappa_b, psi_b, second=True)
+        _, _, dkk, dkp, dpp = _jp_logZ_moments_vec(kappa_b, psi_b)
+        zero = np.zeros(np.broadcast_shapes(phi.shape, Q.shape))
+        return {
+            ("xi", "xi"): -lmbd_b * (s * Q + lmbd_b * c * c) / Q2
+            + t["hphiphi"],
+            ("xi", "kappa"): -t["hphik"],
+            ("xi", "psi"): -t["hphip"],
+            ("xi", "lmbd"): -c / Q2,
+            ("kappa", "kappa"): t["hkk"] - dkk,
+            ("kappa", "psi"): t["hkp"] - dkp,
+            ("kappa", "lmbd"): zero,
+            ("psi", "psi"): t["hpp"] - dpp,
+            ("psi", "lmbd"): zero,
+            ("lmbd", "lmbd"): -s * s / Q2,
+        }
+
+    def _argcheck(self, xi, kappa, psi, lmbd):
+        try:
+            xi_arr, kappa_arr, psi_arr, lmbd_arr = np.broadcast_arrays(xi, kappa, psi, lmbd)
+        except ValueError:
+            return False
         return (
             (xi_arr >= 0.0)
             & (xi_arr <= 2.0 * np.pi)
@@ -5498,32 +8057,46 @@ def _validate_params(self, xi, kappa, psi, lmbd):
             & (lmbd_arr <= 1.0)
         )
 
-    def _argcheck(self, xi, kappa, psi, lmbd):
-        try:
-            return self._validate_params(xi, kappa, psi, lmbd)
-        except ValueError:
-            return False
-
     def _pdf(self, x, xi, kappa, psi, lmbd):
         x = np.asarray(x, dtype=float)
-        xi_scalar = _jp_ensure_scalar(xi, "xi")
-        kappa_scalar = _jp_ensure_scalar(kappa, "kappa")
-        psi_scalar = _jp_ensure_scalar(psi, "psi")
-        lmbd_scalar = _jp_ensure_scalar(lmbd, "lmbd")
+        xi_scalar = _jp_as_scalar(xi)
+        kappa_scalar = _jp_as_scalar(kappa)
+        psi_scalar = _jp_as_scalar(psi)
+        lmbd_scalar = _jp_as_scalar(lmbd)
+
+        if any(v is None for v in (xi_scalar, kappa_scalar, psi_scalar, lmbd_scalar)):
+            # Per-observation parameters — regression contract path,
+            # assembled in log space like the base JP (methods-parity P1).
+            xi_b, kappa_b, psi_b, lmbd_b = np.broadcast_arrays(
+                *(np.asarray(a, dtype=float) for a in (xi, kappa, psi, lmbd))
+            )
+            phi = x - xi_b
+            skew = 1.0 + lmbd_b * np.sin(phi)
+            logc = _jp_log_c_vec(kappa_b, psi_b)
+            h = _jp_score_terms(phi, kappa_b, psi_b, second=False)["h"]
+            vm = np.abs(psi_b) < _JP_PSI_TOL
+            h = np.where(vm, kappa_b * np.cos(phi), h)
+            dens = np.exp(h + logc) * skew
+            return np.where(
+                kappa_b < _JP_KAPPA_TOL, skew / (2.0 * np.pi), dens
+            )
 
         if abs(kappa_scalar) < _JP_KAPPA_TOL:
             return (1.0 / (2.0 * np.pi)) * (1.0 + lmbd_scalar * np.sin(x - xi_scalar))
 
-        normalizer = self._get_cached_normalizer(
-            lambda: _c_jonespewsey(xi_scalar, kappa_scalar, psi_scalar),
-            xi_scalar,
+        log_c = self._get_cached_normalizer(
+            lambda: _jp_log_c(kappa_scalar, psi_scalar),
             kappa_scalar,
             psi_scalar,
         )
-        self._c = normalizer
+        self._c = float(np.exp(log_c))  # legacy attribute (write-only)
 
-        base = _kernel_jonespewsey(x, xi_scalar, kappa_scalar, psi_scalar)
-        return normalizer * base * (1.0 + lmbd_scalar * np.sin(x - xi_scalar))
+        phi = x - xi_scalar
+        if abs(psi_scalar) < _JP_PSI_TOL:
+            h = kappa_scalar * np.cos(phi)
+        else:
+            h = _jp_score_terms(phi, kappa_scalar, psi_scalar, second=False)["h"]
+        return np.exp(h + log_c) * (1.0 + lmbd_scalar * np.sin(phi))
 
     def pdf(self, x, xi, kappa, psi, lmbd, *args, **kwargs):
         r"""
@@ -5547,7 +8120,7 @@ def pdf(self, x, xi, kappa, psi, lmbd, *args, **kwargs):
             Shape parameter, -∞ <= ψ <= ∞. When ψ=-1, the distribution reduces to the wrapped Cauchy,
             when ψ=0, von Mises, and when ψ=1, cardioid.
         lmbd : float
-            Skewness parameter, -1 < λ < 1. Controls the asymmetry introduced by the sine-skewing.
+            Skewness parameter, -1 <= λ <= 1. Controls the asymmetry introduced by the sine-skewing.
 
         Returns
         -------
@@ -5557,6 +8130,114 @@ def pdf(self, x, xi, kappa, psi, lmbd, *args, **kwargs):
 
         return super().pdf(x, xi, kappa, psi, lmbd, *args, **kwargs)
 
+    def _logpdf(self, x, xi, kappa, psi, lmbd):
+        # base-JP log density + log1p(λ sin φ) (P2), mirroring ``_pdf``
+        # branch-for-branch incl. the per-observation regression path;
+        # −inf only at the honest sine-skew zero (|λ| = 1 at sin φ = ∓1)
+        x = np.asarray(x, dtype=float)
+        xi_scalar = _jp_as_scalar(xi)
+        kappa_scalar = _jp_as_scalar(kappa)
+        psi_scalar = _jp_as_scalar(psi)
+        lmbd_scalar = _jp_as_scalar(lmbd)
+
+        if any(v is None for v in (xi_scalar, kappa_scalar, psi_scalar, lmbd_scalar)):
+            xi_b, kappa_b, psi_b, lmbd_b = np.broadcast_arrays(
+                *(np.asarray(a, dtype=float) for a in (xi, kappa, psi, lmbd))
+            )
+            phi = x - xi_b
+            with np.errstate(divide="ignore"):
+                log_skew = np.log1p(lmbd_b * np.sin(phi))
+            logc = _jp_log_c_vec(kappa_b, psi_b)
+            h = _jp_score_terms(phi, kappa_b, psi_b, second=False)["h"]
+            vm = np.abs(psi_b) < _JP_PSI_TOL
+            h = np.where(vm, kappa_b * np.cos(phi), h)
+            return np.where(
+                kappa_b < _JP_KAPPA_TOL,
+                log_skew - np.log(2.0 * np.pi),
+                h + logc + log_skew,
+            )
+
+        phi = x - xi_scalar
+        with np.errstate(divide="ignore"):
+            log_skew = np.log1p(lmbd_scalar * np.sin(phi))
+
+        if abs(kappa_scalar) < _JP_KAPPA_TOL:
+            return log_skew - np.log(2.0 * np.pi)
+
+        log_c = self._get_cached_normalizer(
+            lambda: _jp_log_c(kappa_scalar, psi_scalar),
+            kappa_scalar,
+            psi_scalar,
+        )
+
+        if abs(psi_scalar) < _JP_PSI_TOL:
+            h = kappa_scalar * np.cos(phi)
+        else:
+            h = _jp_score_terms(phi, kappa_scalar, psi_scalar, second=False)["h"]
+        return h + log_c + log_skew
+
+    def logpdf(self, x, xi, kappa, psi, lmbd, *args, **kwargs):
+        r"""
+        Logarithm of the probability density function of the sine-skewed
+        Jones-Pewsey distribution: the base-JP log density plus
+        $\mathrm{log1p}(\lambda\sin(\theta-\xi))$, assembled in log space —
+        finite in underflowed tails, $-\infty$ only at the honest sine-skew
+        zero ($|\lambda| = 1$).
+
+        Accepts per-observation parameter arrays like ``pdf`` (the
+        regression contract).
+
+        Parameters
+        ----------
+        x : array_like
+            Points at which to evaluate the log-density.
+        xi : float
+            Direction parameter, 0 <= xi <= 2*pi.
+        kappa : float
+            Concentration parameter, kappa >= 0.
+        psi : float
+            Shape parameter.
+        lmbd : float
+            Skewness parameter, -1 <= lmbd <= 1.
+
+        Returns
+        -------
+        logpdf_values : array_like
+            Logarithm of the probability density function evaluated at `x`.
+        """
+        return super().logpdf(x, xi, kappa, psi, lmbd, *args, **kwargs)
+
+    def trig_moment(self, p: int = 1, *args, **kwargs) -> complex:
+        """Trigonometric moment via the exact sine-skewing relation (book
+        §4.3.11): with ᾱ_q the centered base-JP cosine moments
+        (``_jp_cos_moment``, one GL-ladder sweep each, exact),
+
+            m_p = [ᾱ_p + iλ(ᾱ_{p−1} − ᾱ_{p+1})/2]·e^{ipξ}.
+        """
+        shape_args, non_shape_kwargs = self._separate_shape_parameters(
+            args, kwargs, "trig_moment"
+        )
+        call_kwargs = self._prepare_call_kwargs(non_shape_kwargs, "trig_moment")
+        xi, kappa, psi, lmbd = (
+            float(np.asarray(v, dtype=float))
+            for v in self._parse_args(*shape_args, **call_kwargs)[0]
+        )
+
+        if not np.isscalar(p):
+            raise ValueError("`p` must be an integer scalar.")
+        if int(round(p)) != p:
+            raise ValueError("`p` must be an integer.")
+        k = int(round(p))
+        if k == 0:
+            return complex(1.0, 0.0)
+        ak = abs(k)
+        a_p = _jp_cos_moment(kappa, psi, ak)
+        b_p = 0.5 * lmbd * (
+            _jp_cos_moment(kappa, psi, ak - 1) - _jp_cos_moment(kappa, psi, ak + 1)
+        )
+        value = (a_p + 1j * b_p) * np.exp(1j * ak * xi)
+        return complex(np.conjugate(value)) if k < 0 else complex(value)
+
     def _cdf(self, x, xi, kappa, psi, lmbd):
         wrapped = self._wrap_angles(x)
         arr = np.asarray(wrapped, dtype=float)
@@ -5581,20 +8262,27 @@ def _cdf(self, x, xi, kappa, psi, lmbd):
         if abs(psi_val) < _JP_PSI_TOL and abs(lmbd_val) < 1e-12:
             return jonespewsey.cdf(arr, mu=xi_val, kappa=kappa_val, psi=psi_val)
 
-        n_idx, coeffs = jonespewsey._jp_get_series(kappa_val, psi_val)
-
         phi_start = (-xi_val) % two_pi
         phi_end = (flat - xi_val) % two_pi
 
-        H_start = float(jonespewsey._jp_series_cumulative(np.array([phi_start]), n_idx, coeffs)[0])
-        H_end = jonespewsey._jp_series_cumulative(phi_end, n_idx, coeffs)
-
-        if abs(lmbd_val) > 0:
-            J_start = float(jonespewsey._jp_series_skew_integral(np.array([phi_start]), n_idx, coeffs)[0])
-            J_end = jonespewsey._jp_series_skew_integral(phi_end, n_idx, coeffs)
+        if _jp_cdf_use_ladder(kappa_val, psi_val):
+            # deep ψ < 0: exact ladder cumulatives for both the base and
+            # the sine-skew term (P1-C)
+            H_s, J_s = _jp_cum01(np.array([phi_start]), kappa_val, psi_val)
+            H_start, J_start = float(H_s[0]), float(J_s[0])
+            H_end, J_end = _jp_cum01(phi_end, kappa_val, psi_val)
         else:
-            J_start = 0.0
-            J_end = np.zeros_like(H_end)
+            n_idx, coeffs = jonespewsey._jp_get_series(kappa_val, psi_val)
+
+            H_start = float(jonespewsey._jp_series_cumulative(np.array([phi_start]), n_idx, coeffs)[0])
+            H_end = jonespewsey._jp_series_cumulative(phi_end, n_idx, coeffs)
+
+            if abs(lmbd_val) > 0:
+                J_start = float(jonespewsey._jp_series_skew_integral(np.array([phi_start]), n_idx, coeffs)[0])
+                J_end = jonespewsey._jp_series_skew_integral(phi_end, n_idx, coeffs)
+            else:
+                J_start = 0.0
+                J_end = np.zeros_like(H_end)
 
         base_cdf = np.where(
             phi_end >= phi_start,
@@ -5615,9 +8303,11 @@ def cdf(self, x, xi, kappa, psi, lmbd, *args, **kwargs):
         r"""
         Cumulative distribution function of the sine-skewed Jones--Pewsey law.
 
-        No closed form is available; the implementation integrates the PDF on
-        [0, 2π) using adaptive quadrature, honouring the symmetric JP and
-        uniform limits when ``lambda`` or ``kappa`` approach zero.
+        No closed form is available; the base-JP Fourier series supplies both
+        the symmetric cumulative and the sine-skew integral, honouring the
+        symmetric JP and uniform limits when ``lambda`` or ``kappa`` approach
+        zero. For deep ψ < 0 spikes beyond the series resolution both terms
+        are evaluated exactly on the kernel's feature-scale ladder instead.
         """
         return super().cdf(x, xi, kappa, psi, lmbd, *args, **kwargs)
 
@@ -5656,6 +8346,11 @@ def _ppf(self, q, xi, kappa, psi, lmbd):
                     theta_vals[interior] = jonespewsey.ppf(
                         q_clipped, mu=xi_val, kappa=kappa_val, psi=psi_val
                     )
+                elif _jp_cdf_use_ladder(kappa_val, psi_val):
+                    # deep ψ < 0: table-initialized exact solve (P1-C)
+                    theta_vals[interior] = _jp_ppf_ladder_sineskewed(
+                        q_clipped, xi_val, kappa_val, psi_val, lmbd_val
+                    )
                 else:
                     theta_curr = two_pi * q_clipped
                     L = np.zeros_like(theta_curr)
@@ -5962,39 +8657,48 @@ class jonespewsey_asym_gen(CircularContinuous):
     pdf(x, xi, kappa, psi, nu)
         Probability density function.
 
+    logpdf(x, xi, kappa, psi, nu)
+        Logarithm of the probability density function.
+
     cdf(x, xi, kappa, psi, nu)
         Cumulative distribution function.
 
+    ppf(q, xi, kappa, psi, nu)
+        Percent-point function (inverse CDF).
+
+    rvs(xi, kappa, psi, nu, size=None, random_state=None)
+        Random variates (kernel-table proposals in u = g(φ) with the
+        bounded warp-weight acceptance).
+
+    fit(data, *, weights=None, method="mle" | "moments", ...)
+        Estimate ``(xi, kappa, psi, nu)`` by maximum likelihood, or
+        return the analytic seed with ``method="moments"``.
 
     Note
     ----
     Parameters must be scalar; cached normalisation tables are built per parameter set.
-    Implementation from 4.3.12 of Pewsey et al. (2014)
+    Implementation from 4.3.12 of Pewsey et al. (2013)
     """
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self._sampler_cache = {}
         self._cdf_table_cache = {}
 
-    def _validate_params(self, xi, kappa, psi, nu):
-        xi_arr, kappa_arr, psi_arr, nu_arr = np.broadcast_arrays(xi, kappa, psi, nu)
+    def _argcheck(self, xi, kappa, psi, nu):
+        try:
+            xi_arr, kappa_arr, psi_arr, nu_arr = np.broadcast_arrays(xi, kappa, psi, nu)
+        except ValueError:
+            return False
         return (
             (xi_arr >= 0.0)
             & (xi_arr <= 2.0 * np.pi)
             & (kappa_arr >= 0.0)
             & np.isfinite(kappa_arr)
             & np.isfinite(psi_arr)
-            & (nu_arr >= 0.0)
+            & (nu_arr > -1.0)
             & (nu_arr < 1.0)
         )
 
-    def _argcheck(self, xi, kappa, psi, nu):
-        try:
-            return self._validate_params(xi, kappa, psi, nu)
-        except ValueError:
-            return False
-
     def _pdf(self, x, xi, kappa, psi, nu):
         x = np.asarray(x, dtype=float)
         xi_scalar = _jp_ensure_scalar(xi, "xi")
@@ -6005,16 +8709,17 @@ def _pdf(self, x, xi, kappa, psi, nu):
         if abs(kappa_scalar) < _JP_KAPPA_TOL:
             return np.full_like(x, 1.0 / (2.0 * np.pi), dtype=float)
 
-        norm = self._get_cached_normalizer(
-            lambda: _c_jonespewsey_asym(xi_scalar, kappa_scalar, psi_scalar, nu_scalar),
-            xi_scalar,
+        log_c = self._get_cached_normalizer(
+            lambda: _jp_log_c_asym(kappa_scalar, psi_scalar, nu_scalar),
             kappa_scalar,
             psi_scalar,
             nu_scalar,
         )
-        self._c = norm
-        base = _kernel_jonespewsey_asym(x, xi_scalar, kappa_scalar, psi_scalar, nu_scalar)
-        return norm * base
+        self._c = float(np.exp(log_c))  # legacy attribute (write-only)
+        phi = x - xi_scalar
+        g = phi + nu_scalar * np.cos(phi)
+        h = _jp_score_terms(g, kappa_scalar, psi_scalar, second=False)["h"]
+        return np.exp(h + log_c)
 
     def pdf(self, x, xi, kappa, psi, nu, *args, **kwargs):
         r"""
@@ -6053,7 +8758,7 @@ def pdf(self, x, xi, kappa, psi, nu, *args, **kwargs):
         psi : float
             Shape parameter, $-\infty \leq \psi \leq \infty$. When $\psi = 0$, the distribution reduces to a simpler von Mises-like form.
         nu : float
-            Asymmetry parameter, $0 \leq \nu < 1$. Introduces skewness in the circular distribution.
+            Asymmetry parameter, $-1 < \nu < 1$. Introduces skewness in the circular distribution.
 
         Returns
         -------
@@ -6069,6 +8774,58 @@ def pdf(self, x, xi, kappa, psi, nu, *args, **kwargs):
         """
         return super().pdf(x, xi, kappa, psi, nu, *args, **kwargs)
 
+    def _logpdf(self, x, xi, kappa, psi, nu):
+        # the log-space assembly ``_pdf`` exponentiates (stable kernel h at
+        # the warped angle g(φ) plus the u-substituted log-normalizer),
+        # returned before the exp so deep-spike tails stay finite (P2)
+        x = np.asarray(x, dtype=float)
+        xi_scalar = _jp_ensure_scalar(xi, "xi")
+        kappa_scalar = _jp_ensure_scalar(kappa, "kappa")
+        psi_scalar = _jp_ensure_scalar(psi, "psi")
+        nu_scalar = _jp_ensure_scalar(nu, "nu")
+
+        if abs(kappa_scalar) < _JP_KAPPA_TOL:
+            return np.full_like(x, -np.log(2.0 * np.pi), dtype=float)
+
+        log_c = self._get_cached_normalizer(
+            lambda: _jp_log_c_asym(kappa_scalar, psi_scalar, nu_scalar),
+            kappa_scalar,
+            psi_scalar,
+            nu_scalar,
+        )
+        phi = x - xi_scalar
+        g = phi + nu_scalar * np.cos(phi)
+        h = _jp_score_terms(g, kappa_scalar, psi_scalar, second=False)["h"]
+        return h + log_c
+
+    def logpdf(self, x, xi, kappa, psi, nu, *args, **kwargs):
+        r"""
+        Logarithm of the probability density function of the asymmetric
+        extended Jones-Pewsey distribution: the stable log-kernel evaluated
+        at the warped angle $g(\phi) = \phi + \nu\cos\phi$ plus the
+        u-substituted log normalizing constant — finite in underflowed
+        tails and at deep $\psi < 0$ spikes.
+
+        Parameters
+        ----------
+        x : array_like
+            Points at which to evaluate the log-density.
+        xi : float
+            Direction parameter, 0 <= xi <= 2*pi.
+        kappa : float
+            Concentration parameter, kappa >= 0.
+        psi : float
+            Shape parameter.
+        nu : float
+            Asymmetry parameter, -1 < nu < 1.
+
+        Returns
+        -------
+        logpdf_values : array_like
+            Logarithm of the probability density function evaluated at `x`.
+        """
+        return super().logpdf(x, xi, kappa, psi, nu, *args, **kwargs)
+
     def _cdf(self, x, xi, kappa, psi, nu):
         wrapped = self._wrap_angles(x)
         arr = np.asarray(wrapped, dtype=float)
@@ -6089,10 +8846,18 @@ def _cdf(self, x, xi, kappa, psi, nu):
         phi_start = (-xi_val) % two_pi
         phi_end = (flat - xi_val) % two_pi
 
-        phi_grid, cdf_grid = self._asym_cdf_table(xi_val, kappa_val, psi_val, nu_val)
+        if _jp_cdf_use_ladder(kappa_val, psi_val):
+            # deep ψ < 0: exact u-space ladder cumulative (P1-C) — the
+            # uniform 4096-point table cannot resolve the spike there
+            H_start = float(
+                _jp_cum01_asym(np.array([phi_start]), kappa_val, psi_val, nu_val)[0]
+            )
+            H_end = _jp_cum01_asym(phi_end, kappa_val, psi_val, nu_val)
+        else:
+            phi_grid, cdf_grid = self._asym_cdf_table(xi_val, kappa_val, psi_val, nu_val)
 
-        H_start = float(np.interp(phi_start, phi_grid, cdf_grid, left=0.0, right=1.0))
-        H_end = np.interp(phi_end, phi_grid, cdf_grid, left=0.0, right=1.0)
+            H_start = float(np.interp(phi_start, phi_grid, cdf_grid, left=0.0, right=1.0))
+            H_end = np.interp(phi_end, phi_grid, cdf_grid, left=0.0, right=1.0)
 
         cdf = np.where(
             phi_end >= phi_start,
@@ -6110,6 +8875,9 @@ def cdf(self, x, xi, kappa, psi, nu, *args, **kwargs):
         precomputing a high-resolution trapezoidal cumulative table for each
         parameter set.  Interpolation of this table gives fast evaluations while
         preserving the limiting cases (nu -> 0 reduces to the symmetric JP CDF).
+        For deep psi < 0 spikes beyond the table resolution the cumulative is
+        instead evaluated exactly in the kernel's own angle u = g(phi) on the
+        same feature-scale ladder that serves the normalizer and the sampler.
         """
         return super().cdf(x, xi, kappa, psi, nu, *args, **kwargs)
 
@@ -6142,8 +8910,14 @@ def _ppf(self, q, xi, kappa, psi, nu):
                 q_int = q_valid[interior]
                 eps = 1e-15
                 q_clipped = np.clip(q_int, eps, 1.0 - eps)
-                if kappa_val < _JP_KAPPA_TOL and nu_val < 1e-12:
+                if kappa_val < _JP_KAPPA_TOL and abs(nu_val) < 1e-12:
                     theta_vals[interior] = two_pi * q_clipped
+                elif _jp_cdf_use_ladder(kappa_val, psi_val):
+                    # deep ψ < 0: table-initialized exact u-space solve
+                    # (P1-C)
+                    theta_vals[interior] = _jp_ppf_ladder_asym(
+                        q_clipped, xi_val, kappa_val, psi_val, nu_val
+                    )
                 else:
                     theta_curr = two_pi * q_clipped
                     L = np.zeros_like(theta_curr)
@@ -6220,8 +8994,10 @@ def ppf(self, q, xi, kappa, psi, nu, *args, **kwargs):
         Quantile function of the asymmetric Jones--Pewsey distribution.
 
         Quantiles are obtained by the same safeguarded Newton iteration as in
-        the symmetric case, with the warp-aware CDF supplying residuals.  When
-        nu is effectively zero the method delegates to the symmetric JP solver.
+        the symmetric case, with the warp-aware CDF supplying residuals.  For
+        deep psi < 0 spikes the solve runs in the kernel's own angle against
+        the exact u-space cumulative, initialized from the kernel's quantile
+        table, and the warp is inverted by bisection at the end.
         """
         return super().ppf(q, xi, kappa, psi, nu, *args, **kwargs)
 
@@ -6233,8 +9009,8 @@ def _rvs(self, xi, kappa, psi, nu, size=None, random_state=None):
         kappa_val = _jp_ensure_scalar(kappa, "kappa")
         psi_val = _jp_ensure_scalar(psi, "psi")
         nu_val = _jp_ensure_scalar(nu, "nu")
-        if not (0.0 <= nu_val < 1.0):
-            raise ValueError("`nu` must lie in [0, 1).")
+        if not (-1.0 < nu_val < 1.0):
+            raise ValueError("`nu` must lie in (-1, 1).")
 
         if size is None:
             size_tuple = ()
@@ -6251,29 +9027,36 @@ def _rvs(self, xi, kappa, psi, nu, size=None, random_state=None):
             samples = rng.uniform(0.0, two_pi, size=total)
             return samples.reshape(size_tuple)
 
-        if abs(psi_val) < _JP_PSI_TOL and nu_val < 1e-12:
+        if abs(psi_val) < _JP_PSI_TOL and abs(nu_val) < 1e-12:
             return vonmises.rvs(mu=xi_val, kappa=kappa_val, size=size_tuple or None, random_state=rng)
 
-        kappa_env, envelope_const = self._asym_sampler_envelope(xi_val, kappa_val, psi_val, nu_val)
+        # In the kernel's own angle U = g(Φ) the target law has density
+        # ∝ kernel(u)/g′(g⁻¹(u)): propose u from the pure-kernel table
+        # (exact at any spike depth — see ``_jp_quantile_table``), accept
+        # with the bounded weight ratio (1−|ν|)/g′ ≤ 1 (min g′ = 1 − |ν|
+        # for either sign of ν), then invert the monotone warp by
+        # bisection. (The previous von Mises rejection envelope shared the
+        # symmetric sampler's blindness to sub-grid ψ < 0 spikes.)
+        two_pi_f = 2.0 * np.pi
         samples = np.empty(total, dtype=float)
         filled = 0
-
         while filled < total:
             remaining = total - filled
-            proposals = vonmises.rvs(
-                mu=xi_val,
-                kappa=kappa_env,
-                size=remaining,
-                random_state=rng,
+            u_prop = _jp_sample_table(kappa_val, psi_val, remaining, rng)
+            # map into g's principal range [−π−ν, π−ν] (kernel is
+            # periodic; for ν > 0 the table's upper sliver folds down, for
+            # ν < 0 the lower sliver folds up)
+            u_prop = np.where(u_prop > np.pi - nu_val, u_prop - two_pi_f, u_prop)
+            u_prop = np.where(u_prop < -np.pi - nu_val, u_prop + two_pi_f, u_prop)
+            phi = _jp_warp_inv(u_prop, nu_val)
+            accept = rng.uniform(0.0, 1.0, size=remaining) <= (
+                (1.0 - abs(nu_val)) / (1.0 - nu_val * np.sin(phi))
             )
-            target_vals = self.pdf(proposals, xi_val, kappa_val, psi_val, nu_val)
-            proposal_vals = vonmises.pdf(proposals, mu=xi_val, kappa=kappa_env)
-            ratio = np.where(proposal_vals > 0.0, target_vals / (envelope_const * proposal_vals), 0.0)
-            u = rng.uniform(0.0, 1.0, size=remaining)
-            accept = ratio >= u
             n_accept = int(np.sum(accept))
             if n_accept > 0:
-                samples[filled:filled + n_accept] = proposals[accept][:n_accept]
+                samples[filled:filled + n_accept] = np.mod(
+                    xi_val + phi[accept][:n_accept], two_pi_f
+                )
                 filled += n_accept
 
         return samples.reshape(size_tuple)
@@ -6282,36 +9065,15 @@ def rvs(self, xi, kappa, psi, nu, size=None, random_state=None):
         r"""
         Draw random variates from the asymmetric Jones--Pewsey distribution.
 
-        Sampling uses a curvature-matched von Mises envelope tuned via the
-        optimisation helper, providing an exact acceptance-rejection scheme that
-        works well across nu in [0, 1).  Uniform and symmetric limits are
-        handled explicitly.
+        Sampling works in the kernel's own angle: proposals come from the
+        symmetric kernel's quantile table (exact at any concentration), the
+        warp's Jacobian enters as a bounded acceptance weight
+        ``(1−|ν|)/g′ ≤ 1`` (min g′ = 1 − |ν| for either sign), and the
+        monotone warp is inverted by
+        bisection. Uniform and von Mises limits are handled explicitly.
         """
         return super().rvs(xi, kappa, psi, nu, size=size, random_state=random_state)
 
-    def _asym_sampler_envelope(self, xi, kappa, psi, nu):
-        key = (float(np.mod(xi, 2.0 * np.pi)), float(kappa), float(psi), float(nu))
-        cached = self._sampler_cache.get(key)
-        if cached is not None:
-            return cached
-
-        kappa_env = _jp_effective_kappa(kappa, psi)
-        phi_grid = np.linspace(0.0, 2.0 * np.pi, 2048, endpoint=False)
-        theta_grid = np.mod(xi + phi_grid, 2.0 * np.pi)
-
-        target_vals = self.pdf(theta_grid, xi, kappa, psi, nu)
-        log_target = np.log(np.clip(target_vals, np.finfo(float).tiny, None))
-
-        kappa_env, envelope_const = _optimize_vonmises_envelope(
-            theta_grid,
-            log_target,
-            xi,
-            max(kappa_env, 1e-6),
-        )
-
-        self._sampler_cache[key] = (kappa_env, envelope_const)
-        return kappa_env, envelope_const
-
     def _asym_cdf_table(self, xi, kappa, psi, nu, grid_size=4096):
         key = (float(np.mod(xi, 2.0 * np.pi)), float(kappa), float(psi), float(nu), int(grid_size))
         cached = self._cdf_table_cache.get(key)
@@ -6342,21 +9104,53 @@ def fit(
         data,
         *,
         weights=None,
+        method="mle",
         return_info=False,
         optimizer="L-BFGS-B",
         psi_bounds=(-4.0, 4.0),
         kappa_bounds=(1e-6, 1e3),
-        nu_bounds=(0.0, 0.99),
+        nu_bounds=(-0.99, 0.99),
         base_kwargs=None,
         **kwargs,
     ):
         r"""
-        Estimate asymmetric JP parameters by maximum likelihood.
+        Estimate asymmetric JP parameters ``(xi, kappa, psi, nu)``.
+
+        The symmetric JP fit supplies starting values for (xi, kappa, psi)
+        with nu initialised at zero.  With ``method="mle"`` the full
+        four-parameter log-likelihood is then optimised under simple
+        bounds, re-using the cached normalising constant machinery of the
+        JP core; ``method="moments"`` skips all optimisation and returns
+        the analytic seed (the symmetric base's moment estimates with
+        ``nu = 0``).
+
+        Parameters
+        ----------
+        data : array_like
+            Sample angles (radians), wrapped internally.
+        weights : array_like, optional
+            Non-negative weights broadcastable to ``data``.
+        method : {"mle", "moments"}, optional
+            Full four-parameter MLE (default; aliases: "numerical") or
+            the analytic seed (alias: "analytical").
+        return_info : bool, optional
+            If True, also return a diagnostics dictionary.
+        optimizer : str, optional
+            Name of the ``scipy.optimize.minimize`` method.
+        psi_bounds, kappa_bounds, nu_bounds : tuple, optional
+            Parameter bounds used by the optimiser.
+        base_kwargs : dict, optional
+            Extra keyword arguments forwarded to the symmetric
+            ``jonespewsey.fit`` seeding call.
+        **kwargs :
+            Additional keyword arguments forwarded to the optimiser
+            (ignored under ``method="moments"``).
 
-        The symmetric JP fit supplies starting values for (xi, kappa, psi) with
-        nu initialised at zero.  The full four-parameter log-likelihood is then
-        optimised under simple bounds, re-using the cached normalising constant
-        and envelope machinery developed for the JP core.
+        Returns
+        -------
+        tuple or (tuple, dict)
+            Estimated parameters ``(xi, kappa, psi, nu)`` and, optionally,
+            fit diagnostics when ``return_info`` is True.
         """
         kwargs = self._clean_loc_scale_kwargs(kwargs, caller="fit")
         x = self._wrap_angles(np.asarray(data, dtype=float)).ravel()
@@ -6376,7 +9170,40 @@ def fit(
             raise ValueError("Sum of weights must be positive.")
         n_eff = w_sum**2 / np.sum(w**2)
 
+        method_key = method.lower()
+        alias = {"analytical": "moments", "numerical": "mle"}
+        method_key = alias.get(method_key, method_key)
+        if method_key not in {"moments", "mle"}:
+            raise ValueError("`method` must be either 'moments' or 'mle'.")
+
         base_kwargs = {} if base_kwargs is None else dict(base_kwargs)
+
+        if method_key == "moments":
+            seed_estimates, base_info = jonespewsey.fit(
+                x,
+                weights=w,
+                method="moments",
+                psi_bounds=psi_bounds,
+                kappa_bounds=kappa_bounds,
+                optimizer=optimizer,
+                return_info=True,
+                **base_kwargs,
+            )
+            xi_hat, kappa_hat, psi_hat = seed_estimates
+            nu_hat = 0.0
+            seed_pdf = self.pdf(x, xi_hat, kappa_hat, psi_hat, nu_hat)
+            estimates = (xi_hat, kappa_hat, psi_hat, nu_hat)
+            if return_info:
+                info = {
+                    "method": "moments",
+                    "base": base_info,
+                    "loglik": float(np.sum(w * np.log(seed_pdf))),
+                    "n_effective": float(n_eff),
+                    "converged": True,
+                }
+                return estimates, info
+            return estimates
+
         init_estimates, base_info = jonespewsey.fit(
             x,
             weights=w,
@@ -6436,6 +9263,7 @@ def nll(params):
         estimates = (xi_hat, kappa_hat, psi_hat, nu_hat)
         if return_info:
             info = {
+                "method": "mle",
                 "base": base_info,
                 "loglik": loglik,
                 "converged": bool(result.success),
@@ -6450,26 +9278,170 @@ def nll(params):
 jonespewsey_asym = jonespewsey_asym_gen(name="jonespewsey_asym")
 
 
-def _kernel_jonespewsey_asym(x, xi, kappa, psi, nu):
-    x = np.asarray(x, dtype=float)
-    phi = x - xi
-    phi = phi + nu * np.cos(phi)
-    return _jp_kernel_base(phi, kappa, psi)
-
-
-def _c_jonespewsey_asym(xi, kappa, psi, nu):
+def _jp_warp_inv(u, nu):
+    """φ = g⁻¹(u) on the principal branch of the asymmetry warp
+    g(φ) = φ + ν cos φ, by bisection — monotone (g′ ≥ 1 − ν > 0), and 60
+    halvings (2π/2⁶⁰ ≈ 5e-18) are beyond what the smooth weight consuming
+    φ(u) can distinguish."""
+    u = np.asarray(u, dtype=float)
+    a = np.full_like(u, -np.pi)
+    b = np.full_like(u, np.pi)
+    for _ in range(60):
+        m = 0.5 * (a + b)
+        too_high = m + nu * np.cos(m) > u
+        b = np.where(too_high, m, b)
+        a = np.where(too_high, a, m)
+    return 0.5 * (a + b)
+
+
+def _jp_ladder_edges_asym(kappa, psi, nu):
+    """Break-point ladder in the kernel's own angle u = g(φ) over one
+    period [−π − ν, π − ν]: decade rungs at the ``_jp_feature_scales`` of
+    the peak (u = 0, interior) and the antipodal near-kink (u ≡ ±π; for
+    ν > 0 only −π is interior, for ν < 0 only +π — candidates outside the
+    window are filtered), plus rungs for the weight 1/g′'s own bump at
+    u = g(±π/2) = ±π/2 (the +π/2 bump for ν > 0, −π/2 for ν < 0) of
+    u-width ~(1−|ν|)^{3/2} — unresolved it costs ~1e-5 relative at
+    ν = 0.9 (the kernel ladders have no rungs mid-window)."""
+    two_pi = 2.0 * np.pi
+    lo, hi = -np.pi - nu, np.pi - nu
+    w_peak, w_anti = _jp_feature_scales(kappa, psi)
+    edge_set = {lo, hi, 0.0}
+    r = w_peak
+    while r < two_pi:
+        for cand in (-r, r):
+            if lo < cand < hi:
+                edge_set.add(cand)
+        r *= 10.0
+    r = w_anti
+    while r < two_pi:
+        for cand in (-np.pi - r, -np.pi + r, np.pi - r, np.pi + r):
+            if lo < cand < hi:
+                edge_set.add(cand)
+        r *= 10.0
+    # the 1/g′ weight bump sits at u = g(±π/2) = ±π/2 (at +π/2 for ν > 0,
+    # at −π/2 for ν < 0); rung both, the window filter drops the inert one
+    r = max((1.0 - abs(nu)) ** 1.5, 1e-3)
+    while r < two_pi:
+        for cand in (0.5 * np.pi - r, 0.5 * np.pi + r,
+                     -0.5 * np.pi - r, -0.5 * np.pi + r):
+            if lo < cand < hi:
+                edge_set.add(cand)
+        r *= 10.0
+    return np.asarray(sorted(edge_set))
+
+
+@lru_cache(maxsize=4096)
+def _jp_log_c_asym(kappa: float, psi: float, nu: float) -> float:
+    """log normalizing constant of the asymmetric-extended JP kernel
+    (ξ-invariant), via the substitution u = g(φ) = φ + ν cos φ:
+
+        ∫ kernel(g(φ)) dφ  =  ∫_{−π−ν}^{π−ν} kernel(u) / g′(g⁻¹(u)) du.
+
+    Working in the kernel's own angle u is what makes the spike resolvable:
+    the warp puts the peak at a generic φ* where adjacent doubles are
+    ~|φ*|·1e-16 apart, so for deep ψ < 0 the spike (width ~2e^{−|κψ|})
+    falls *between representable numbers* and no φ-space ladder can see
+    it — in u the peak sits at exactly 0, where doubles are denormally
+    dense, and the kernel features are at the known points 0/±π (no
+    root-solving). The warp survives only as the smooth bounded weight
+    1/g′ ∈ [1/(1+ν), 1/(1−ν)], whose argument g⁻¹(u) a float-limited
+    bisection serves perfectly well. One vectorized ``_jp_score_terms``
+    call over the GL ladder (the scalar-node adaptive quadrature this
+    replaces paid ~100 µs of tiny-array numpy per node).
+    """
     if kappa < _JP_KAPPA_TOL:
-        return 1.0 / (2.0 * np.pi)
-
-    integral = quad_vec(
-        _kernel_jonespewsey_asym,
-        a=-np.pi,
-        b=np.pi,
-        args=(xi, kappa, psi, nu),
-        epsabs=1e-10,
-        epsrel=1e-10,
-    )[0]
-    return 1.0 / integral
+        return float(-np.log(2.0 * np.pi))
+
+    nodes, wts = _gl_panels_from_edges(_jp_ladder_edges_asym(kappa, psi, nu))
+    weight = 1.0 / (1.0 - nu * np.sin(_jp_warp_inv(nodes, nu)))
+    h = _jp_log_kernel(nodes, kappa, psi)
+    integral = float(np.sum(wts * np.exp(h - kappa) * weight))
+    return float(-(kappa + np.log(max(integral, np.finfo(float).tiny))))
+
+
+@lru_cache(maxsize=1024)
+def _jp_cdf_ladder_asym(kappa: float, psi: float, nu: float):
+    """Cumulative table for the asymmetric-extended JP law in the kernel's
+    own angle: GL-exact cumulatives of kernel(u)/g′(g⁻¹(u)) (peak value
+    suppressed to e^{h−κ}) at the ``_jp_ladder_edges_asym`` panel edges
+    over [−π − ν, π − ν]. Returns ``(edges, cum)`` with ``cum[0] = 0``."""
+    edges = _jp_ladder_edges_asym(kappa, psi, nu)
+    nodes, wts = _gl_panels_from_edges(edges)
+    weight = 1.0 / (1.0 - nu * np.sin(_jp_warp_inv(nodes, nu)))
+    h = _jp_log_kernel(nodes, kappa, psi)
+    n_gl = _JP_GL_XW[0].size
+    panels = (wts * np.exp(h - kappa) * weight).reshape(
+        edges.size - 1, n_gl
+    ).sum(axis=1)
+    return edges, np.concatenate([[0.0], np.cumsum(panels)])
+
+
+def _jp_weighted_cum_asym(u, kappa, psi, nu):
+    """Cw(u) = ∫_{−π−ν}^{u} e^{h(t)−κ}/g′(g⁻¹(t)) dt: cached edge
+    cumulatives plus a 24-point partial panel per query (the aeJP analog
+    of ``_jp_half_cum``; no half-line fold — the weight is not even)."""
+    edges, cum = _jp_cdf_ladder_asym(kappa, psi, nu)
+    u = np.clip(np.asarray(u, dtype=float).reshape(-1), edges[0], edges[-1])
+    k = np.clip(np.searchsorted(edges, u, side="right") - 1, 0, edges.size - 2)
+    hw = 0.5 * (u - edges[k])
+    xi_gl, w_gl = _JP_GL_XW
+    nodes = (edges[k] + hw)[:, None] + hw[:, None] * xi_gl[None, :]
+    weight = 1.0 / (1.0 - nu * np.sin(_jp_warp_inv(nodes, nu)))
+    h = _jp_log_kernel(nodes, kappa, psi)
+    return cum[k] + hw * ((np.exp(h - kappa) * weight) @ w_gl)
+
+
+def _jp_cum01_asym(s, kappa, psi, nu):
+    """Exact H(s) = ∫₀ˢ f_ae(ξ + t) dt over s ∈ [0, 2π) for the
+    asymmetric-extended JP law (ξ-invariant): fold s into the centered
+    offset ζ ∈ (−π, π], map through the warp u = g(ζ), and difference the
+    u-space cumulative against the mass below the start point g(0) = ν.
+    Same denormal best-effort boundary as ``_jp_cum01``."""
+    s = np.asarray(s, dtype=float).reshape(-1)
+    upper = s > np.pi
+    zeta = np.where(upper, s - 2.0 * np.pi, s)
+    u = zeta + nu * np.cos(zeta)
+    edges, cum = _jp_cdf_ladder_asym(kappa, psi, nu)
+    z = max(float(cum[-1]), np.finfo(float).tiny)
+    C = _jp_weighted_cum_asym(u, kappa, psi, nu) / z
+    C_nu = float(_jp_weighted_cum_asym(np.array([nu]), kappa, psi, nu)[0]) / z
+    return np.where(upper, 1.0 - C_nu + C, C - C_nu)
+
+
+def _jp_ppf_ladder_asym(q, xi, kappa, psi, nu):
+    """Deep-regime aeJP quantiles: solve C(u) = target in the kernel's own
+    angle (spike at u = 0), initialized weight-blind from the symmetric
+    kernel's quantile table (the weight is a bounded smooth factor the
+    Newton polish absorbs), then invert the warp. ``q`` must be
+    interior."""
+    two_pi = 2.0 * np.pi
+    H_start = float(
+        _jp_cum01_asym(np.array([(-xi) % two_pi]), kappa, psi, nu)[0]
+    )
+    edges, cum = _jp_cdf_ladder_asym(kappa, psi, nu)
+    z = max(float(cum[-1]), np.finfo(float).tiny)
+    C_nu = float(_jp_weighted_cum_asym(np.array([nu]), kappa, psi, nu)[0]) / z
+    u_t = (np.asarray(q, dtype=float) + H_start + C_nu) % 1.0
+    u0 = _jp_table_invert(u_t, kappa, psi)
+    # the kernel table spans [−π, π]; fold the out-of-window sliver onto
+    # the equivalent stretch of the aeJP period [−π − ν, π − ν] (upper
+    # sliver for ν > 0, lower for ν < 0 — cf. _rvs)
+    u0 = np.where(u0 > np.pi - nu, u0 - two_pi, u0)
+    u0 = np.where(u0 < -np.pi - nu, u0 + two_pi, u0)
+
+    def cdf_fn(u):
+        return _jp_weighted_cum_asym(u, kappa, psi, nu) / z
+
+    def pdf_fn(u):
+        weight = 1.0 / (1.0 - nu * np.sin(_jp_warp_inv(u, nu)))
+        h = _jp_log_kernel(u, kappa, psi)
+        return np.exp(h - kappa) * weight / z
+
+    u_root = _jp_solve_quantile(
+        u_t, u0, -np.pi - nu, np.pi - nu, cdf_fn, pdf_fn
+    )
+    return np.mod(xi + _jp_warp_inv(u_root, nu), two_pi)
 
 
 class inverse_batschelet_gen(CircularContinuous):
@@ -6477,10 +9449,10 @@ class inverse_batschelet_gen(CircularContinuous):
 
     ![inverse-batschelet](../images/circ-mod-inverse-batschelet.png)
 
-    The inverse Batschelet family (Pewsey, Neuhaüser & Ruxton, 2014, §4.3.13)
+    The inverse Batschelet family (Pewsey, Neuhäuser & Ruxton, 2013, §4.3.13)
     extends the von Mises distribution by applying two inverse angular warps:
-    a "peakedness" transform controlled by $\nu$, and an inverse
-    Batschelet skew transform governed by $\lambda$. The resulting density on
+    a skewness transform controlled by $\nu$, and an inverse
+    Batschelet peakedness transform governed by $\lambda$. The resulting density on
     $[0, 2\pi)$ takes the form
 
     $$
@@ -6501,6 +9473,9 @@ class inverse_batschelet_gen(CircularContinuous):
     pdf(x, xi, kappa, nu, lmbd)
         Probability density function.
 
+    logpdf(x, xi, kappa, nu, lmbd)
+        Logarithm of the probability density function.
+
     cdf(x, xi, kappa, nu, lmbd)
         Cumulative distribution function.
 
@@ -6524,8 +9499,11 @@ def _clear_normalization_cache(self):
         self._invbat_table_cache = {}
         self._invbat_sampler_cache = {}
 
-    def _validate_params(self, xi, kappa, nu, lmbd):
-        xi_arr, kappa_arr, nu_arr, lmbd_arr = np.broadcast_arrays(xi, kappa, nu, lmbd)
+    def _argcheck(self, xi, kappa, nu, lmbd):
+        try:
+            xi_arr, kappa_arr, nu_arr, lmbd_arr = np.broadcast_arrays(xi, kappa, nu, lmbd)
+        except ValueError:
+            return False
         return (
             (xi_arr >= 0.0)
             & (xi_arr <= 2.0 * np.pi)
@@ -6533,15 +9511,9 @@ def _validate_params(self, xi, kappa, nu, lmbd):
             & np.isfinite(kappa_arr)
             & (nu_arr >= -1.0)
             & (nu_arr <= 1.0)
-            & (lmbd_arr >= -1.0)
-            & (lmbd_arr <= 1.0)
-        )
-
-    def _argcheck(self, xi, kappa, nu, lmbd):
-        try:
-            return self._validate_params(xi, kappa, nu, lmbd)
-        except ValueError:
-            return False
+            & (lmbd_arr >= -1.0)
+            & (lmbd_arr <= 1.0)
+        )
 
     def _pdf(self, x, xi, kappa, nu, lmbd):
         scalar_input = np.isscalar(x)
@@ -6629,9 +9601,9 @@ def pdf(self, x, xi, kappa, nu, lmbd, *args, **kwargs):
         kappa : float
             Concentration parameter, $\kappa \geq 0$. Higher values result in sharper peaks around $\xi$.
         nu : float
-            Shape parameter, $-1 \leq \nu \leq 1$. Controls asymmetry through angular transformation.
+            Skewness parameter, $-1 \leq \nu \leq 1$. Controls asymmetry through the angular transformation.
         lmbd : float
-            Skewness parameter, $-1 \leq \lambda \leq 1$. Controls the degree of skewness in the distribution.
+            Peakedness parameter, $-1 \leq \lambda \leq 1$. Controls the peak shape, from flat-topped to sharply peaked.
 
         Returns
         -------
@@ -6640,6 +9612,84 @@ def pdf(self, x, xi, kappa, nu, lmbd, *args, **kwargs):
         """
         return super().pdf(x, xi, kappa, nu, lmbd, *args, **kwargs)
 
+    def _logpdf(self, x, xi, kappa, nu, lmbd):
+        # log(normalizer) + log-kernel — the assembly ``_pdf`` exponentiates
+        # (P2): the warped vM kernel underflows from κ ≈ 360 at the
+        # antipodal flank while the log form stays finite across the
+        # κ ≤ 700 range
+        scalar_input = np.isscalar(x)
+        x_arr = np.asarray([x], dtype=float) if scalar_input else np.asarray(x, dtype=float)
+        if x_arr.size == 0:
+            return x_arr.astype(float)
+
+        xi_val = _invbat_ensure_scalar(xi, "xi")
+        kappa_val = float(np.clip(_invbat_ensure_scalar(kappa, "kappa"), 0.0, _INVBAT_KAPPA_UPPER))
+        nu_val = _invbat_ensure_scalar(nu, "nu")
+        lmbd_val = _invbat_ensure_scalar(lmbd, "lmbd")
+
+        if not (
+            np.isfinite(xi_val)
+            and np.isfinite(kappa_val)
+            and np.isfinite(nu_val)
+            and np.isfinite(lmbd_val)
+        ):
+            result = np.full_like(x_arr, np.nan, dtype=float)
+            return float(result[0]) if scalar_input else result
+
+        if kappa_val <= _INVBAT_KAPPA_TOL:
+            result = np.full_like(x_arr, -np.log(2.0 * np.pi), dtype=float)
+            return float(result[0]) if scalar_input else result
+
+        normalizer = self._get_cached_normalizer(
+            lambda: _c_invbatschelet(kappa_val, lmbd_val),
+            kappa_val,
+            lmbd_val,
+        )
+        if not np.isfinite(normalizer) or normalizer <= 0.0:
+            normalizer = _c_invbatschelet_numeric(kappa_val, lmbd_val, grid_size=_INVBAT_NUMERIC_GRID)
+
+        phi = _tnu(x_arr, nu_val, xi_val)
+        skew = _slmbdinv(phi, lmbd_val)
+
+        if np.isclose(lmbd_val, -1.0):
+            log_kernel = kappa_val * np.cos(phi - np.sin(phi))
+        else:
+            con1 = (1.0 - lmbd_val) / (1.0 + lmbd_val)
+            con2 = (2.0 * lmbd_val) / (1.0 + lmbd_val)
+            log_kernel = kappa_val * np.cos(con1 * phi + con2 * skew)
+
+        logpdf_vals = np.log(normalizer) + log_kernel
+        if scalar_input:
+            return float(logpdf_vals.reshape(-1)[0])
+        return logpdf_vals
+
+    def logpdf(self, x, xi, kappa, nu, lmbd, *args, **kwargs):
+        r"""
+        Logarithm of the probability density function of the inverse
+        Batschelet distribution: the log normalizing constant plus the
+        warped von Mises log-kernel — finite across the advertised
+        parameter range, including tails where the density underflows.
+
+        Parameters
+        ----------
+        x : array_like
+            Points at which to evaluate the log-density.
+        xi : float
+            Direction parameter, 0 <= xi <= 2*pi.
+        kappa : float
+            Concentration parameter, 0 <= kappa <= 700.
+        nu : float
+            Skewness parameter, -1 <= nu <= 1.
+        lmbd : float
+            Peakedness parameter, -1 <= lmbd <= 1.
+
+        Returns
+        -------
+        logpdf_values : array_like
+            Logarithm of the probability density function evaluated at `x`.
+        """
+        return super().logpdf(x, xi, kappa, nu, lmbd, *args, **kwargs)
+
     def _cdf(self, x, xi, kappa, nu, lmbd):
         wrapped = self._wrap_angles(x)
         arr = np.asarray(wrapped, dtype=float)
@@ -6693,8 +9743,8 @@ def cdf(self, x, xi, kappa, nu, lmbd, *args, **kwargs):
 
         The implementation precomputes the normalised primitive on a periodic grid
         in the centred angle $\varphi = (\theta - \xi) \bmod 2\pi - \pi$. For each
-        grid node, the inverse peakedness transform $t_\nu^{-1}$ and inverse
-        Batschelet skew $s_\lambda^{-1}$ are evaluated, and the resulting kernel is
+        grid node, the inverse skewness transform $t_\nu^{-1}$ and inverse
+        Batschelet peakedness $s_\lambda^{-1}$ are evaluated, and the resulting kernel is
         accumulated via a trapezoidal rule. The cumulative table is cached per
         parameter triple $(\kappa, \nu, \lambda)$, enabling $O(1)$ queries after the
         initial $O(N)$ precomputation. The limit $\kappa \to 0$ reduces to the
@@ -6709,9 +9759,9 @@ def cdf(self, x, xi, kappa, nu, lmbd, *args, **kwargs):
         kappa : float
             Concentration parameter, $\kappa \geq 0$.
         nu : float
-            Shape parameter, $-1 \leq \nu \leq 1$.
+            Skewness parameter, $-1 \leq \nu \leq 1$.
         lmbd : float
-            Skewness parameter, $-1 \leq \lambda \leq 1$.
+            Peakedness parameter, $-1 \leq \lambda \leq 1$.
 
         Returns
         -------
@@ -6834,9 +9884,9 @@ def ppf(self, q, xi, kappa, nu, lmbd, *args, **kwargs):
         kappa : float
             Concentration parameter, $\kappa \geq 0$.
         nu : float
-            Shape parameter, $-1 \leq \nu \leq 1$.
+            Skewness parameter, $-1 \leq \nu \leq 1$.
         lmbd : float
-            Skewness parameter, $-1 \leq \lambda \leq 1$.
+            Peakedness parameter, $-1 \leq \lambda \leq 1$.
 
         Returns
         -------
@@ -6984,9 +10034,9 @@ def rvs(self, xi=None, kappa=None, nu=None, lmbd=None, size=None, random_state=N
         kappa : float
             Concentration parameter, $\kappa \geq 0$.
         nu : float
-            Shape parameter, $-1 \leq \nu \leq 1$.
+            Skewness parameter, $-1 \leq \nu \leq 1$.
         lmbd : float
-            Skewness parameter, $-1 \leq \lambda \leq 1$.
+            Peakedness parameter, $-1 \leq \lambda \leq 1$.
         size : int or tuple of ints, optional
             Desired output shape.
         random_state : {None, int, np.random.Generator}, optional
@@ -7290,10 +10340,10 @@ def _build_invbat_table(self, kappa, nu, lmbd, grid_size):
 
         cdf_interp = PchipInterpolator(phi, cumulative, extrapolate=True)
 
-        unique_vals, unique_idx = np.unique(cumulative, return_index=True)
+        knots = _inverse_cdf_knots(phi, cumulative)
         inv_cdf_interp = (
-            PchipInterpolator(unique_vals, phi[unique_idx], extrapolate=True)
-            if unique_vals.size >= 2
+            PchipInterpolator(knots[0], knots[1], extrapolate=True)
+            if knots is not None
             else None
         )
 
@@ -7378,7 +10428,7 @@ def _equation(u):
 
 
 def _A1(kappa):
-    return i1(kappa) / i0(kappa)
+    return i1e(kappa) / i0e(kappa)  # scaled: i1/i0 is nan for κ ≥ 713
 
 
 def _c_invbatschelet(kappa, lmbd):
@@ -7488,10 +10538,13 @@ class wrapstable_gen(CircularContinuous):
     \mu_p =
     \begin{cases}
         \delta p + \beta \tan\left(\tfrac{\pi\alpha}{2}\right)\bigl((\gamma p)^\alpha - \gamma p\bigr), & \alpha \ne 1, \\[6pt]
-        \delta p + \tfrac{2}{\pi}\beta\gamma p \log(\gamma p), & \alpha = 1.
+        \delta p - \tfrac{2}{\pi}\beta\gamma p \log(\gamma p), & \alpha = 1,
     \end{cases}
     $$
 
+    the S0 (Nolan) parameterization of Pewsey (2008), jointly continuous in
+    all four parameters.
+
     Special cases include the wrapped normal (``α=2, β=0``), wrapped Cauchy
     (``α=1, β=0``), and wrapped Lévy (``α=1/2, β=1``).
 
@@ -7500,6 +10553,10 @@ class wrapstable_gen(CircularContinuous):
     pdf(x, delta, alpha, beta, gamma)
         Probability density function via adaptive Fourier series.
 
+    logpdf(x, delta, alpha, beta, gamma)
+        Logarithm of the probability density function (series, floored at
+        float-tiny — the series is its own accuracy floor).
+
     cdf(x, delta, alpha, beta, gamma)
         Analytic cumulative distribution function using integrated series.
 
@@ -7518,6 +10575,22 @@ class wrapstable_gen(CircularContinuous):
 
     Parameters must be scalar; Fourier series coefficients are cached per
     parameter set.
+
+    **Small-α boundary (performance over diagnostics, by design).** As
+    α → 0 the law approaches an atom-plus-uniform mixture
+    (ρ_p = e^{−(γp)^α} → e^{−1} for *every* harmonic) which has no
+    density, and the number of terms a faithful density series would
+    need grows like (−ln ε)^{1/α}/γ. The series build is therefore
+    capped at 20 000 terms (`_WRAPSTABLE_MAX_TERMS`) and the cap engages
+    *silently*: below the cap-onset α (≈ 0.38 at γ = 0.7; earlier for
+    smaller γ) `pdf` degrades to a truncated-kernel artifact —
+    oscillatory, with negative side-lobes floored by `logpdf`'s tiny
+    clamp — while `cdf`/`ppf` inherit only the milder 1/p-damped
+    truncation error and stay monotone, and `trig_moment` is exact at
+    any α (closed characteristic-function form, no truncation). No
+    warning is raised and α is not floored: the cap is the performance
+    guard, and callers needing densities in the deep-α regime should
+    treat α ≲ 0.4 as out of the series' faithful range.
     """
 
     def __init__(self, *args, **kwargs):
@@ -7528,24 +10601,21 @@ def _clear_normalization_cache(self):
         super()._clear_normalization_cache()
         self._series_cache = {}
 
-    def _validate_params(self, delta, alpha, beta, gamma):
-        delta_arr, alpha_arr, beta_arr, gamma_arr = np.broadcast_arrays(delta, alpha, beta, gamma)
+    def _argcheck(self, delta, alpha, beta, gamma):
+        try:
+            delta_arr, alpha_arr, beta_arr, gamma_arr = np.broadcast_arrays(delta, alpha, beta, gamma)
+        except ValueError:
+            return False
         return (
             (delta_arr >= 0.0)
             & (delta_arr <= 2.0 * np.pi)
             & (alpha_arr > 0.0)
             & (alpha_arr <= 2.0)
-            & (beta_arr > -1.0)
-            & (beta_arr < 1.0)
+            & (beta_arr >= -1.0)
+            & (beta_arr <= 1.0)
             & (gamma_arr > 0.0)
         )
 
-    def _argcheck(self, delta, alpha, beta, gamma):
-        try:
-            return self._validate_params(delta, alpha, beta, gamma)
-        except ValueError:
-            return False
-
     def _pdf(self, x, delta, alpha, beta, gamma):
         x_arr = np.asarray(x, dtype=float)
         rho_vals, mu_vals, p = self._get_series_terms(delta, alpha, beta, gamma)
@@ -7571,10 +10641,10 @@ def pdf(self, x, delta, alpha, beta, gamma, *args, **kwargs):
         $$
 
         $$
-        \mu_p = 
+        \mu_p =
         \begin{cases}
             \delta p + \beta \tan\left(\frac{\pi \alpha}{2}\right) \left((\gamma p)^\alpha - \gamma p\right), & \alpha \neq 1 \\
-            \delta p - \beta \frac{2}{\pi} \log(\gamma p), & \text{if } \alpha = 1
+            \delta p - \frac{2}{\pi} \beta \gamma p \log(\gamma p), & \text{if } \alpha = 1
         \end{cases}
         $$
 
@@ -7598,6 +10668,77 @@ def pdf(self, x, delta, alpha, beta, gamma, *args, **kwargs):
         """
         return super().pdf(x, delta, alpha, beta, gamma, *args, **kwargs)
 
+    def _logpdf(self, x, delta, alpha, beta, gamma):
+        # log of the Fourier-series density, floored at float-tiny
+        # (methods-parity §6 decision 2): the series is a truncated trig
+        # polynomial whose ringing noise is the density's own accuracy
+        # floor, so values below ~2.2e-308 are not meaningful — the floor
+        # keeps the log finite (≈ −708) instead of nan on a noise-negative
+        # cell. Tails above that floor are exact.
+        pdf_vals = self._pdf(x, delta, alpha, beta, gamma)
+        return np.log(np.clip(pdf_vals, np.finfo(float).tiny, None))
+
+    def logpdf(self, x, delta, alpha, beta, gamma, *args, **kwargs):
+        r"""
+        Logarithm of the probability density function of the Wrapped Stable
+        distribution: the log of the Fourier-series density, floored at the
+        smallest normal double. The series is the density's own accuracy
+        floor (a truncated trigonometric polynomial), so log-density values
+        below ≈ −708 are reported as the floor rather than as noise.
+
+        Parameters
+        ----------
+        x : array_like
+            Points at which to evaluate the log-density.
+        delta : float
+            Location parameter, 0 <= delta <= 2*pi.
+        alpha : float
+            Stability parameter, 0 < alpha <= 2.
+        beta : float
+            Skewness parameter, -1 < beta < 1.
+        gamma : float
+            Scale parameter, gamma > 0.
+
+        Returns
+        -------
+        logpdf_values : array_like
+            Logarithm of the probability density function evaluated at `x`.
+        """
+        return super().logpdf(x, delta, alpha, beta, gamma, *args, **kwargs)
+
+    def trig_moment(self, p: int = 1, *args, **kwargs) -> complex:
+        """Closed-form trigonometric moment (Pewsey 2008 eqs. 3–4, the S0
+        characteristic-function terms the series itself is built from):
+        m_p = ρ_p·e^{iμ_p} with ρ_p = e^{−(γp)^α} and the α = 1 phase
+        carrying the minus sign (validation bug #3). Exact for every p —
+        unlike the density series, no truncation cap is involved."""
+        shape_args, non_shape_kwargs = self._separate_shape_parameters(
+            args, kwargs, "trig_moment"
+        )
+        call_kwargs = self._prepare_call_kwargs(non_shape_kwargs, "trig_moment")
+        delta, alpha, beta, gamma = (
+            float(np.asarray(v, dtype=float))
+            for v in self._parse_args(*shape_args, **call_kwargs)[0]
+        )
+
+        if not np.isscalar(p):
+            raise ValueError("`p` must be an integer scalar.")
+        if int(round(p)) != p:
+            raise ValueError("`p` must be an integer.")
+        k = int(round(p))
+        if k == 0:
+            return complex(1.0, 0.0)
+        ak = float(abs(k))
+        rho_p = np.exp(-((gamma * ak) ** alpha))
+        if abs(alpha - 1.0) <= _WRAPSTABLE_ALPHA_TOL:
+            mu_p = delta * ak - (2.0 / np.pi) * beta * gamma * ak * np.log(gamma * ak)
+        else:
+            mu_p = delta * ak + beta * np.tan(0.5 * np.pi * alpha) * (
+                (gamma * ak) ** alpha - gamma * ak
+            )
+        value = rho_p * np.exp(1j * mu_p)
+        return complex(np.conjugate(value)) if k < 0 else complex(value)
+
     def _cdf(self, x, delta, alpha, beta, gamma):
         x_arr = np.asarray(x, dtype=float)
         scalar_input = x_arr.ndim == 0
@@ -7613,19 +10754,59 @@ def _cdf(self, x, delta, alpha, beta, gamma):
         cdf_vals = (theta_flat[0] / (2.0 * np.pi)) + (1.0 / np.pi) * series_sum
 
         anchor = (1.0 / np.pi) * np.sum((rho_vals / p_vals) * np.sin(-mu_vals))
-        cdf_vals = cdf_vals - anchor
-        cdf_vals = np.where(cdf_vals < 0.0, cdf_vals + 1.0, cdf_vals)
-        cdf_vals = np.clip(cdf_vals, 0.0, 1.0)
-
-        # Ensure exact endpoints
+        # The anchored difference raw(θ) − raw(0) of one increasing function
+        # is ≥ 0 for every θ ∈ [0, 2π]; only float/truncation dust can go
+        # negative, so clip — never wrap by +1 (that would turn −1e-17 into
+        # ≈ 1).
+        cdf_vals = np.clip(cdf_vals - anchor, 0.0, 1.0)
+
+        # Exact-endpoint pins only: an isclose() here (whose default rtol
+        # survives an atol override) would swallow honest tail values up to
+        # ~6e-5 away from 2π.
         two_pi = 2.0 * np.pi
-        cdf_vals[np.isclose(theta_flat[0], 0.0, atol=1e-12)] = 0.0
-        cdf_vals[np.isclose(theta_flat[0], two_pi, atol=1e-12)] = 1.0
+        cdf_vals[theta_flat[0] == 0.0] = 0.0
+        cdf_vals[theta_flat[0] == two_pi] = 1.0
 
         if scalar_input:
             return float(cdf_vals.reshape(-1)[0])
         return cdf_vals.reshape(x_arr.shape)
 
+    def cdf(self, x, delta, alpha, beta, gamma, *args, **kwargs):
+        r"""
+        Cumulative distribution function of the Wrapped Stable distribution.
+
+        The characteristic-function series integrates term by term, giving
+        the analytic form
+
+        $$
+        F(\theta) = \frac{\theta}{2\pi} + \frac{1}{\pi}\sum_{p\ge 1}
+        \frac{\rho_p}{p}\,\Bigl[\sin(p\theta - \mu_p) - \sin(-\mu_p)\Bigr],
+        \qquad \rho_p = e^{-(\gamma p)^\alpha},
+        $$
+
+        truncated once the exact tail bound drops below tolerance — no
+        quadrature is involved.
+
+        Parameters
+        ----------
+        x : array_like
+            Points at which to evaluate the CDF.
+        delta : float
+            Location (mean-direction) parameter, ``0 <= delta <= 2*pi``.
+        alpha : float
+            Stability index, ``0 < alpha <= 2``.
+        beta : float
+            Skewness parameter, ``-1 < beta < 1``.
+        gamma : float
+            Scale parameter, ``gamma > 0``.
+
+        Returns
+        -------
+        cdf_values : array_like
+            CDF evaluated at `x`.
+        """
+        return super().cdf(x, delta, alpha, beta, gamma, *args, **kwargs)
+
     def _ppf(self, q, delta, alpha, beta, gamma):
         q_arr = np.asarray(q, dtype=float)
         flat = q_arr.reshape(-1)
@@ -7643,69 +10824,141 @@ def _ppf(self, q, delta, alpha, beta, gamma):
             shaped = result.reshape(q_arr.shape)
             return float(shaped) if q_arr.ndim == 0 else shaped
 
-        q_valid = flat[valid]
-        close_zero = np.isclose(q_valid, 0.0, atol=1e-12, rtol=0.0)
-        close_one = np.isclose(q_valid, 1.0, atol=1e-12, rtol=0.0)
-
         two_pi = 2.0 * np.pi
+        close_zero = valid & np.isclose(flat, 0.0, atol=1e-12, rtol=0.0)
+        close_one = valid & np.isclose(flat, 1.0, atol=1e-12, rtol=0.0)
+        result[close_zero] = 0.0
+        result[close_one] = two_pi
 
-        theta_vals = np.empty_like(q_valid)
-        for idx, q_val in enumerate(q_valid):
-            if close_zero[idx]:
-                theta_vals[idx] = 0.0
-                continue
-            if close_one[idx]:
-                theta_vals[idx] = two_pi
-                continue
-
-            lo, hi = 0.0, two_pi
-            theta = q_val * two_pi
-
-            for _ in range(_WRAPSTABLE_NEWTON_MAXITER):
-                cdf_theta = float(self._cdf(theta, delta_val, alpha_val, beta_val, gamma_val))
-                pdf_theta = float(self._pdf(theta, delta_val, alpha_val, beta_val, gamma_val))
-                residual = cdf_theta - q_val
-
-                if abs(residual) <= _WRAPSTABLE_NEWTON_TOL and (hi - lo) <= _WRAPSTABLE_NEWTON_WIDTH_TOL:
-                    break
+        interior = valid & ~(close_zero | close_one)
+        if not np.any(interior):
+            shaped = result.reshape(q_arr.shape)
+            return float(shaped) if q_arr.ndim == 0 else shaped
 
-                if residual > 0.0:
-                    hi = min(hi, theta)
-                else:
-                    lo = max(lo, theta)
+        q_sub = flat[interior]
 
-                if pdf_theta <= 0.0 or not np.isfinite(pdf_theta):
-                    theta = 0.5 * (lo + hi)
-                    continue
+        def cdf_fn(t):
+            return np.asarray(
+                self._cdf(t, delta_val, alpha_val, beta_val, gamma_val),
+                dtype=float,
+            )
 
-                step = residual / pdf_theta
-                theta_new = theta - step
-                if not np.isfinite(theta_new) or theta_new <= lo or theta_new >= hi:
-                    theta = 0.5 * (lo + hi)
-                else:
-                    theta = theta_new
+        def pdf_fn(t):
+            return np.asarray(
+                self._pdf(t, delta_val, alpha_val, beta_val, gamma_val),
+                dtype=float,
+            )
 
-                if (hi - lo) <= _WRAPSTABLE_NEWTON_WIDTH_TOL:
+        # Bracket every quantile on a coarse grid of the series cdf, then
+        # polish with one shared bracket-safeguarded Newton, iterating only
+        # the unconverged subset (the former per-quantile scalar loop paid
+        # the full series matrix per q per iteration).
+        grid = np.linspace(0.0, two_pi, 33)
+        f_grid = cdf_fn(grid)
+        idx = np.clip(np.sum(f_grid[:, None] <= q_sub[None, :], axis=0), 1, 32)
+        lower = grid[idx - 1]
+        upper = grid[idx]
+        f_lo = f_grid[idx - 1]
+        f_hi = f_grid[idx]
+        span = np.clip(f_hi - f_lo, 1e-300, None)
+        theta = lower + (upper - lower) * np.clip((q_sub - f_lo) / span, 0.0, 1.0)
+
+        residual = cdf_fn(theta) - q_sub
+        lower = np.where(residual <= 0.0, theta, lower)
+        upper = np.where(residual > 0.0, theta, upper)
+        act = np.flatnonzero(np.abs(residual) > _WRAPSTABLE_NEWTON_TOL)
+        for _ in range(_WRAPSTABLE_NEWTON_MAXITER):
+            if not act.size:
+                break
+            th_a = theta[act]
+            lo_a = lower[act]
+            hi_a = upper[act]
+            p_a = pdf_fn(th_a)
+            step = residual[act] / np.clip(p_a, np.finfo(float).tiny, None)
+            th_n = th_a - step
+            bad = (
+                ~np.isfinite(th_n)
+                | (th_n <= lo_a)
+                | (th_n >= hi_a)
+                | (p_a <= 0.0)
+                | ~np.isfinite(p_a)
+            )
+            th_n = np.where(bad, 0.5 * (lo_a + hi_a), th_n)
+            r_n = cdf_fn(th_n) - q_sub[act]
+            theta[act] = th_n
+            residual[act] = r_n
+            lower[act] = np.where(r_n <= 0.0, th_n, lo_a)
+            upper[act] = np.where(r_n > 0.0, th_n, hi_a)
+            act = act[np.abs(r_n) > _WRAPSTABLE_NEWTON_TOL]
+
+        # Residual-converged cells still owe the width certificate
+        # (|F − q| ≤ tol alone cannot pin θ in a flat stretch). One probe
+        # pass settles all sharp cells with two cdf evals — a ±w bracket is
+        # confirmed wherever the cdf visibly crosses q inside it — instead
+        # of the ~30 blanket bisections the per-quantile loop paid.
+        need = np.flatnonzero(upper - lower > _WRAPSTABLE_NEWTON_WIDTH_TOL)
+        if need.size:
+            w = 0.4 * _WRAPSTABLE_NEWTON_WIDTH_TOL
+            lo_p = np.maximum(theta[need] - w, lower[need])
+            hi_p = np.minimum(theta[need] + w, upper[need])
+            below = cdf_fn(lo_p) - q_sub[need] <= 0.0
+            above = cdf_fn(hi_p) - q_sub[need] > 0.0
+            lower[need] = np.where(below, lo_p, lower[need])
+            upper[need] = np.where(above, hi_p, upper[need])
+
+        # flat-zone stragglers: bisect the bracket to the width tolerance
+        rem = np.flatnonzero(upper - lower > _WRAPSTABLE_NEWTON_WIDTH_TOL)
+        if rem.size:
+            lo_u = lower[rem]
+            hi_u = upper[rem]
+            q_u = q_sub[rem]
+            for _ in range(40):
+                if np.all(hi_u - lo_u <= _WRAPSTABLE_NEWTON_WIDTH_TOL):
                     break
-
-            else:  # pragma: no cover - fallback to bisection if Newton fails
-                for _ in range(30):
-                    theta_mid = 0.5 * (lo + hi)
-                    cdf_mid = float(self._cdf(theta_mid, delta_val, alpha_val, beta_val, gamma_val))
-                    if cdf_mid > q_val:
-                        hi = theta_mid
-                    else:
-                        lo = theta_mid
-                theta = 0.5 * (lo + hi)
-
-            theta_vals[idx] = (theta + two_pi) % two_pi
-
-        result[valid] = theta_vals
+                mid = 0.5 * (lo_u + hi_u)
+                go_up = cdf_fn(mid) <= q_u
+                lo_u = np.where(go_up, mid, lo_u)
+                hi_u = np.where(go_up, hi_u, mid)
+            theta[rem] = 0.5 * (lo_u + hi_u)
+            lower[rem] = lo_u
+            upper[rem] = hi_u
+
+        result[interior] = (theta + two_pi) % two_pi
         shaped = result.reshape(q_arr.shape)
         if q_arr.ndim == 0:
             return float(shaped)
         return shaped
 
+    def ppf(self, q, delta, alpha, beta, gamma, *args, **kwargs):
+        """
+        Percent-point function (inverse CDF) of the Wrapped Stable
+        distribution.
+
+        Quantiles invert the analytic series CDF with a vectorized
+        bracket-safeguarded Newton iteration (series PDF as the slope) and
+        a bracket-width certificate, so ``ppf`` stays in exact sync with
+        ``cdf``.
+
+        Parameters
+        ----------
+        q : array_like
+            Quantiles to evaluate (values in ``[0, 1]``).
+        delta : float
+            Location (mean-direction) parameter, ``0 <= delta <= 2*pi``.
+        alpha : float
+            Stability index, ``0 < alpha <= 2``.
+        beta : float
+            Skewness parameter, ``-1 < beta < 1``.
+        gamma : float
+            Scale parameter, ``gamma > 0``.
+
+        Returns
+        -------
+        ppf_values : array_like
+            Angles in ``[0, 2π)`` such that ``cdf(angle) = q``.
+        """
+        return super().ppf(q, delta, alpha, beta, gamma, *args, **kwargs)
+
     def _rvs(self, delta, alpha, beta, gamma, size=None, random_state=None):
         rng = self._init_rng(random_state)
 
@@ -7716,8 +10969,8 @@ def _rvs(self, delta, alpha, beta, gamma, size=None, random_state=None):
 
         if not (0.0 < alpha_val <= 2.0):
             raise ValueError("`alpha` must lie in (0, 2].")
-        if not (-1.0 < beta_val < 1.0):
-            raise ValueError("`beta` must lie in (-1, 1).")
+        if not (-1.0 <= beta_val <= 1.0):
+            raise ValueError("`beta` must lie in [-1, 1].")
         if not (gamma_val > 0.0):
             raise ValueError("`gamma` must be positive.")
 
@@ -7822,6 +11075,8 @@ def weighted_moment(p):
         phi2 = phi2_raw + 2.0 * np.pi * round((2.0 * phi1 - phi2_raw) / (2.0 * np.pi))
 
         if abs(alpha_mom - 1.0) <= _WRAPSTABLE_ALPHA_TOL:
+            # mu_p = delta*p - beta*B_p with B_p = (2/pi)*gamma*p*log(gamma*p)
+            # (S0, alpha = 1), so phi2 - 2*phi1 = -beta*(B2 - 2*B1).
             B1 = (2.0 / np.pi) * gamma_mom * np.log(gamma_mom)
             B2 = (2.0 / np.pi) * (gamma_mom * 2.0) * np.log(gamma_mom * 2.0)
             denom = B2 - 2.0 * B1
@@ -7829,9 +11084,9 @@ def weighted_moment(p):
                 beta_mom = 0.0
                 delta_mom = phi1
             else:
-                beta_mom = (phi2 - 2.0 * phi1) / denom
+                beta_mom = (2.0 * phi1 - phi2) / denom
                 beta_mom = float(np.clip(beta_mom, beta_bounds[0], beta_bounds[1]))
-                delta_mom = phi1 - beta_mom * B1
+                delta_mom = phi1 + beta_mom * B1
         else:
             A = np.tan(0.5 * np.pi * alpha_mom)
             B1 = (gamma_mom) ** alpha_mom - gamma_mom
@@ -8016,7 +11271,9 @@ def _initial_order(tol):
         rho_vals = np.exp(-((gamma * p) ** alpha))
 
         if abs(alpha - 1.0) <= _WRAPSTABLE_ALPHA_TOL:
-            mu_vals = delta * p + (2.0 / np.pi) * beta * gamma * p * np.log(gamma * p)
+            # S0 convention (Pewsey 2008, eq. 4): the alpha = 1 phase carries a
+            # minus sign — also the alpha -> 1 limit of the branch below.
+            mu_vals = delta * p - (2.0 / np.pi) * beta * gamma * p * np.log(gamma * p)
         else:
             mu_vals = delta * p + beta * np.tan(0.5 * np.pi * alpha) * (
                 (gamma * p) ** alpha - gamma * p
@@ -8054,29 +11311,115 @@ def _wrapstable_sample_linear(alpha, beta, gamma, delta, *, size, rng):
     V = rng.uniform(-0.5 * np.pi, 0.5 * np.pi, size=size)
     W = rng.exponential(1.0, size=size)
 
+    # CMS draws are standard S1 variates; the series/pdf use the S0(delta)
+    # convention (Pewsey 2008, eq. 2), so the location shift below converts
+    # the scaled draw to S0 location ``delta`` (Nolan's delta0/delta1 relation).
     if abs(alpha - 1.0) > _WRAPSTABLE_ALPHA_TOL:
         tan_term = np.tan(0.5 * np.pi * alpha)
         theta0 = np.arctan(beta * tan_term) / alpha
         factor = (1.0 + (beta * tan_term) ** 2) ** (1.0 / (2.0 * alpha))
 
-        delta_s1 = delta - gamma * beta * tan_term
         part1 = np.sin(alpha * (V + theta0)) / (np.cos(V) ** (1.0 / alpha))
         part2 = (np.cos(V - alpha * (V + theta0)) / W) ** ((1.0 - alpha) / alpha)
-        x_s1 = gamma * factor * part1 * part2 + delta_s1
-        x = x_s1 + (delta - delta_s1)
+        # gamma*Z + c is S1 with location c; S0 location delta needs
+        # c = delta - beta*gamma*tan(pi*alpha/2).
+        x = gamma * factor * part1 * part2 + delta - gamma * beta * tan_term
     else:
         factor = 2.0 / np.pi
-        delta_s1 = delta - factor * beta * gamma * np.log(gamma)
         term = (
             (0.5 * np.pi + beta * V) * np.tan(V)
             - beta * np.log((0.5 * np.pi * W * np.cos(V)) / (0.5 * np.pi + beta * V))
         )
-        x_s1 = gamma * factor * term + delta_s1
-        x = x_s1 + (delta - delta_s1)
+        # At alpha = 1 scaling a standard S1 draw already shifts the S1
+        # location by -(2/pi)*beta*gamma*log(gamma), which exactly cancels
+        # the S0<->S1 conversion: gamma*Z + delta has S0 location delta.
+        x = gamma * factor * term + delta
 
     return x
 
-class katojones_gen(CircularContinuous):
+def _kj_cart_scores(x, mu, gamma, a, b, second=True):
+    r"""Derivatives of the Kato–Jones log-density in Cartesian shape
+    coordinates ``(a, b) = (ρ cos λ, ρ sin λ)`` (regression plan §3.4).
+
+    In these coordinates the log-density is elementary:
+
+    $$\ell = \log N - \log 2\pi,\qquad
+      N = 1 + \frac{2\gamma(c - a)}{D},\qquad
+      D = 1 + a^2 + b^2 - 2ac - 2bs,$$
+
+    with ``c = cos(θ−μ)``, ``s = sin(θ−μ)``. All partials follow from the
+    quotient rule on ``F = (c−a)/D`` (``N = 1 + 2γF``); the only nonzero
+    second partials of the numerator/denominator are ``E_{μμ} = −c`` and
+    ``D_{aa} = D_{bb} = 2``, ``D_{μa} = −2s``, ``D_{μb} = 2c``,
+    ``D_{μμ} = 2(bs + ac)``. ``N`` is floored at a tiny positive value for
+    the same reason ``_pdf`` clips: strictly inside the Theorem-1 disc the
+    density is positive, but boundary-hugging parameters can round it to 0.
+
+    Returns ``(l1, l2)`` dicts keyed by ``mu``/``gamma``/``a``/``b`` and
+    their unordered pairs (``l2 = None`` when ``second=False``).
+    """
+    x_b, mu_b, g_b, a_b, b_b = np.broadcast_arrays(
+        *(np.asarray(v, dtype=float) for v in (x, mu, gamma, a, b))
+    )
+    delta = x_b - mu_b
+    c, s = np.cos(delta), np.sin(delta)
+    D = 1.0 + a_b * a_b + b_b * b_b - 2.0 * a_b * c - 2.0 * b_b * s
+    D = np.maximum(D, 1e-300)
+    E = c - a_b
+    F = E / D
+    N = np.maximum(1.0 + 2.0 * g_b * F, 1e-300)
+
+    E1 = {"mu": s, "a": -np.ones_like(s), "b": np.zeros_like(s)}
+    D1 = {
+        "mu": 2.0 * (b_b * c - a_b * s),
+        "a": 2.0 * (a_b - c),
+        "b": 2.0 * (b_b - s),
+    }
+    names = ("mu", "a", "b")
+    F1 = {k: (E1[k] * D - E * D1[k]) / (D * D) for k in names}
+
+    l1 = {
+        "mu": 2.0 * g_b * F1["mu"] / N,
+        "gamma": 2.0 * F / N,
+        "a": 2.0 * g_b * F1["a"] / N,
+        "b": 2.0 * g_b * F1["b"] / N,
+    }
+    if not second:
+        return l1, None
+
+    zero = np.zeros_like(s)
+    E2 = {("mu", "mu"): -c}
+    D2 = {
+        ("mu", "mu"): 2.0 * (b_b * s + a_b * c),
+        ("mu", "a"): -2.0 * s,
+        ("mu", "b"): 2.0 * c,
+        ("a", "a"): 2.0 * np.ones_like(s),
+        ("a", "b"): zero,
+        ("b", "b"): 2.0 * np.ones_like(s),
+    }
+    l2 = {}
+    for i, xn in enumerate(names):
+        for yn in names[i:]:
+            Exy = E2.get((xn, yn), zero)
+            Dxy = D2[(xn, yn)]
+            Fxy = (
+                Exy / D
+                - (E1[xn] * D1[yn] + E1[yn] * D1[xn] + E * Dxy) / (D * D)
+                + 2.0 * E * D1[xn] * D1[yn] / (D * D * D)
+            )
+            l2[(xn, yn)] = (
+                2.0 * g_b * Fxy / N
+                - 4.0 * g_b * g_b * F1[xn] * F1[yn] / (N * N)
+            )
+    l2[("gamma", "gamma")] = -4.0 * F * F / (N * N)
+    for xn in names:
+        l2[("gamma", xn)] = (
+            2.0 * F1[xn] / N - 4.0 * g_b * F * F1[xn] / (N * N)
+        )
+    return l1, l2
+
+
+class katojones_gen(_RegressionReady, CircularContinuous):
     """
     Kato--Jones (2015) Distribution
 
@@ -8086,8 +11429,13 @@ class katojones_gen(CircularContinuous):
     -------
     pdf(x, mu, gamma, rho, lam)
         Probability density function.
+    logpdf(x, mu, gamma, rho, lam)
+        Logarithm of the probability density function.
     cdf(x, mu, gamma, rho, lam)
         Cumulative distribution function via adaptive Fourier series.
+    ppf(q, mu, gamma, rho, lam)
+        Percent-point function (vectorized bracket-safeguarded Newton on
+        the series CDF).
 
     rvs(mu, gamma, rho, lam, size=None, random_state=None)
         Random variates obtained by inverting the CDF.
@@ -8113,6 +11461,182 @@ class katojones_gen(CircularContinuous):
       102(1), 181-190.
     """
 
+    # --- regression overlay (Phase 1 contract; read by the regression engine
+    # only — descriptive use keeps the book parameterization (mu, gamma, rho,
+    # lam)). The regression coordinates are the **disc chart** of plan §3.4:
+    # the Theorem-1 feasible set for the Cartesian shape pair
+    # (a, b) = (ρ cos λ, ρ sin λ) is the closed disc of center (γ, 0) and
+    # radius 1−γ, and the chart
+    #
+    #     (a, b) = (γ, 0) + (1−γ)·u/√(1+‖u‖²),   u ∈ ℝ²,
+    #
+    # maps an unconstrained u to its open interior — every (μ, γ, u₁, u₂)
+    # with γ ∈ (0, 1) is feasible, so univariate links suffice and no
+    # constraint ever reaches the optimizer. u = 0 is exactly the wrapped
+    # Cauchy WC(μ, γ) (the nesting test point). ``dlogpdf``/``d2logpdf``
+    # therefore take (mu, gamma, u1, u2); the ``KatoJonesLL`` family in
+    # ``regression.py`` owns the (γ, u) → (ρ, λ) translation for ``logpdf``
+    # and ``fit``. ---
+    param_roles = {
+        "mu": "location",
+        "gamma": "concentration",
+        "u1": "shape",
+        "u2": "shape",
+    }
+    default_links = {
+        "location": "tanhalf",
+        "concentration": "logit",
+        "shape": "identity",
+    }
+
+    @staticmethod
+    def disc_chart(gamma, u1, u2):
+        """Map unconstrained chart coordinates ``u`` to the Cartesian shape
+        pair ``(a, b)`` strictly inside the Theorem-1 disc (plan §3.4)."""
+        gamma, u1, u2 = (np.asarray(v, dtype=float) for v in (gamma, u1, u2))
+        r = np.sqrt(1.0 + u1 * u1 + u2 * u2)
+        om = 1.0 - gamma
+        return gamma + om * u1 / r, om * u2 / r
+
+    @staticmethod
+    def disc_chart_inverse(gamma, rho, lam, *, vmax=1.0 - 1e-9):
+        """Closed-form chart inverse: book shape parameters ``(ρ, λ)`` →
+        chart coordinates ``(u₁, u₂)``. Boundary or infeasible (ρ, λ) — a
+        moments fit can land exactly on the Theorem-1 circle — are first
+        pulled radially to ``vmax`` times the disc radius so the inverse
+        stays finite."""
+        gamma, rho, lam = (np.asarray(v, dtype=float) for v in (gamma, rho, lam))
+        om = np.maximum(1.0 - gamma, 1e-12)
+        v1 = (rho * np.cos(lam) - gamma) / om
+        v2 = rho * np.sin(lam) / om
+        n = np.hypot(v1, v2)
+        scale = np.where(n > vmax, vmax / np.maximum(n, 1e-300), 1.0)
+        v1, v2 = v1 * scale, v2 * scale
+        den = np.sqrt(np.maximum(1.0 - (v1 * v1 + v2 * v2), 1e-18))
+        return v1 / den, v2 / den
+
+    @staticmethod
+    def _chart_pieces(gamma, u1, u2, second):
+        """Chart value plus the Jacobian/Hessian pieces the chain rule
+        needs: ``(a, b)``, ``∂(a,b)/∂γ``, ``∂(a,b)/∂u_j`` and, for l2, the
+        ``∂²(a,b)`` blocks (the chart is linear in γ, so only the mixed
+        γ–u and u–u second derivatives survive)."""
+        r2 = 1.0 + u1 * u1 + u2 * u2
+        r = np.sqrt(r2)
+        r3 = r2 * r
+        v1, v2 = u1 / r, u2 / r
+        om = 1.0 - gamma
+        a = gamma + om * v1
+        b = om * v2
+        J11 = 1.0 / r - u1 * u1 / r3
+        J12 = -u1 * u2 / r3
+        J22 = 1.0 / r - u2 * u2 / r3
+        first = {
+            "a": a, "b": b,
+            "a_g": 1.0 - v1, "b_g": -v2,
+            "a_u": (om * J11, om * J12),
+            "b_u": (om * J12, om * J22),
+        }
+        if not second:
+            return first, None
+        r5 = r3 * r2
+        H1 = (
+            -3.0 * u1 / r3 + 3.0 * u1**3 / r5,        # ∂²v₁/∂u₁²
+            -u2 / r3 + 3.0 * u1 * u1 * u2 / r5,       # ∂²v₁/∂u₁∂u₂
+            -u1 / r3 + 3.0 * u1 * u2 * u2 / r5,       # ∂²v₁/∂u₂²
+        )
+        H2 = (
+            -u2 / r3 + 3.0 * u1 * u1 * u2 / r5,       # ∂²v₂/∂u₁²
+            -u1 / r3 + 3.0 * u1 * u2 * u2 / r5,       # ∂²v₂/∂u₁∂u₂
+            -3.0 * u2 / r3 + 3.0 * u2**3 / r5,        # ∂²v₂/∂u₂²
+        )
+        return first, {"J": (J11, J12, J22), "H1": H1, "H2": H2}
+
+    def dlogpdf(self, x, mu, gamma, u1, u2):
+        r"""First derivatives of ``logpdf`` w.r.t. the **chart parameters**
+        ``(μ, γ, u₁, u₂)`` (l1) — the Cartesian scores of
+        ``_kj_cart_scores`` pushed through the disc chart; the γ column
+        picks up the chart's own γ-dependence:
+
+        $$\frac{\partial\ell}{\partial\gamma}\Big|_{\text{chart}} =
+          \ell_\gamma + \ell_a(1 - v_1) - \ell_b v_2 .$$
+
+        Vectorizes over per-observation parameter arrays; returns a dict
+        keyed by the declared parameter names.
+        """
+        mu_b, g_b, u1_b, u2_b = np.broadcast_arrays(
+            *(np.asarray(v, dtype=float) for v in (mu, gamma, u1, u2))
+        )
+        ch, _ = self._chart_pieces(g_b, u1_b, u2_b, second=False)
+        l1, _ = _kj_cart_scores(x, mu_b, g_b, ch["a"], ch["b"], second=False)
+        la, lb = l1["a"], l1["b"]
+        return {
+            "mu": l1["mu"],
+            "gamma": l1["gamma"] + la * ch["a_g"] + lb * ch["b_g"],
+            "u1": la * ch["a_u"][0] + lb * ch["b_u"][0],
+            "u2": la * ch["a_u"][1] + lb * ch["b_u"][1],
+        }
+
+    def d2logpdf(self, x, mu, gamma, u1, u2):
+        r"""Second derivatives of ``logpdf`` w.r.t. the chart parameters
+        (l2) — the full second-order chain rule through the disc chart:
+        quadratic forms of the Cartesian Hessian in the chart Jacobian,
+        plus first-order Cartesian scores times the chart's curvature
+        (``∂²(a,b)/∂γ∂u`` and ``∂²(a,b)/∂u∂u``; the chart is linear in γ).
+        """
+        mu_b, g_b, u1_b, u2_b = np.broadcast_arrays(
+            *(np.asarray(v, dtype=float) for v in (mu, gamma, u1, u2))
+        )
+        ch, ch2 = self._chart_pieces(g_b, u1_b, u2_b, second=True)
+        l1, l2 = _kj_cart_scores(x, mu_b, g_b, ch["a"], ch["b"], second=True)
+        la, lb = l1["a"], l1["b"]
+        ag, bg = ch["a_g"], ch["b_g"]
+        au, bu = ch["a_u"], ch["b_u"]
+        J11, J12, J22 = ch2["J"]
+        Jrow = ((J11, J12), (J12, J22))  # J[k][j] = ∂v_k/∂u_j
+        H1, H2 = ch2["H1"], ch2["H2"]
+        Hidx = {(0, 0): 0, (0, 1): 1, (1, 1): 2}
+        om = 1.0 - g_b
+
+        lmm = l2[("mu", "mu")]
+        lma, lmb = l2[("mu", "a")], l2[("mu", "b")]
+        lgg = l2[("gamma", "gamma")]
+        lgm = l2[("gamma", "mu")]
+        lga, lgb = l2[("gamma", "a")], l2[("gamma", "b")]
+        laa, lab, lbb = l2[("a", "a")], l2[("a", "b")], l2[("b", "b")]
+
+        out = {
+            ("mu", "mu"): lmm,
+            ("mu", "gamma"): lgm + lma * ag + lmb * bg,
+            ("mu", "u1"): lma * au[0] + lmb * bu[0],
+            ("mu", "u2"): lma * au[1] + lmb * bu[1],
+            ("gamma", "gamma"): lgg
+            + 2.0 * (lga * ag + lgb * bg)
+            + laa * ag * ag
+            + 2.0 * lab * ag * bg
+            + lbb * bg * bg,
+        }
+        for j, name in enumerate(("u1", "u2")):
+            out[("gamma", name)] = (
+                lga * au[j]
+                + lgb * bu[j]
+                + (laa * ag + lab * bg) * au[j]
+                + (lab * ag + lbb * bg) * bu[j]
+                - la * Jrow[0][j]
+                - lb * Jrow[1][j]
+            )
+        for key, i, j in ((("u1", "u1"), 0, 0), (("u1", "u2"), 0, 1),
+                          (("u2", "u2"), 1, 1)):
+            k = Hidx[(i, j)]
+            out[key] = (
+                laa * au[i] * au[j]
+                + lab * (au[i] * bu[j] + bu[i] * au[j])
+                + lbb * bu[i] * bu[j]
+                + la * om * H1[k]
+                + lb * om * H2[k]
+            )
+        return out
+
     _moment_tolerance = 1e-12
 
     def __init__(self, *args, **kwargs):
@@ -8216,11 +11740,16 @@ def _cdf(self, x, mu, gamma, rho, lam):
         else:
             series = self._get_series_terms(mu_val, gamma_val, rho_val, lam_val)
             cdf_raw = self._evaluate_cdf_series(flat, mu_val, gamma_val, rho_val, lam_val, series=series)
-            cdf_flat = np.mod(cdf_raw, 1.0)
+            # The anchored series G(θ) − G(0) is ≥ 0 and ≤ 1 for every
+            # θ ∈ [0, 2π]; only dust strays outside, so clip — a mod(·, 1)
+            # here would wrap −1e-17 to ≈ 1 (and 1 + 1e-17 to ≈ 0).
+            cdf_flat = cdf_raw
 
+        # exact-endpoint pins only (isclose's default rtol survives an
+        # atol override and would swallow honest tail values near 2π)
         cdf_flat = np.clip(cdf_flat, 0.0, 1.0)
-        cdf_flat[np.isclose(flat, 0.0, atol=1e-12)] = 0.0
-        cdf_flat[np.isclose(flat, 2.0 * np.pi, atol=1e-12)] = 1.0
+        cdf_flat[flat == 0.0] = 0.0
+        cdf_flat[flat == 2.0 * np.pi] = 1.0
 
         if scalar_input:
             return float(cdf_flat[0])
@@ -8315,65 +11844,133 @@ def _ppf(self, q, mu, gamma, rho, lam):
         series = self._get_series_terms(mu_val, gamma_val, rho_val, lam_val)
         two_pi = 2.0 * np.pi
 
-        def cdf_single(theta):
-            value = self._evaluate_cdf_series(theta, mu_val, gamma_val, rho_val, lam_val, series=series)
-            value = np.mod(value, 1.0)
-            return float(np.clip(value, 0.0, 1.0))
-
-        for idx, q_val in enumerate(flat):
-            if not valid[idx]:
-                continue
-            if np.isclose(q_val, 0.0, atol=1e-12):
-                result[idx] = 0.0
-                continue
-            if np.isclose(q_val, 1.0, atol=1e-12):
-                result[idx] = two_pi
-                continue
-
-            lo, hi = 0.0, two_pi
-            theta = q_val * two_pi
-
-            for _ in range(_KJ_NEWTON_MAXITER):
-                cdf_theta = cdf_single(theta)
-                pdf_theta = float(self._pdf(theta, mu_val, gamma_val, rho_val, lam_val))
-                residual = cdf_theta - q_val
+        # rtol=0.0 matters: isclose's default rtol against 1.0 would remap
+        # every q ≥ 1 − 1e-5 to the upper endpoint
+        close_zero = valid & np.isclose(flat, 0.0, atol=1e-12, rtol=0.0)
+        close_one = valid & np.isclose(flat, 1.0, atol=1e-12, rtol=0.0)
+        result[close_zero] = 0.0
+        result[close_one] = two_pi
 
-                if abs(residual) <= _KJ_NEWTON_TOL and (hi - lo) <= _KJ_NEWTON_WIDTH_TOL:
-                    break
+        interior = valid & ~(close_zero | close_one)
+        if not np.any(interior):
+            return float(result[0]) if scalar_input else result.reshape(q_arr.shape)
 
-                if residual > 0.0:
-                    hi = min(hi, theta)
-                else:
-                    lo = max(lo, theta)
+        q_sub = flat[interior]
 
-                if pdf_theta <= 0.0 or not np.isfinite(pdf_theta):
-                    theta = 0.5 * (lo + hi)
-                    continue
+        def cdf_fn(t):
+            value = self._evaluate_cdf_series(
+                t, mu_val, gamma_val, rho_val, lam_val, series=series
+            )
+            return np.clip(np.asarray(value, dtype=float), 0.0, 1.0)
 
-                step = residual / pdf_theta
-                theta_candidate = theta - step
-                if not np.isfinite(theta_candidate) or theta_candidate <= lo or theta_candidate >= hi:
-                    theta = 0.5 * (lo + hi)
-                else:
-                    theta = theta_candidate
+        def pdf_fn(t):
+            return np.asarray(
+                self._pdf(t, mu_val, gamma_val, rho_val, lam_val), dtype=float
+            )
 
-                if (hi - lo) <= _KJ_NEWTON_WIDTH_TOL:
+        # Bracket on a coarse grid of the series cdf, then one shared
+        # bracket-safeguarded Newton over the unconverged subset (the former
+        # per-quantile scalar loop re-evaluated the series per q per step).
+        grid = np.linspace(0.0, two_pi, 33)
+        f_grid = cdf_fn(grid)
+        idx = np.clip(np.sum(f_grid[:, None] <= q_sub[None, :], axis=0), 1, 32)
+        lower = grid[idx - 1]
+        upper = grid[idx]
+        f_lo = f_grid[idx - 1]
+        f_hi = f_grid[idx]
+        span = np.clip(f_hi - f_lo, 1e-300, None)
+        theta = lower + (upper - lower) * np.clip((q_sub - f_lo) / span, 0.0, 1.0)
+
+        residual = cdf_fn(theta) - q_sub
+        lower = np.where(residual <= 0.0, theta, lower)
+        upper = np.where(residual > 0.0, theta, upper)
+        act = np.flatnonzero(np.abs(residual) > _KJ_NEWTON_TOL)
+        for _ in range(_KJ_NEWTON_MAXITER):
+            if not act.size:
+                break
+            th_a = theta[act]
+            lo_a = lower[act]
+            hi_a = upper[act]
+            p_a = pdf_fn(th_a)
+            step = residual[act] / np.clip(p_a, np.finfo(float).tiny, None)
+            th_n = th_a - step
+            bad = (
+                ~np.isfinite(th_n)
+                | (th_n <= lo_a)
+                | (th_n >= hi_a)
+                | (p_a <= 0.0)
+                | ~np.isfinite(p_a)
+            )
+            th_n = np.where(bad, 0.5 * (lo_a + hi_a), th_n)
+            r_n = cdf_fn(th_n) - q_sub[act]
+            theta[act] = th_n
+            residual[act] = r_n
+            lower[act] = np.where(r_n <= 0.0, th_n, lo_a)
+            upper[act] = np.where(r_n > 0.0, th_n, hi_a)
+            act = act[np.abs(r_n) > _KJ_NEWTON_TOL]
+
+        # width certificate: one probe pass settles sharp cells with two
+        # cdf evals; only flat-zone stragglers fall through to bisection
+        need = np.flatnonzero(upper - lower > _KJ_NEWTON_WIDTH_TOL)
+        if need.size:
+            w = 0.4 * _KJ_NEWTON_WIDTH_TOL
+            lo_p = np.maximum(theta[need] - w, lower[need])
+            hi_p = np.minimum(theta[need] + w, upper[need])
+            below = cdf_fn(lo_p) - q_sub[need] <= 0.0
+            above = cdf_fn(hi_p) - q_sub[need] > 0.0
+            lower[need] = np.where(below, lo_p, lower[need])
+            upper[need] = np.where(above, hi_p, upper[need])
+
+        rem = np.flatnonzero(upper - lower > _KJ_NEWTON_WIDTH_TOL)
+        if rem.size:
+            lo_u = lower[rem]
+            hi_u = upper[rem]
+            q_u = q_sub[rem]
+            for _ in range(40):
+                if np.all(hi_u - lo_u <= _KJ_NEWTON_WIDTH_TOL):
                     break
-            else:
-                for _ in range(30):
-                    mid = 0.5 * (lo + hi)
-                    if cdf_single(mid) > q_val:
-                        hi = mid
-                    else:
-                        lo = mid
-                theta = 0.5 * (lo + hi)
+                mid = 0.5 * (lo_u + hi_u)
+                go_up = cdf_fn(mid) <= q_u
+                lo_u = np.where(go_up, mid, lo_u)
+                hi_u = np.where(go_up, hi_u, mid)
+            theta[rem] = 0.5 * (lo_u + hi_u)
 
-            result[idx] = theta % two_pi
+        result[interior] = theta % two_pi
 
         if scalar_input:
             return float(result[0])
         return result.reshape(q_arr.shape)
 
+    def ppf(self, q, mu, gamma, rho, lam, *args, **kwargs):
+        """
+        Percent-point function (inverse CDF) of the Kato--Jones (2015)
+        distribution.
+
+        Quantiles invert the analytic Fourier-series CDF with a vectorized
+        bracket-safeguarded Newton iteration (closed-form PDF as the
+        slope) and a bracket-width certificate, so ``ppf`` stays in exact
+        sync with ``cdf``.
+
+        Parameters
+        ----------
+        q : array_like
+            Quantiles to evaluate (values in ``[0, 1]``).
+        mu : float
+            Mean direction, ``0 <= mu < 2*pi``.
+        gamma : float
+            Mean resultant length, ``0 <= gamma < 1``.
+        rho : float
+            Second-order magnitude, ``0 <= rho < 1``.
+        lam : float
+            Second-order phase, ``0 <= lam < 2*pi``.
+
+        Returns
+        -------
+        ppf_values : array_like
+            Angles in ``[0, 2π)`` such that ``cdf(angle) = q``.
+        """
+        return super().ppf(q, mu, gamma, rho, lam, *args, **kwargs)
+
     def _rvs(self, mu, gamma, rho, lam, size=None, random_state=None):
         rng = self._init_rng(random_state)
 
@@ -8659,7 +12256,10 @@ def _get_series_terms(self, mu, gamma, rho, lam):
         return cache[key]
 
     def _compute_series_terms(self, mu, gamma, rho, lam):
-        if gamma <= _KJ_GAMMA_TOL or rho <= _KJ_GAMMA_TOL:
+        # Only gamma ~ 0 collapses the CDF to the uniform theta/(2*pi); at
+        # rho = 0 (the cardioid case) the series keeps its single p = 1 term
+        # with coefficient gamma, handled by the P = 1 branch below.
+        if gamma <= _KJ_GAMMA_TOL:
             return {
                 "coeffs": np.empty(0, dtype=float),
                 "phases": np.empty(0, dtype=float),
diff --git a/pycircstat2/hypothesis.py b/pycircstat2/hypothesis.py
index a43e714..b7a1e0d 100644
--- a/pycircstat2/hypothesis.py
+++ b/pycircstat2/hypothesis.py
@@ -4,7 +4,7 @@
 from typing import Any, Optional, Sequence, Union
 
 import numpy as np
-import pandas as pd
+import polars as pl
 from scipy.special import comb, i0, iv
 from scipy.stats import chi2, f, norm, rankdata, wilcoxon
 
@@ -501,7 +501,7 @@ class ChangePointTestResult(TestResult):
 @dataclass(frozen=True)
 class HarrisonKanjiTestResult(TestResult):
     p_values: tuple[Optional[float], Optional[float], Optional[float]]
-    anova_table: pd.DataFrame
+    anova_table: pl.DataFrame
 
 
 @dataclass(frozen=True)
@@ -3173,7 +3173,7 @@ def harrison_kanji_test(
     HarrisonKanjiTestResult
         Dataclass containing `p_values` — the (factor A, factor B, interaction)
         p-value triple, where the interaction entry is NaN when ``inter=False`` —
-        and `anova_table`, the assembled ANOVA table as a pandas DataFrame.
+        and `anova_table`, the assembled ANOVA table as a polars DataFrame.
     """
 
     if fn is None:
@@ -3188,30 +3188,32 @@ def harrison_kanji_test(
     p = len(np.unique(idp))
     q = len(np.unique(idq))
 
-    # Data frame for aggregation
-    df = pd.DataFrame({fn[0]: idp, fn[1]: idq, "dependent": alpha})
-    n = len(df)
+    n = len(alpha)
 
     # Total resultant vector length
-    tr = n * circ_r(np.array(df["dependent"].values))
+    tr = n * circ_r(alpha)
     kk = circ_kappa(tr / n)
 
-    # Compute mean resultants per group
-    gr = df.groupby(fn)
-    cn = gr.count()
-    cr = gr.agg(circ_r) * cn
-    cn = cn.unstack(fn[1])
-    cr = cr.unstack(fn[1])
-
-    # Factor A
-    gr = df.groupby(fn[0])
-    pn = gr.count()["dependent"]
-    pr = gr.agg(circ_r)["dependent"] * pn
-
-    # Factor B
-    gr = df.groupby(fn[1])
-    qn = gr.count()["dependent"]
-    qr = gr.agg(circ_r)["dependent"] * qn
+    # Per-cell and per-factor resultants (numpy; replaces the pandas
+    # groupby(...).unstack()). cn/cr are p×q matrices — rows index the sorted
+    # factor-A levels, cols the sorted factor-B levels — matching the old
+    # unstacked layout; pn/pr and qn/qr are the factor-A and factor-B
+    # marginals. circ_r returns the mean resultant length, so multiplying by
+    # the group count gives the resultant length R.
+    a_levels = np.unique(idp)
+    b_levels = np.unique(idq)
+    cn = np.zeros((p, q))
+    cr = np.zeros((p, q))
+    for i, a in enumerate(a_levels):
+        for j, b in enumerate(b_levels):
+            cell = alpha[(idp == a) & (idq == b)]
+            cn[i, j] = cell.size
+            cr[i, j] = circ_r(cell) * cell.size if cell.size else 0.0
+
+    pn = np.array([(idp == a).sum() for a in a_levels], dtype=float)
+    pr = np.array([circ_r(alpha[idp == a]) for a in a_levels]) * pn
+    qn = np.array([(idq == b).sum() for b in b_levels], dtype=float)
+    qr = np.array([circ_r(alpha[idq == b]) for b in b_levels]) * qn
 
     if kk > 2:  # Large kappa approximation
         eff_1 = sum(pr**2 / np.sum(cn, axis=1)) - tr**2 / n
@@ -3224,17 +3226,17 @@ def harrison_kanji_test(
 
         eff_t = n - tr**2 / n
         df_t = n - 1
-        m = np.asarray(cn.values).mean()
+        m = cn.mean()
 
         if inter:
             beta = 1 / (1 - 1 / (5 * kk) - 1 / (10 * (kk**2)))
 
-            eff_r = n - np.asarray((cr**2.0 / cn).values).sum()
+            eff_r = n - (cr**2.0 / cn).sum()
             df_r = p * q * (m - 1)
             ms_r = eff_r / df_r
 
             eff_i = (
-                np.asarray((cr**2.0 / cn).values).sum()
+                (cr**2.0 / cn).sum()
                 - sum(qr**2.0 / qn)
                 - sum(pr**2.0 / pn)
                 + tr**2 / n
@@ -3249,7 +3251,7 @@ def harrison_kanji_test(
             df_r = (p - 1) * (q - 1)
             ms_r = eff_r / df_r
 
-            eff_i, df_i, ms_i, FI, pI = None, None, None, None, np.nan
+            eff_i, df_i, ms_i, FI, pI = np.nan, np.nan, np.nan, np.nan, np.nan
             beta = 1
 
         F1 = beta * ms_1 / ms_r
@@ -3271,7 +3273,7 @@ def harrison_kanji_test(
         p2 = chi2.sf(chi2_val, df=df_2)
 
         chiI = kappa_factor * (
-            np.asarray((cr**2.0 / cn).values).sum()
+            (cr**2.0 / cn).sum()
             - sum(pr**2.0 / pn)
             - sum(qr**2.0 / qn)
             + tr**2 / n
@@ -3283,25 +3285,27 @@ def harrison_kanji_test(
 
     # Construct ANOVA Table
     if kk > 2:
-        table = pd.DataFrame(
+        table = pl.DataFrame(
             {
                 "Source": fn + ["Interaction", "Residual", "Total"],
                 "DoF": [df_1, df_2, df_i, df_r, df_t],
                 "SS": [eff_1, eff_2, eff_i, eff_r, eff_t],
                 "MS": [ms_1, ms_2, ms_i, ms_r, np.nan],
-                "F": [np.squeeze(F1), np.squeeze(F2), FI, np.nan, np.nan],
+                "F": [float(F1), float(F2), FI, np.nan, np.nan],
                 "p": list(pval) + [np.nan, np.nan],
-            }
-        ).set_index("Source")
+            },
+            strict=False,
+        )
     else:
-        table = pd.DataFrame(
+        table = pl.DataFrame(
             {
                 "Source": fn + ["Interaction"],
                 "DoF": [df_1, df_2, df_i],
-                "chi2": [chi1.squeeze(), chi2_val.squeeze(), chiI.squeeze()],
-                "p": pval,
-            }
-        ).set_index("Source")
+                "chi2": [float(chi1), float(chi2_val), float(chiI)],
+                "p": list(pval),
+            },
+            strict=False,
+        )
 
     result = HarrisonKanjiTestResult(p_values=pval, anova_table=table)
 
diff --git a/pycircstat2/regression.py b/pycircstat2/regression.py
index aa466ce..0450b75 100644
--- a/pycircstat2/regression.py
+++ b/pycircstat2/regression.py
@@ -1,18 +1,37 @@
 import re
 import warnings
+from itertools import combinations_with_replacement
 from typing import Iterable, List, Optional, Tuple, Union
 
 import numpy as np
-import pandas as pd
 import polars as pl
-from hea import lm as _hea_lm
-from scipy.linalg import lstsq
-from scipy.special import i0e
+from hea.family import GeneralFamily, gamlss_etamu, gamlss_gH, trind_generator
+from hea.models import gam as _hea_gam, lm as _hea_lm
+from scipy.special import expit, i0e
 from scipy.stats import chi2, norm, t as student_t
 
-from .utils import A1, A1inv, significance_code
+from .distributions import get_link, katojones, vonmises
+from .utils import A1, A1inv, A1prime, significance_code
 
-__all__ = ["CLRegression", "CCRegression", "LCRegression"]
+__all__ = ["CircularLL", "KatoJonesLL", "CLRegression", "CCRegression",
+           "LCRegression"]
+
+
+def _to_polars(data) -> "pl.DataFrame":
+    """Coerce a DataFrame to polars.
+
+    Polars frames pass through; pandas frames are accepted as a soft
+    convenience and converted via ``pl.from_pandas`` (which imports pandas
+    lazily — pandas is never a hard dependency, since a pandas input can only
+    exist if pandas is already installed).
+    """
+    if isinstance(data, pl.DataFrame):
+        return data
+    if type(data).__module__.startswith("pandas"):
+        return pl.from_pandas(data)
+    raise TypeError(
+        f"`data` must be a polars (or pandas) DataFrame; got {type(data).__name__}"
+    )
 
 
 def _safe_solve(matrix: np.ndarray, rhs: np.ndarray) -> np.ndarray:
@@ -29,29 +48,512 @@ def _safe_inverse(matrix: np.ndarray) -> np.ndarray:
         return np.linalg.pinv(matrix)
 
 
+def _ravel(v) -> np.ndarray:
+    """Flatten an ``hea.lm`` output (polars frame or ndarray) to 1-D float."""
+    if isinstance(v, pl.DataFrame):
+        return v.to_numpy().ravel()
+    return np.asarray(v, dtype=float).ravel()
+
+
+def _harmonic_block_order(by_name: dict, feats: list, order: int) -> np.ndarray:
+    """Reorder per-coefficient values keyed by hea coefficient name into the
+    legacy CCRegression design order ``[intercept | cos-block | sin-block]``,
+    each block feature-major then harmonic-minor. ``by_name`` maps a name
+    (``"(Intercept)"`` or ``harmonic(<var>, k=…, period=…)cos{j}``) → value; the
+    ``cos{j}``/``sin{j}`` suffix makes the reorder robust to hea's interleaved
+    column order. (``_LC_HARMONIC_COEF_RE`` is defined in the LC markers below.)
+    """
+    lut: dict = {}
+    intercept = 0.0
+    for name, val in by_name.items():
+        if name == "(Intercept)":
+            intercept = float(val)
+            continue
+        m = _LC_HARMONIC_COEF_RE.match(name)
+        if m:
+            lut[(m.group("var"), m.group("trig"), int(m.group("order")))] = float(val)
+    out = [intercept]
+    out += [lut[(f, "cos", k)] for f in feats for k in range(1, order + 1)]
+    out += [lut[(f, "sin", k)] for f in feats for k in range(1, order + 1)]
+    return np.asarray(out, dtype=float)
+
+
+# --- backend dispatch: parametric (hea.lm) vs smooth (hea.gam) ---------------
+# A smooth term — s()/te()/ti()/t2(), mgcv's smooth constructors — routes the
+# fit to ``hea.models.gam`` (a penalized REML/GCV smooth); a purely parametric
+# RHS (``harmonic()`` / ``cos()+sin()`` / linear) stays on ``hea.models.lm``.
+# GAM is a *basis/backend axis, not a taxonomy axis*: a cyclic smooth is the
+# nonparametric counterpart of a harmonic on the same (response, predictor)
+# category, so it folds into LC/CC rather than a separate class. The ``\b``
+# keeps the trailing ``s(`` of ``cos(``/``sin(`` from matching the ``s`` smooth
+# constructor (no word boundary between ``o`` and ``s``).
+_SMOOTH_RE = re.compile(r"\b(?:s|te|ti|t2)\s*\(")
+_SMOOTH_VAR_RE = re.compile(r"\b(?:s|te|ti|t2)\s*\(\s*([^\W\d_]\w*)")
+
+
+def _has_smooth(formula: str) -> bool:
+    """True if the formula RHS contains a smooth term (→ gam backend)."""
+    rhs = formula.split("~", 1)[1] if "~" in formula else formula
+    return bool(_SMOOTH_RE.search(rhs))
+
+
+def _smooth_vars(rhs: str) -> List[str]:
+    """First-argument variable of each smooth term, in formula order."""
+    return [m.group(1) for m in _SMOOTH_VAR_RE.finditer(rhs)]
+
+
+# Cyclic smooth bases (mgcv): bs='cc' (cyclic cubic) / bs='cp' (cyclic p-spline).
+# Their boundary knots set the *period*; for circular predictors that is 2π, so
+# LC/CC supply it by default. hea stays general — mgcv defaults cyclic knots to
+# the data range, which collapses f(0)=f(period) for angles (the lung-deaths
+# Dec≡Jan bug). This default is pycircstat2's circular knowledge, not hea's.
+_SMOOTH_TERM_RE = re.compile(r"\b(?:s|te|ti|t2)\s*\(([^)]*)\)")
+_CYCLIC_BS_RE = re.compile(r"""bs\s*=\s*['"](?:cc|cp)['"]""")
+_TERM_VAR_RE = re.compile(r"\s*([^\W\d_]\w*)")
+
+
+def _cyclic_smooth_vars(rhs: str) -> List[str]:
+    """Variables of cyclic smooths (``bs='cc'``/``'cp'``) in a formula RHS."""
+    out = []
+    for m in _SMOOTH_TERM_RE.finditer(rhs):
+        body = m.group(1)
+        if _CYCLIC_BS_RE.search(body):
+            vm = _TERM_VAR_RE.match(body)
+            if vm:
+                out.append(vm.group(1))
+    return out
+
+
+def _resolve_cyclic_knots(formula: str, user_knots: Optional[dict]) -> Optional[dict]:
+    """Default each cyclic smooth's boundary knots to the circular period
+    ``[0, 2π]`` so callers need not spell it out; explicit ``user_knots`` win
+    per variable. Returns ``None`` when there is nothing to set.
+    """
+    rhs = formula.split("~", 1)[1] if "~" in formula else formula
+    merged = {v: [0.0, 2 * np.pi] for v in _cyclic_smooth_vars(rhs)}
+    if user_knots:
+        merged.update(user_knots)
+    return merged or None
+
+
+def _gam_predict_ci(gam_fit, newdata: "pl.DataFrame", level: float):
+    """Point prediction + Wald confidence band from an ``hea.gam`` fit.
+
+    Returns ``(yhat, lo, hi)`` on the response scale; ``lo``/``hi`` are ``None``
+    when ``level`` is falsy. The band is ``fit ± z·se.fit`` with the Gaussian
+    quantile — mgcv's default across-the-function smooth confidence interval.
+    """
+    pred = gam_fit.predict(newdata=newdata, se_fit=bool(level))
+    yhat = np.asarray(pred["fit"].to_numpy(), dtype=float)
+    if not level:
+        return yhat, None, None
+    se = np.asarray(pred["se.fit"].to_numpy(), dtype=float)
+    z = float(norm.ppf(0.5 + level / 2.0))
+    return yhat, yhat - z * se, yhat + z * se
+
+
+def _two_panel_axes(plt, figsize, polar: bool, axes):
+    """Create or validate the (overlay, residual) axis pair for a fit plot.
+
+    When the caller supplies no axes, the overlay (left) axis is made polar
+    iff ``polar``; the residual (right) axis is always cartesian. A
+    caller-provided ``axes`` pair is used as-is (the caller owns its
+    projection).
+    """
+    if axes is None:
+        fig = plt.figure(figsize=figsize or (11, 4.5))
+        ax0 = fig.add_subplot(1, 2, 1, projection="polar" if polar else None)
+        ax1 = fig.add_subplot(1, 2, 2)
+        return fig, ax0, ax1
+    axes = list(axes)
+    if len(axes) != 2:
+        raise ValueError("`axes` must be a sequence of length 2.")
+    return axes[0].figure, axes[0], axes[1]
+
+
+# --- the hea bridge: a circular distribution as an mgcv-style general family --
+
+
+class CircularLL(GeneralFamily):
+    """A regression-ready circular distribution as a hea/mgcv **general
+    family** — the Phase-2 bridge of ``dev/plans/circular_gam_integration.md``.
+
+    Mirrors ``hea.family.gaulss``: one linear predictor per modelable
+    distribution parameter, in the order the distribution declares them
+    (von Mises: μ via ``tanhalf``, log κ via ``log``; wrapped Cauchy: μ, logit
+    ρ; projected normal: identity μ₁, μ₂). ``ll()`` fills the packed
+    per-datum derivative arrays ``l1..l4`` from the distribution's contract
+    methods (``dlogpdf``..``d4logpdf`` — derivatives w.r.t. the *distribution
+    parameters*, never pre-chained through links) and delegates the link
+    chain rule and gradient/Hessian assembly to ``gamlss_etamu`` /
+    ``gamlss_gH``. hea never learns "circular"; pycircstat2 never learns
+    penalties or REML.
+
+        gam(["theta ~ s(x, bs='cc')",   # LP1 → μ      (tanhalf)
+             "      ~ s(z)"],           # LP2 → log κ  (log)
+            data, family=CircularLL(vonmises), method="REML")
+
+    Note the mgcv parameterization: every LP's intercept lives *inside* the
+    link — μ = 2·atan(β₀ + …) — unlike the fisher-lee ``CLRegression``'s
+    Fisher–Lee offset ``μ₀ + 2·atan(Xβ)``. The two coincide for
+    intercept-only models; with covariates they are different (both valid)
+    parameterizations, so compare fitted curves, not coefficients.
+    """
+
+    scale_known = True
+    n_theta = 0
+
+    def __init__(self, dist, links=None):
+        roles = getattr(dist, "param_roles", None)
+        if not roles:
+            raise TypeError(
+                f"{getattr(dist, 'name', dist)!r} is not regression-ready: it "
+                "declares no `param_roles` overlay (see the Phase 1 contract)."
+            )
+        self.dist = dist
+        self.params = list(roles)  # book-named, declaration order = LP order
+        self.n_lp = len(self.params)
+        # The base class assumes the LP coordinates *are* the distribution's
+        # own logpdf parameters (its ll/fit seams pass them straight
+        # through). A family that regresses in transformed coordinates —
+        # katojones declares the §3.4 chart pair u1/u2, which logpdf does
+        # not accept — needs its dedicated subclass; fail fast here instead
+        # of deep in scipy's argument parsing on the first ll() call.
+        if type(self) is CircularLL:
+            shapes = getattr(dist, "shapes", None) or ""
+            shape_names = {s.strip() for s in shapes.split(",") if s.strip()}
+            missing = [p for p in self.params if p not in shape_names]
+            if shape_names and missing:
+                hint = _LL_BY_DIST.get(getattr(dist, "name", None))
+                raise TypeError(
+                    f"{getattr(dist, 'name', dist)!r} declares LP "
+                    f"coordinates {missing} that are not parameters of its "
+                    "logpdf — it regresses in transformed coordinates"
+                    + (f"; use {hint.__name__}() instead of CircularLL"
+                       if hint else "")
+                    + "."
+                )
+        if links is None:
+            links = [get_link(dist.link_for(p)) for p in self.params]
+        else:
+            if len(links) != self.n_lp:
+                raise ValueError(
+                    f"expected {self.n_lp} links (one per LP), got {len(links)}"
+                )
+            links = [get_link(lnk) for lnk in links]
+        # Contract depth gates the outer-Newton mode hea may use:
+        # l4 → full Newton (2), l3 → gradient-only outer (1), l2 → EFS (0).
+        self.available_derivs = (
+            2 if hasattr(dist, "d4logpdf")
+            else 1 if hasattr(dist, "d3logpdf")
+            else 0
+        )
+        self.tri = trind_generator(self.n_lp)
+        self.name = f"CircularLL({getattr(dist, 'name', 'dist')})"
+        super().__init__(links)
+
+    def _etas(self, X, coef, jj, offset):
+        etas = []
+        for j in range(self.n_lp):
+            eta = X[:, jj[j]] @ coef[jj[j]]
+            if offset is not None and len(offset) > j and offset[j] is not None:
+                eta = eta + offset[j]
+            etas.append(eta)
+        return etas
+
+    def _param_values(self, etas):
+        """Inverse-link each LP into a book-named parameter dict. A tanhalf
+        LP is a principal-branch angle in (−π, π); wrap it to [0, 2π) so the
+        distribution's own domain checks pass (every tanhalf consumer is
+        2π-periodic in that parameter, so this changes nothing else)."""
+        out = {}
+        for name, link, eta in zip(self.params, self.links, etas):
+            val = link.linkinv(eta)
+            if link.name == "tanhalf":
+                val = np.mod(val, 2.0 * np.pi)
+            out[name] = val
+        return out
+
+    def _loglik_values(self, y, params):
+        """Per-datum log-likelihood at LP-named parameter values. The seam a
+        subclass overrides when its LP coordinates are not the
+        distribution's own (KatoJonesLL: chart → book translation)."""
+        return np.asarray(self.dist.logpdf(y, **params), dtype=float)
+
+    def _null_params(self, y):
+        """Intercept-only parameter estimates in LP coordinates,
+        declaration-ordered — the distribution's own ``fit`` for every
+        family whose LP coordinates are the book parameters."""
+        return np.atleast_1d(self.dist.fit(y)).astype(float)
+
+    def ll(self, y, X, coef, wt=None, *, lpi, offset=None, deriv: int = 0,
+           d1b=None, d2b=None, fh=None, D=None) -> dict:
+        y = np.asarray(y, dtype=float)
+        X = np.asarray(X, dtype=float)
+        coef = np.asarray(coef, dtype=float)
+        jj = [np.asarray(ix, dtype=int) for ix in lpi]
+        etas = self._etas(X, coef, jj, offset)
+        params = self._param_values(etas)
+
+        l0 = self._loglik_values(y, params)
+        ret: dict = {"l": float(np.sum(l0)), "l0": l0}
+        if deriv == 0:
+            return ret
+
+        names = self.params
+        shape = y.shape
+        d1 = self.dist.dlogpdf(y, **params)
+        d2 = self.dist.d2logpdf(y, **params)
+        l1 = np.column_stack([np.broadcast_to(d1[p], shape) for p in names])
+        l2 = np.column_stack(
+            [np.broadcast_to(d2[k], shape)
+             for k in combinations_with_replacement(names, 2)]
+        )
+        ig1 = np.column_stack(
+            [link.mu_eta(eta) for link, eta in zip(self.links, etas)]
+        )
+        g2 = np.column_stack(
+            [link.d2link(params[name])
+             for link, name in zip(self.links, names)]
+        )
+        l3 = l4 = g3 = g4 = None
+        if deriv > 1:
+            d3 = self.dist.d3logpdf(y, **params)
+            l3 = np.column_stack(
+                [np.broadcast_to(d3[k], shape)
+                 for k in combinations_with_replacement(names, 3)]
+            )
+            g3 = np.column_stack(
+                [link.d3link(params[name])
+                 for link, name in zip(self.links, names)]
+            )
+        if deriv > 3:
+            d4 = self.dist.d4logpdf(y, **params)
+            l4 = np.column_stack(
+                [np.broadcast_to(d4[k], shape)
+                 for k in combinations_with_replacement(names, 4)]
+            )
+            g4 = np.column_stack(
+                [link.d4link(params[name])
+                 for link, name in zip(self.links, names)]
+            )
+
+        tri = self.tri
+        de = gamlss_etamu(l1, l2, l3, l4, ig1, g2, g3, g4,
+                          tri["i2"], tri["i3"], tri["i4"], deriv - 1)
+        gh = gamlss_gH(X, jj, de["l1"], de["l2"], tri["i2"],
+                       l3=de["l3"], i3=tri["i3"], l4=de["l4"], i4=tri["i4"],
+                       d1b=d1b, d2b=d2b, deriv=deriv - 1, fh=fh, D=D)
+        ret.update(gh)
+        return ret
+
+    def initialize_coef(self, y, X, lpi, E=None, offset=None,
+                        use_unscaled: bool = False) -> np.ndarray:
+        """Null-model start, mgcv-flavoured but circular-safe: fit the
+        *distribution itself* to ``y`` (its own ``fit`` — analytic or
+        moment-seeded MLE), then least-squares each LP onto the constant
+        target ``link(param̂)`` with the penalty root ``E`` stacked as a
+        regularizer. gaulss regresses transformed *data* per LP and needs
+        pen.reg's edf search; a constant target has no df to overfit, so the
+        plain stacked solve serves both ``use_unscaled`` branches.
+        """
+        y = np.asarray(y, dtype=float)
+        X = np.asarray(X, dtype=float)
+        jj = [np.asarray(ix, dtype=int) for ix in lpi]
+        n, p = X.shape
+        if E is None:
+            E = np.zeros((0, p))
+        param_hat = self._null_params(y)
+        start = np.zeros(p)
+        for j, (link, par0) in enumerate(zip(self.links, param_hat)):
+            # clip guards the tanhalf pole at μ̂ ≡ π (η → ∞)
+            eta0 = float(np.clip(link.link(float(par0)), -1e6, 1e6))
+            target = np.full(n, eta0)
+            if offset is not None and len(offset) > j and offset[j] is not None:
+                target = target - offset[j]
+            cols = jj[j]
+            xa = np.vstack([X[:, cols], E[:, cols]])
+            ta = np.concatenate([target, np.zeros(E.shape[0])])
+            b, *_ = np.linalg.lstsq(xa, ta, rcond=None)
+            b[~np.isfinite(b)] = 0.0
+            start[cols] = b
+        return start
+
+    def _fitted_direction(self, fitted):
+        """Fitted mean direction from the (n, n_lp) parameter matrix —
+        role-aware: a single location parameter *is* the angle; the projected
+        normal's two Cartesian location components combine via atan2."""
+        loc = self.dist.params_by_role().get("location", [])
+        idx = [self.params.index(nm) for nm in loc]
+        if len(idx) == 1:
+            return np.asarray(fitted)[:, idx[0]]
+        if len(idx) == 2:
+            f = np.asarray(fitted)
+            return np.arctan2(f[:, idx[1]], f[:, idx[0]])
+        raise NotImplementedError(
+            f"{self.name}: cannot derive a direction from location "
+            f"parameters {loc!r}"
+        )
+
+    def postproc(self, y, fitted) -> dict:
+        """Null deviance for the summary's "deviance explained": the
+        distribution's own intercept-only fit pushed through the same
+        deviance-residual convention as :meth:`residuals` (twice the
+        log-likelihood gap to the fitted-mode saturated reference)."""
+        y = np.asarray(y, dtype=float)
+        par0 = self._null_params(y)
+        fitted0 = np.broadcast_to(par0, (y.shape[0], self.n_lp))
+        r0 = self.residuals(y, fitted0, type="deviance")
+        return {"null_deviance": float(np.sum(r0 * r0))}
+
+    def residuals(self, y, fitted, type: str = "deviance") -> np.ndarray:
+        """Angular residuals. ``response`` = the wrapped difference
+        ``y − μ̂(x)`` in (−π, π]; ``deviance``/``pearson`` =
+        ``sign·√(2·max(ℓ_mode − ℓ, 0))`` with the saturated value taken at
+        the fitted direction — the density mode for every symmetric family;
+        for the skewed ones (sine-skewed JP, Kato–Jones) the fitted
+        direction is a mode *anchor* rather than the argmax, so the inner
+        clip at 0 keeps the convention well-defined there.
+        ``fitted`` is the (n, n_lp) matrix of inverse-linked parameters."""
+        y = np.asarray(y, dtype=float)
+        fitted = np.asarray(fitted, dtype=float)
+        mu_hat = self._fitted_direction(fitted)
+        rsd = np.angle(np.exp(1j * (y - mu_hat)))
+        if type == "response":
+            return rsd
+        params = {
+            name: (np.mod(fitted[:, j], 2.0 * np.pi)
+                   if self.links[j].name == "tanhalf" else fitted[:, j])
+            for j, name in enumerate(self.params)
+        }
+        l_obs = self._loglik_values(y, params)
+        l_sat = self._loglik_values(np.mod(mu_hat, 2.0 * np.pi), params)
+        return np.sign(rsd) * np.sqrt(2.0 * np.clip(l_sat - l_obs, 0.0, None))
+
+    def __repr__(self):
+        links = ", ".join(repr(lnk.name) for lnk in self.links)
+        return f"{self.name} (links: {links})"
+
+
+class KatoJonesLL(CircularLL):
+    """Kato–Jones (2015) as a general family in **disc-chart coordinates**
+    (μ, γ, u₁, u₂) — the §3.4 design of ``dev/plans/
+    distribution-validation.md``.
+
+    The four LPs are μ (tanhalf), γ (logit) and the unconstrained chart pair
+    u₁, u₂ (identity); the chart ``(a, b) = (γ, 0) + (1−γ)·u/√(1+‖u‖²)``
+    keeps the Cartesian shape pair (a, b) = (ρ cos λ, ρ sin λ) strictly
+    inside the Theorem-1 feasible disc for *every* coefficient vector, so
+    the coupled constraint never reaches hea. ``u ≡ 0`` recovers the wrapped
+    Cauchy WC(μ, γ) exactly — an intercept-only model for both u-LPs is the
+    natural reduced model.
+
+    The distribution's contract derivatives are already chart-coordinate
+    (``katojones.dlogpdf``/``d2logpdf`` chain the Cartesian scores through
+    the chart), so this subclass only owns the two seams where book
+    parameters appear: per-datum log-likelihood values (γ, u → ρ, λ
+    translation) and the null start (moments fit + closed-form chart
+    inverse).
+
+        gam(["theta ~ s(x, bs='cc')",   # LP1 → μ        (tanhalf)
+             "      ~ z",               # LP2 → logit γ  (logit)
+             "      ~ 1",               # LP3 → u₁       (identity)
+             "      ~ 1"],              # LP4 → u₂       (identity)
+            data, family=KatoJonesLL(), method="REML")
+    """
+
+    def __init__(self, links=None):
+        super().__init__(katojones, links)
+
+    @staticmethod
+    def _book_params(params):
+        """Chart-named LP values → the distribution's book parameters."""
+        a, b = katojones.disc_chart(params["gamma"], params["u1"],
+                                    params["u2"])
+        return {
+            "mu": params["mu"],
+            "gamma": params["gamma"],
+            "rho": np.hypot(a, b),
+            "lam": np.mod(np.arctan2(b, a), 2.0 * np.pi),
+        }
+
+    def _loglik_values(self, y, params):
+        return np.asarray(
+            self.dist.logpdf(y, **self._book_params(params)), dtype=float
+        )
+
+    def _null_params(self, y):
+        mu0, g0, rho0, lam0 = self.dist.fit(y, method="moments")
+        u1, u2 = self.dist.disc_chart_inverse(g0, rho0, lam0)
+        return np.array([mu0, g0, float(u1), float(u2)])
+
+
+# Distributions whose regression (LP) coordinates are not their own logpdf
+# parameters, keyed by distribution name → the dedicated family class.
+# Consulted by `_circular_family` (the `CLRegression` auto-route) and by the
+# `CircularLL.__init__` fail-fast guard's error hint.
+_LL_BY_DIST = {"katojones": KatoJonesLL}
+
+
+def _circular_family(dist) -> CircularLL:
+    """Wrap a regression-ready distribution in its general-family class:
+    plain :class:`CircularLL` for every family whose LP coordinates are its
+    own parameters, the dedicated subclass otherwise (``katojones`` →
+    :class:`KatoJonesLL`, which owns the disc-chart translation)."""
+    cls = _LL_BY_DIST.get(getattr(dist, "name", None))
+    return cls() if cls is not None else CircularLL(dist)
+
+
 class CLRegression:
     """
     Circular-Linear Regression.
 
-    Fits a circular response to linear predictors using iterative optimization.
+    Two backends behind **one formula grammar**, selected with ``backend=``:
+
+    - **"gam"** (default): ``hea.gam`` with a :class:`CircularLL` general
+      family — one formula per linear predictor in the mgcv parameterization
+      (intercept *inside* the link). Handles any regression-ready ``family=``,
+      penalized smooths (``s()``/``te()``), REML, and **different covariates
+      per LP**. ``CLRegression(["θ ~ s(x)", "~ s(z)"], data)`` smooths μ(x)
+      and log κ(z) jointly. Use ``family=projectednormal`` when μ(x) must
+      sweep the full circle (the tanhalf link cannot cross ±π).
+    - **"fisher-lee"**: the von Mises MLE — ``μ_i = μ₀ + 2·atan(x_iᵀβ)``
+      (offset *outside* the link; Fisher & Lee 1992 / Fisher 1993 §6.4),
+      ``κ_i = exp(α + x_iᵀγ)``. Fast, dependency-light, R-validated. It ties
+      μ and κ to one shared design, so it expresses mean / kappa / mixed
+      (which LP carries covariates) but **not** different covariates per LP.
+
+    **Formula grammar (both backends):** a string ``"θ ~ rhs"`` is sugar for
+    ``["θ ~ rhs", "~ 1"]`` (a mean model — μ on the covariates, constant κ);
+    a list ``[μ-formula, logκ-formula]`` models each linear predictor.
+
+    Dispatch: a **smooth term forces "gam"**; ``theta=``/``X=`` arrays and the
+    deprecated ``model_type=`` alias imply ``"fisher-lee"``; otherwise the
+    default is ``"gam"``. The two parameterizations differ with covariates
+    (intercept inside vs outside the link) — they coincide for intercept-only
+    models, otherwise compare fitted *curves*, not coefficients.
 
     Parameters
     ----------
-    formula : str, optional
-        A formula string like 'θ ~ x1 + x2 + x3' specifying the model.
-    data : pd.DataFrame, optional
-        A pandas DataFrame containing the response and predictors.
+    formula : str or list of str, optional
+        ``"θ ~ rhs"`` (≡ ``["θ ~ rhs", "~ 1"]``, a mean model) or a
+        ``[μ-formula, logκ-formula]`` list. Smooth terms require
+        ``backend="gam"``.
+    data : polars.DataFrame, optional
+        A polars (or pandas) DataFrame containing the response and predictors.
     theta : np.ndarray, optional
-        A numpy array of circular response values in radians.
+        Circular response in radians (fisher-lee array interface; with ``X``).
     X : np.ndarray, optional
-        A numpy array of predictor values.
+        Predictor array (fisher-lee array interface; with ``theta``).
+    backend : {"gam", "fisher-lee"}, optional
+        Which engine to use. Default ``"gam"`` (``"fisher-lee"`` for the array
+        interface and the legacy ``model_type=`` alias). A smooth term forces
+        ``"gam"``.
     model_type : str, optional
-        Type of model to fit. Must be one of 'mean', 'kappa', or 'mixed'.
-
-        - 'mean': Fit a model for the mean direction.
-        - 'kappa': Fit a model for the concentration parameter.
-        - 'mixed': Fit a mixed circular-linear model.
-
+        **Deprecated** — pass a formula list instead (``["θ ~ X", "~ 1"]`` =
+        mean, ``["θ ~ 1", "~ X"]`` = kappa, ``["θ ~ X", "~ X"]`` = mixed).
+        Still accepted for the array/single-formula forms; implies
+        ``backend="fisher-lee"``. One of 'mean', 'kappa', 'mixed'.
     beta0 : np.ndarray, optional
         Initial values for the beta coefficients.
     alpha0 : float, optional
@@ -96,6 +598,13 @@ class CLRegression:
     predict_kappa(X_new)
         Predict per-observation κ̂(X) for ``model_type`` in
         ``{'kappa', 'mixed'}``.
+    predict_params(data)
+        Per-observation fitted distribution parameters at new data
+        (gam backend).
+    shape_inference(data=None, level=0.95, cov="Vp")
+        Delta-method SEs/CIs for the Kato–Jones shape parameters
+        (γ, a, b, ρ, λ, u) through the disc chart (gam backend,
+        ``family=KatoJonesLL``).
     plot(figsize=None, n_curve=200, axes=None)
         Two-panel diagnostic figure (fit overlay / κ curve / residuals,
         depending on ``model_type`` and dimensionality).
@@ -114,56 +623,429 @@ class CLRegression:
     References
     ----------
     - Fisher, N. I. (1993). Statistical analysis of circular data. Cambridge University Press.
-    - Pewsey, A., Neuhäuser, M., & Ruxton, G. D. (2014) Circular Statistics in R. Oxford University Press.
+    - Pewsey, A., Neuhäuser, M., & Ruxton, G. D. (2013) Circular Statistics in R. Oxford University Press.
     """
 
     def __init__(
         self,
-        formula: Optional[str] = None,
-        data: Optional[pd.DataFrame] = None,
+        formula: Union[str, List[str], None] = None,
+        data: Optional[pl.DataFrame] = None,
         theta: Optional[np.ndarray] = None,
         X: Optional[np.ndarray] = None,
-        model_type: str = "mixed",
+        model_type: Optional[str] = None,
         beta0: Union[np.ndarray, None] = None,
         alpha0: Union[float, None] = None,
         gamma0: Union[np.ndarray, None] = None,
         tol: float = 1e-8,
         max_iter: int = 100,
         verbose: bool = False,
+        *,
+        backend: Optional[str] = None,
+        family=None,
+        knots: Optional[dict] = None,
+        method: str = "REML",
+        **gam_kwargs,
     ):
         self.verbose = verbose
         self.tol = tol
         self.max_iter = max_iter
-        self.model_type = model_type
 
-        # Parse inputs
-        if formula and data is not None:
+        # --- backend resolution (plan §5). One formula grammar, two engines:
+        #   • "fisher-lee" — von Mises MLE with the tan-half link and the
+        #     offset *outside* (μ = μ₀ + 2·atan(Xβ); Fisher & Lee 1992 /
+        #     Fisher 1993 §6.4). Fast, R-validated; ties μ and κ to one shared
+        #     design (mean / kappa / mixed = which LP carries covariates).
+        #   • "gam" — hea general family (CircularLL) in the mgcv
+        #     parameterization (intercept *inside* the link); any
+        #     regression-ready family, smooths, REML, different covariates
+        #     per LP.
+        # Default: gam. A smooth term forces gam. theta=/X= arrays and the
+        # legacy (deprecated) model_type= alias both imply fisher-lee.
+        if isinstance(formula, (list, tuple)):
+            has_smooth = any(_has_smooth(str(f)) for f in formula)
+        elif isinstance(formula, str):
+            has_smooth = _has_smooth(formula)
+        else:
+            has_smooth = False
+        arrays = theta is not None or X is not None
+
+        if model_type is not None:
+            if backend not in (None, "fisher-lee"):
+                raise ValueError(
+                    "model_type= is a fisher-lee alias; drop it to use "
+                    "backend='gam' (model each LP via the formula list)."
+                )
+            backend = "fisher-lee"
+        elif arrays:
+            backend = backend or "fisher-lee"
+            if backend != "fisher-lee":
+                raise ValueError(
+                    "theta=/X= arrays use backend='fisher-lee'; pass a "
+                    "formula + data for backend='gam'."
+                )
+        else:
+            backend = backend or "gam"
+
+        if backend not in ("fisher-lee", "gam"):
+            raise ValueError(f"backend must be 'fisher-lee' or 'gam'; got {backend!r}.")
+        if has_smooth and backend == "fisher-lee":
+            raise ValueError(
+                "smooth terms (s()/te()/…) require backend='gam'; the "
+                "fisher-lee backend has no penalized smoother."
+            )
+        self.backend = backend
+
+        if backend == "gam":
+            if data is None:
+                raise ValueError("The gam backend requires a formula + `data`.")
+            self.model_type = None  # the formula list plays this role
+            self._init_gam(formula, data, family, knots, method, gam_kwargs)
+            return
+
+        if family is not None or knots is not None or gam_kwargs:
+            raise ValueError("family=/knots=/gam options apply only to backend='gam'.")
+        self.gam_fit = None
+        self.family = None
+        self._init_fisher_lee(formula, data, theta, X, model_type, beta0, alpha0, gamma0)
+
+    def _init_fisher_lee(self, formula, data, theta, X, model_type,
+                         beta0, alpha0, gamma0):
+        """Set up and fit the Fisher–Lee von Mises backend from any accepted
+        input shape: ``theta``/``X`` arrays, a ``[μ-formula, logκ-formula]``
+        list, or a single μ-formula (≡ a mean model). The legacy
+        ``model_type`` still selects mean/kappa/mixed for the array and
+        single-formula forms; for a formula list it is read off which LP
+        carries covariates."""
+        # Location link from the contract (vonmises declares tanhalf via its
+        # role overlay); μ_i = μ₀ + linkinv(x_iᵀβ).
+        self._mu_link = get_link(vonmises.link_for("mu"))
+
+        if isinstance(formula, (list, tuple)):
+            theta_arr, X_arr, feature_names, mt = self._parse_lp_formulas(formula, data)
+        elif formula is not None and data is not None:
             theta_arr, X_arr, feature_names = self._parse_formula(formula, data)
+            mt = model_type or "mean"  # a bare μ-formula is a mean model
         elif theta is not None and X is not None:
-            feature_names = None
-            theta_arr, X_arr = theta, X
+            theta_arr, X_arr, feature_names = theta, X, None
+            mt = model_type or "mixed"  # historic array default
         else:
-            raise ValueError("Provide either a formula + data or theta and X.")
+            raise ValueError(
+                "Provide a formula + data, a formula list, or theta and X."
+            )
 
-        self.theta, self.X = self._prepare_design(theta_arr, X_arr)
-        if feature_names is None:
-            self.feature_names = [f"x{i}" for i in range(self.X.shape[1])]
-        else:
-            self.feature_names = feature_names
+        if mt not in ("mean", "kappa", "mixed"):
+            raise ValueError("model_type must be 'mean', 'kappa', or 'mixed'.")
+        self.model_type = mt
 
-        # Validate model type
-        if model_type not in ["mean", "kappa", "mixed"]:
-            raise ValueError("Model type must be 'mean', 'kappa', or 'mixed'.")
+        self.theta, self.X = self._prepare_design(theta_arr, X_arr)
+        self.feature_names = feature_names or [f"x{i}" for i in range(self.X.shape[1])]
 
-        # Initialize parameters
         p = self.X.shape[1]
         self.alpha = float(alpha0) if alpha0 is not None else 0.0
         self.beta = self._coerce_vector(beta0, p, name="beta")
         self.gamma = self._coerce_vector(gamma0, p, name="gamma")
-
-        # Fit the model
         self.result = self._fit()
 
+    def _parse_lp_formulas(self, formulas, data):
+        """Parse a fisher-lee ``[μ-formula, logκ-formula]`` list into
+        ``(theta, X, feature_names, model_type)``. The model type follows
+        which LP carries covariates; since the backend ties both LPs to one
+        shared design, two non-empty LPs must name the same predictors (else
+        use ``backend='gam'``)."""
+        if len(formulas) != 2:
+            raise ValueError(
+                "the fisher-lee backend expects a [μ-formula, logκ-formula] "
+                f"list of length 2 (got {len(formulas)})."
+            )
+        data = _to_polars(data)
+        cols = set(data.columns)
+        mu_f, kappa_f = formulas
+        response = mu_f.split("~", 1)[0].strip()
+        if not response:
+            raise ValueError(f"First formula must name the response: {mu_f!r}")
+        theta = data[response].to_numpy()
+        mu_cov = self._formula_covariates(mu_f, cols)
+        kappa_cov = self._formula_covariates(kappa_f, cols)
+
+        if mu_cov and not kappa_cov:
+            return theta, data[mu_cov].to_numpy(), mu_cov, "mean"
+        if kappa_cov and not mu_cov:
+            return theta, data[kappa_cov].to_numpy(), kappa_cov, "kappa"
+        if mu_cov and kappa_cov:
+            if set(mu_cov) != set(kappa_cov):
+                raise ValueError(
+                    "the fisher-lee backend ties μ and κ to one shared design, "
+                    f"but μ uses {mu_cov} and κ uses {kappa_cov}. Use "
+                    "backend='gam' for different covariates per predictor."
+                )
+            return theta, data[mu_cov].to_numpy(), mu_cov, "mixed"
+        raise ValueError("no predictors in either formula; nothing to regress.")
+
+    @staticmethod
+    def _formula_covariates(formula: str, data_cols) -> List[str]:
+        """Covariate column names on a formula RHS (``~ 1`` / ``~`` ⇒ none)."""
+        rhs = formula.split("~", 1)[1] if "~" in formula else formula
+        out: List[str] = []
+        for term in rhs.split("+"):
+            term = term.strip()
+            if term in ("", "1"):
+                continue
+            if term not in data_cols:
+                raise ValueError(
+                    f"unknown predictor {term!r}; columns are {sorted(data_cols)}."
+                )
+            out.append(term)
+        return out
+
+    def _init_gam(self, formula, data, family, knots, method, gam_kwargs):
+        """Fit the distributional gam backend: one formula per linear
+        predictor, ``hea.gam`` + a :class:`CircularLL` family, REML by
+        default. A single formula gets a constant second LP (``"~ 1"``) —
+        smooth μ(x), constant concentration."""
+        if family is None:
+            family = CircularLL(vonmises)
+        elif not isinstance(family, CircularLL):
+            # a regression-ready distribution: wrap in its family class
+            # (katojones auto-routes to KatoJonesLL via _circular_family)
+            family = _circular_family(family)
+        formulas = list(formula) if isinstance(formula, (list, tuple)) else [formula]
+        if len(formulas) == 1 and family.n_lp == 2:
+            formulas.append("~ 1")
+        if len(formulas) != family.n_lp:
+            raise ValueError(
+                f"{family.name} has {family.n_lp} linear predictors; got "
+                f"{len(formulas)} formulas."
+            )
+        self.formula = formulas
+        self.family = family
+        self.data = _to_polars(data)
+        response = formulas[0].split("~", 1)[0].strip()
+        if not response:
+            raise ValueError(f"First formula must name the response: {formulas[0]!r}")
+        self.feature_names = None
+        self.theta = np.mod(
+            np.asarray(self.data[response].to_numpy(), dtype=float), 2.0 * np.pi
+        )
+
+        # Cyclic smooths (bs='cc'/'cp') on circular predictors default their
+        # boundary knots to the period [0, 2π] — same circular knowledge the
+        # LC/CC backends supply; explicit user knots win per variable.
+        merged: dict = {}
+        for f in formulas:
+            merged.update(_resolve_cyclic_knots(f, None) or {})
+        if knots:
+            merged.update(knots)
+
+        self.gam_fit = _hea_gam(
+            formulas,
+            self.data,
+            family=family,
+            knots=merged or None,
+            method=method,
+            **gam_kwargs,
+        )
+        self.result = self._build_result_gam()
+
+    def _build_result_gam(self) -> dict:
+        """Result dict for the distributional gam backend. Keys shared with
+        the fisher-lee path keep their meaning (``mu``/``kappa`` become
+        per-observation fitted values; ``log_likelihood`` is the penalized
+        fit's log-likelihood); smooth-specific summaries are added."""
+        g = self.gam_fit
+        fam = self.family
+        fv = np.asarray(g.fitted_values, dtype=float)
+        mu = np.mod(fam._fitted_direction(fv), 2.0 * np.pi)
+        conc = fam.dist.params_by_role().get("concentration", [])
+        kappa = fv[:, fam.params.index(conc[0])] if conc else None
+        out = {
+            "backend": "gam",
+            "param_names": list(fam.params),
+            "fitted_params": fv,
+            "mu": mu,
+            "kappa": kappa,
+            "coefficients": dict(zip(g.bhat.columns, g.bhat.row(0))),
+            "log_likelihood": float(g.logLik),
+            "aic": float(g.AIC),
+            "bic": float(g.BIC),
+            "reml": float(g.REML_criterion),
+            "edf_total": float(g.edf_total),
+            "residuals": np.asarray(g.residuals, dtype=float),
+            "n": fv.shape[0],
+        }
+        edf_by = getattr(g, "edf_by_smooth", None)
+        if edf_by is not None:
+            out["edf_by_smooth"] = {k: float(v) for k, v in dict(edf_by).items()}
+        return out
+
+    def predict_params(self, data) -> dict:
+        """Per-observation fitted distribution parameters at new data
+        (gam backend only): a book-named dict, one entry per linear
+        predictor, on the parameter scale (tanhalf angles wrapped to
+        ``[0, 2π)``). ``data`` may be a DataFrame or a design array."""
+        if self.backend != "gam":
+            raise ValueError(
+                "predict_params() is for the distributional gam backend; the "
+                "fisher-lee path exposes predict()/predict_kappa()."
+            )
+        new = self._gam_newdata(data)
+        pred = self.gam_fit.predict(newdata=new)
+        cols = list(pred.columns)
+        out = {}
+        for j, name in enumerate(self.family.params):
+            v = np.asarray(pred[cols[j]].to_numpy(), dtype=float)
+            if self.family.links[j].name == "tanhalf":
+                v = np.mod(v, 2.0 * np.pi)
+            out[name] = v
+        return out
+
+    def shape_inference(self, data=None, *, level=0.95, cov="Vp"):
+        r"""Delta-method SEs and CIs for the Kato–Jones shape parameters
+        (gam backend, ``family=KatoJonesLL`` — plan §3.4's deferred item).
+
+        The fitted shape lives in η-space as ``(logit γ, u₁, u₂)`` with
+        joint coefficient covariance ``V`` from the gam fit; this pushes
+        that uncertainty through the inverse link and the disc chart,
+
+        $$(γ, a, b) = T(η),\qquad Σ_{(γ,a,b)} = J\,Σ_η\,J^{\top},$$
+
+        and once more through ``(ρ, λ) = (\sqrt{a²+b²},\ \mathrm{atan2}(b,a))``,
+        reporting estimates, standard errors and Wald intervals for all
+        five book/shape quantities (plus the chart pair ``u₁, u₂``
+        themselves on their identity scale).
+
+        Parameters
+        ----------
+        data : DataFrame or array, optional
+            Covariate rows at which to evaluate the shape parameters.
+            May be omitted **only when every shape formula (γ, u₁, u₂) is
+            intercept-only** — the fitted shape is then a single constant
+            tuple; with covariates on γ the shape pair varies per row and
+            evaluation rows are required.
+        level : float, optional
+            Two-sided confidence level (default 0.95).
+        cov : {"Vp", "Ve", "Vc"}, optional
+            Which gam coefficient covariance to propagate (Bayesian
+            posterior ``Vp`` — mgcv's default for intervals —, frequentist
+            ``Ve``, or the smoothing-parameter-corrected ``Vc``).
+
+        Returns
+        -------
+        dict
+            ``{name: {"estimate", "se", "lo", "hi"}}`` arrays (one entry
+            per evaluation row) for ``gamma, a, b, rho, lam, u1, u2``,
+            plus ``"level"`` and ``"cov"``. The γ interval is computed on
+            the logit scale and back-transformed (stays inside (0, 1));
+            ``rho``'s Wald interval is clipped to [0, 1); ``lam`` is
+            angular — its interval is ``λ̂ ± z·se`` in radians, and its SE
+            diverges as ρ → 0 where the phase is unidentified.
+
+        Notes
+        -----
+        Wald/delta intervals are first-order: trustworthy when the
+        coefficient posterior is approximately Gaussian on the η-scale,
+        the same caveat as every mgcv-style interval. Near the feasibility
+        boundary (‖u‖ → ∞, the logistic-separation analog) they will
+        understate the asymmetry — judge such fits by likelihood, as the
+        §3.4 smoke notes already advise.
+        """
+        if self.backend != "gam":
+            raise ValueError("shape_inference() is for the gam backend.")
+        if not isinstance(self.family, KatoJonesLL):
+            raise ValueError(
+                "shape_inference() reports the Kato–Jones disc-chart shape "
+                "parameters; fit with family=katojones / KatoJonesLL()."
+            )
+        if not (0.0 < level < 1.0):
+            raise ValueError("`level` must be in (0, 1).")
+        g = self.gam_fit
+        V = np.asarray(getattr(g, cov), dtype=float)
+        lpi = [np.asarray(ix, dtype=int) for ix in g.lpi]
+        beta = np.asarray(g.bhat.row(0), dtype=float)
+
+        if data is None:
+            names = list(g.column_names)
+            for j, lp_name in ((1, "gamma"), (2, "u1"), (3, "u2")):
+                if lpi[j].size != 1 or "Intercept" not in names[lpi[j][0]]:
+                    raise ValueError(
+                        f"the {lp_name} formula has covariates — pass "
+                        "`data` rows at which to evaluate the shape."
+                    )
+            new = self.data[:1]  # carrier row; shape columns are intercepts
+        else:
+            new = self._gam_newdata(data)
+        X = np.asarray(g.predict(newdata=new, type="lpmatrix"), dtype=float)
+        m = X.shape[0]
+
+        # per-row η for the three shape LPs and their joint 3×3 covariance
+        # (cross-LP coefficient covariance included)
+        G = np.zeros((m, 3, beta.size), dtype=float)
+        for k, j in enumerate((1, 2, 3)):
+            G[:, k, lpi[j]] = X[:, lpi[j]]
+        eta = np.einsum("mkp,p->mk", G, beta)
+        sig_eta = np.einsum("mkp,pq,mlq->mkl", G, V, G)
+
+        eta_g, u1, u2 = eta[:, 0], eta[:, 1], eta[:, 2]
+        gamma = expit(eta_g)
+        pieces, _ = katojones._chart_pieces(gamma, u1, u2, second=False)
+        a, b = pieces["a"], pieces["b"]
+        dgam = gamma * (1.0 - gamma)  # dγ/dη_γ
+
+        # J: (γ, a, b) wrt (η_γ, u₁, u₂)
+        J = np.zeros((m, 3, 3), dtype=float)
+        J[:, 0, 0] = dgam
+        J[:, 1, 0] = pieces["a_g"] * dgam
+        J[:, 1, 1], J[:, 1, 2] = pieces["a_u"]
+        J[:, 2, 0] = pieces["b_g"] * dgam
+        J[:, 2, 1], J[:, 2, 2] = pieces["b_u"]
+        sig_gab = np.einsum("mij,mjk,mlk->mil", J, sig_eta, J)
+
+        # (ρ, λ) from the (a, b) block
+        rho = np.hypot(a, b)
+        lam = np.mod(np.arctan2(b, a), 2.0 * np.pi)
+        with np.errstate(divide="ignore", invalid="ignore"):
+            J2 = np.zeros((m, 2, 2), dtype=float)
+            J2[:, 0, 0], J2[:, 0, 1] = a / rho, b / rho
+            J2[:, 1, 0], J2[:, 1, 1] = -b / rho**2, a / rho**2
+            sig_rl = np.einsum(
+                "mij,mjk,mlk->mil", J2, sig_gab[:, 1:, 1:], J2
+            )
+
+        z = float(norm.ppf(0.5 + 0.5 * level))
+
+        def _entry(est, se, lo=None, hi=None):
+            lo = est - z * se if lo is None else lo
+            hi = est + z * se if hi is None else hi
+            return {
+                "estimate": est, "se": se,
+                "lo": lo, "hi": hi,
+            }
+
+        se_eta = np.sqrt(np.maximum(np.diagonal(sig_eta, axis1=1, axis2=2), 0.0))
+        se_gab = np.sqrt(np.maximum(np.diagonal(sig_gab, axis1=1, axis2=2), 0.0))
+        se_rl = np.sqrt(np.maximum(np.diagonal(sig_rl, axis1=1, axis2=2), 0.0))
+
+        out = {
+            # γ interval on the logit scale, back-transformed
+            "gamma": _entry(
+                gamma, se_gab[:, 0],
+                lo=expit(eta_g - z * se_eta[:, 0]),
+                hi=expit(eta_g + z * se_eta[:, 0]),
+            ),
+            "a": _entry(a, se_gab[:, 1]),
+            "b": _entry(b, se_gab[:, 2]),
+            "rho": {
+                "estimate": rho, "se": se_rl[:, 0],
+                "lo": np.clip(rho - z * se_rl[:, 0], 0.0, 1.0),
+                "hi": np.clip(rho + z * se_rl[:, 0], 0.0, 1.0),
+            },
+            "lam": _entry(lam, se_rl[:, 1]),
+            "u1": _entry(u1, se_eta[:, 1]),
+            "u2": _entry(u2, se_eta[:, 2]),
+            "level": level,
+            "cov": cov,
+        }
+        return out
+
     @staticmethod
     def _coerce_vector(vec: Optional[np.ndarray], length: int, name: str) -> np.ndarray:
         if vec is None:
@@ -195,8 +1077,9 @@ def _prepare_design(theta: Iterable[float], X: Iterable[Iterable[float]]) -> Tup
         return theta_arr, X_arr
 
     def _parse_formula(
-        self, formula: str, data: pd.DataFrame
+        self, formula: str, data: pl.DataFrame
     ) -> Tuple[np.ndarray, np.ndarray, List[str]]:
+        data = _to_polars(data)
         parts = formula.split("~")
         if len(parts) != 2:
             raise ValueError(
@@ -204,23 +1087,18 @@ def _parse_formula(
             )
         theta_col, x_cols = parts
         theta_series = data[theta_col.strip()]
-        if theta_series.isnull().any():
+        if theta_series.is_null().any():
             raise ValueError("Response column contains missing values.")
         theta = theta_series.to_numpy()
         x_cols = [col.strip() for col in x_cols.split("+") if col.strip()]
         if not x_cols:
             raise ValueError(f"No predictors found in formula: {formula!r}")
         X_df = data[x_cols]
-        if X_df.isnull().any().any():
+        if X_df.null_count().to_numpy().any():
             raise ValueError("Predictor columns contain missing values.")
         X = X_df.to_numpy()
         return theta, X, x_cols
 
-    @staticmethod
-    def _A1_prime(kappa: np.ndarray) -> np.ndarray:
-        a1 = A1(kappa)
-        return 1 - a1 / kappa - a1**2
-
     @staticmethod
     def _safe_exp_kappa(eta: np.ndarray) -> np.ndarray:
         # Bound the log-concentration to avoid exp overflow during iterations.
@@ -261,18 +1139,21 @@ def _fit(self):
         for iter_count in range(self.max_iter):
             if self.model_type == "mean":
                 # Step 1: Compute mu and kappa
-                raw_deviation = theta - 2 * np.arctan(X @ beta)
+                raw_deviation = theta - self._mu_link.linkinv(X @ beta)
                 S = np.mean(np.sin(raw_deviation))
                 C = np.mean(np.cos(raw_deviation))
                 R = np.hypot(S, C)
                 kappa = float(A1inv(R))
                 mu = np.arctan2(S, C)
 
-                # Step 2: Update beta
-                denom = 1 + (X @ beta) ** 2
-                G = 2 * X / denom[:, None]
+                # Step 2: Update beta. Score from the von Mises regression
+                # contract (vonmises.dlogpdf — the §3.2 lift); the IRLS weight
+                # is the *expected* (Fisher) information −E[∂²_{μμ}ℓ] = κ A1(κ),
+                # distinct from the observed d2logpdf. G = ∂μ/∂β chains the
+                # link's mu_eta through the design.
+                G = self._mu_link.mu_eta(X @ beta)[:, None] * X
                 weight = float(kappa * A1(kappa))
-                u = kappa * np.sin(raw_deviation - mu)
+                u = vonmises.dlogpdf(raw_deviation, mu, kappa)["mu"]
                 XtX = G.T @ G
                 rhs = G.T @ u + weight * XtX @ beta
                 mat = weight * XtX + ridge_X
@@ -291,12 +1172,12 @@ def _fit(self):
                 C = float(np.sum(kappa * np.cos(theta)))
                 mu = np.arctan2(S, C)
 
-                # Step 2: Update gamma
-                a1_kappa = A1(kappa)
-                # Floor A1'(κ) to keep the IRLS step finite when some κ_i are
-                # very large (A1'(κ) → 0 as κ → ∞ ⇒ y_gamma blows up).
-                a1_prime = np.maximum(self._A1_prime(kappa), 1e-12)
-                residuals_gamma = np.cos(theta - mu) - a1_kappa
+                # Step 2: Update gamma. κ-score from the contract
+                # (vonmises.dlogpdf — cos(θ−μ) − A1(κ)); the IRLS weight uses
+                # the expected info A1'(κ). Floor A1'(κ) to keep the step finite
+                # when some κ_i are large (A1'(κ) → 0 as κ → ∞ ⇒ y_gamma blows up).
+                a1_prime = np.maximum(A1prime(kappa), 1e-12)
+                residuals_gamma = vonmises.dlogpdf(theta, mu, kappa)["kappa"]
                 y_gamma = residuals_gamma / (a1_prime * kappa)
                 weights = (kappa**2) * a1_prime
                 XtWX = X1.T @ (weights[:, None] * X1)
@@ -313,26 +1194,29 @@ def _fit(self):
             elif self.model_type == "mixed":
                 # Step 1: Compute mu and kappa
                 kappa = self._safe_exp_kappa(alpha + X @ gamma)
-                raw_deviation = theta - 2 * np.arctan(X @ beta)
+                raw_deviation = theta - self._mu_link.linkinv(X @ beta)
                 S = np.sum(kappa * np.sin(raw_deviation))
                 C = np.sum(kappa * np.cos(raw_deviation))
                 mu = np.arctan2(S, C)
 
+                # Both scores from the contract (vonmises.dlogpdf) at the
+                # de-trended angle; the IRLS weights are the expected (Fisher)
+                # information κ A1(κ) for β and A1'(κ) for γ.
+                score = vonmises.dlogpdf(raw_deviation, mu, kappa)
+
                 # Step 2: Update beta — Fisher scoring step from current β.
-                # Score s(β) = Gᵀ (κ ⊙ sin(rdev − μ)); info I(β) = Gᵀ diag(κ A1(κ)) G.
-                # β_new solves I β_new = I β + s.
-                denom = 1 + (X @ beta) ** 2
-                G = 2 * X / denom[:, None]
+                # Score s(β) = Gᵀ (κ ⊙ sin(rdev − μ)); info I(β) = Gᵀ diag(κ A1(κ)) G,
+                # with G = ∂μ/∂β = mu_eta(Xβ)·X. β_new solves I β_new = I β + s.
+                G = self._mu_link.mu_eta(X @ beta)[:, None] * X
                 weights_beta = kappa * A1(kappa)
                 XtWX_beta = G.T @ (weights_beta[:, None] * G)
-                u_beta = kappa * np.sin(raw_deviation - mu)
+                u_beta = score["mu"]
                 rhs_beta = G.T @ u_beta + XtWX_beta @ beta
                 beta_new = _safe_solve(XtWX_beta + ridge_X, rhs_beta)
 
                 # Step 3: Update gamma
-                a1_kappa = A1(kappa)
-                a1_prime = np.maximum(self._A1_prime(kappa), 1e-12)
-                residuals_gamma = np.cos(raw_deviation - mu) - a1_kappa
+                a1_prime = np.maximum(A1prime(kappa), 1e-12)
+                residuals_gamma = score["kappa"]
                 y_gamma = residuals_gamma / (a1_prime * kappa)
                 weights_gamma = (kappa**2) * a1_prime
                 XtWX = X1.T @ (weights_gamma[:, None] * X1)
@@ -394,8 +1278,7 @@ def _compute_standard_errors(self, result):
 
         if self.model_type == "mean":
             # Mean Direction Model
-            denom = 1 + (X @ beta) ** 2
-            G = 2 * X / denom[:, None]
+            G = self._mu_link.mu_eta(X @ beta)[:, None] * X
             weight = float(kappa * A1(kappa))
             XtAX = weight * (G.T @ G)
             cov_beta = _safe_inverse(XtAX)
@@ -417,7 +1300,7 @@ def _compute_standard_errors(self, result):
         elif self.model_type == "kappa":
             # Concentration Parameter Model
             X1 = np.column_stack((np.ones(n), X))  # Add intercept
-            weights = (kappa**2) * self._A1_prime(kappa)
+            weights = (kappa**2) * A1prime(kappa)
             XtWX = X1.T @ (weights[:, None] * X1)
 
             cov_gamma_alpha = _safe_inverse(XtWX)
@@ -441,8 +1324,7 @@ def _compute_standard_errors(self, result):
 
         elif self.model_type == "mixed":
             # Mixed Model
-            denom = 1 + (X @ beta) ** 2
-            G = 2 * X / denom[:, None]
+            G = self._mu_link.mu_eta(X @ beta)[:, None] * X
             weights_beta = kappa * A1(kappa)
             XtGKGX = G.T @ (weights_beta[:, None] * G)
 
@@ -450,7 +1332,7 @@ def _compute_standard_errors(self, result):
             se_beta = np.sqrt(np.diag(cov_beta))
 
             X1 = np.column_stack((np.ones(n), X))  # Add intercept
-            weights_gamma = (kappa**2) * self._A1_prime(kappa)
+            weights_gamma = (kappa**2) * A1prime(kappa)
             XtWX_gamma = X1.T @ (weights_gamma[:, None] * X1)
 
             cov_gamma_alpha = _safe_inverse(XtWX_gamma)
@@ -483,6 +1365,10 @@ def AIC(self):
         if self.result is None:
             raise ValueError("Model must be fitted before calculating AIC.")
 
+        if self.backend == "gam":
+            # hea's general-family AIC (edf-corrected, mgcv convention).
+            return self.result["aic"]
+
         log_likelihood = self.result["log_likelihood"]
         if self.model_type == "mean":
             n_params = len(self.result["beta"])  # Only beta
@@ -504,6 +1390,9 @@ def BIC(self):
         if self.result is None:
             raise ValueError("Model must be fitted before calculating BIC.")
 
+        if self.backend == "gam":
+            return self.result["bic"]
+
         log_likelihood = self.result["log_likelihood"]
         n = len(self.theta)
         if self.model_type == "mean":
@@ -519,14 +1408,59 @@ def BIC(self):
 
         return -2 * log_likelihood + n_params * np.log(n)
 
+    def _gam_newdata(self, data):
+        """Coerce gam-backend predict input to a DataFrame. A DataFrame passes
+        through; a numpy array maps its columns to the formula predictor
+        variables in order (mirroring the fisher-lee array interface), so
+        ``predict(array)`` works on both backends."""
+        if isinstance(data, pl.DataFrame) or type(data).__module__.startswith("pandas"):
+            return _to_polars(data)
+        arr = np.asarray(data, dtype=float)
+        if arr.ndim == 1:
+            arr = arr[:, None]
+        variables = self._gam_variables()
+        if arr.shape[1] != len(variables):
+            raise ValueError(
+                f"expected {len(variables)} predictor column(s) {variables} for "
+                f"the gam backend; got an array with {arr.shape[1]}."
+            )
+        return pl.DataFrame({v: arr[:, i] for i, v in enumerate(variables)})
+
+    def _fl_design(self, X_new) -> np.ndarray:
+        """Coerce fisher-lee predict input to the design array. An array is
+        used as-is; a DataFrame's ``feature_names`` columns are extracted (in
+        the model's order), so ``predict(df)`` works on both backends."""
+        if isinstance(X_new, pl.DataFrame) or type(X_new).__module__.startswith("pandas"):
+            dfn = _to_polars(X_new)
+            missing = [c for c in self.feature_names if c not in dfn.columns]
+            if missing:
+                raise ValueError(
+                    f"missing predictor column(s) {missing}; frame has {dfn.columns}."
+                )
+            arr = dfn.select(self.feature_names).to_numpy().astype(float)
+        else:
+            arr = np.asarray(X_new, dtype=float)
+            if arr.ndim == 1:
+                arr = arr[:, None]
+        if not np.all(np.isfinite(arr)):
+            raise ValueError("`X_new` contains non-finite values.")
+        if arr.shape[1] != self.X.shape[1]:
+            raise ValueError(
+                f"Expected {self.X.shape[1]} predictors, received {arr.shape[1]}."
+            )
+        return arr
+
     def predict(self, X_new):
         """
-        Predict circular response values for new predictor values.
+        Predict circular response values (fitted mean direction) for new
+        predictor values.
 
         Parameters
         ----------
-        X_new: array-like, shape (n_samples, n_features)
-            New predictor data.
+        X_new: array-like or DataFrame
+            New predictor data. Both backends accept either a design array
+            (columns = predictors in model order) or a DataFrame carrying the
+            predictor columns.
 
         Returns
         -------
@@ -536,16 +1470,12 @@ def predict(self, X_new):
         if self.result is None:
             raise ValueError("Model must be fitted before making predictions.")
 
-        X_arr = np.asarray(X_new, dtype=float)
-        if X_arr.ndim == 1:
-            X_arr = X_arr[:, None]
-        if not np.all(np.isfinite(X_arr)):
-            raise ValueError("`X_new` contains non-finite values.")
-        if X_arr.shape[1] != self.X.shape[1]:
-            raise ValueError(
-                f"Expected {self.X.shape[1]} predictors, received {X_arr.shape[1]}."
-            )
+        if self.backend == "gam":
+            params = self.predict_params(X_new)
+            fv = np.column_stack([params[nm] for nm in self.family.params])
+            return np.mod(self.family._fitted_direction(fv), 2.0 * np.pi)
 
+        X_arr = self._fl_design(X_new)
         mu = self.result["mu"]
         if self.model_type == "kappa":
             # Conditional mean is constant μ (β is not part of the model).
@@ -554,7 +1484,7 @@ def predict(self, X_new):
         beta = self.result.get("beta")
         if beta is None or np.any(~np.isfinite(beta)):
             raise ValueError("Model does not contain beta coefficients for prediction.")
-        return np.mod(mu + 2 * np.arctan(X_arr @ beta), 2 * np.pi)
+        return np.mod(mu + self._mu_link.linkinv(X_arr @ beta), 2 * np.pi)
 
     def predict_kappa(self, X_new) -> np.ndarray:
         """Predict per-observation concentration κ_i = exp(α + X_iᵀγ).
@@ -572,21 +1502,21 @@ def predict_kappa(self, X_new) -> np.ndarray:
         -------
         np.ndarray, shape (n_samples,)
         """
+        if self.backend == "gam":
+            conc = self.family.dist.params_by_role().get("concentration", [])
+            if not conc:
+                raise ValueError(
+                    f"{self.family.name} has no 'concentration' parameter; "
+                    "use predict_params() for the fitted parameters."
+                )
+            return self.predict_params(X_new)[conc[0]]
+
         if self.model_type == "mean":
             raise ValueError(
                 "predict_kappa() is for model_type in {'kappa', 'mixed'}; "
                 "the 'mean' model has a scalar κ in result['kappa']."
             )
-        X_arr = np.asarray(X_new, dtype=float)
-        if X_arr.ndim == 1:
-            X_arr = X_arr[:, None]
-        if X_arr.shape[1] != self.X.shape[1]:
-            raise ValueError(
-                f"Expected {self.X.shape[1]} predictors, received {X_arr.shape[1]}."
-            )
-        if not np.all(np.isfinite(X_arr)):
-            raise ValueError("`X_new` contains non-finite values.")
-        return self._predict_kappa(X_arr)
+        return self._predict_kappa(self._fl_design(X_new))
 
     def _predict_kappa(self, X_arr: np.ndarray) -> np.ndarray:
         """Internal: numpy-only κ̂(X) without input validation."""
@@ -599,17 +1529,33 @@ def plot(
         self,
         figsize: Optional[Tuple[float, float]] = None,
         n_curve: int = 200,
+        ci: bool = True,
+        pi: bool = True,
+        level: float = 0.95,
         axes=None,
     ):
-        """Two-panel diagnostic figure.
-
-        Layout depends on ``model_type`` and the number of predictors:
-
-        - 1D X, ``model_type`` in ``{"mean", "mixed"}``: fit overlay
-          (data and curve replicated at θ and θ+2π) and residuals vs X.
-        - 1D X, ``model_type`` == ``"kappa"``: data scatter with the
-          constant μ line, plus fitted κ_i = exp(α + X_iᵀγ) on the right.
-        - Multi-D X: residuals vs fitted angle, plus residual histogram.
+        """Two-panel diagnostic figure, laid out the same way for both
+        backends (single shared predictor):
+
+        - **Left — fit overlay:** data (replicated at θ and θ+2π) and the
+          fitted μ̂(x) curve, with two optional bands — a ``level`` CI band
+          ``μ̂ ± z·se`` (``ci``) and a ±1 circular-SD *dispersion* band from
+          the fitted concentration (``pi``, ``circ-SD = √(−2 ln R̂(x))`` with
+          ``R̂ = A1(κ̂)`` for von Mises, ``ρ̂`` for wrapped Cauchy). For the
+          fisher-lee ``"kappa"`` model the curve is the constant μ line.
+        - **Right — fitted concentration vs residuals:** the κ̂(x) curve when
+          concentration depends on x (fisher-lee kappa/mixed; a gam κ LP with
+          covariates), else residuals vs x (constant-κ models). The projected
+          normal shows ‖μ̂(x)‖ as its concentration proxy.
+
+        Multi-predictor fits drop the overlay for a residuals-vs-fitted /
+        residual-histogram pair.
+
+        The CI band is exact (joint covariance) for the gam backend and a
+        μ₀ ⟂ β delta-method approximation for fisher-lee; the gam CI band is
+        available only when the direction is a single fitted parameter (von
+        Mises, wrapped Cauchy — not the projected normal's atan2 of two LPs).
+        ``ci``/``pi``/``level`` apply to both backends.
 
         Returns
         -------
@@ -617,6 +1563,9 @@ def plot(
         """
         import matplotlib.pyplot as plt
 
+        if self.backend == "gam":
+            return self._plot_gam(plt, figsize, n_curve, ci, pi, level, axes)
+
         n_features = self.X.shape[1]
         is_1d = n_features == 1
 
@@ -644,14 +1593,34 @@ def plot(
         if self.model_type in ("mean", "mixed"):
             mu = self.result["mu"]
             beta = self.result["beta"]
-            curve = np.mod(mu + 2 * np.arctan(x_grid * beta[0]), 2 * np.pi)
-            curve_plot = curve.astype(float).copy()
+            eta = x_grid * beta[0]
+            curve = np.mod(mu + self._mu_link.linkinv(eta), 2 * np.pi)
             jumps = np.where(np.abs(np.diff(curve)) > np.pi)[0]
+            # CI band on μ̂(X): delta method through the link, treating μ₀ and β
+            # as independent (the fit estimates them in separate steps and stores
+            # no cross-covariance — an approximation, unlike the gam backend's
+            # full-covariance se). ∂μ̂/∂β = mu_eta(βX)·X. Dispersion band from κ̂.
+            se_dir = None
+            if self.result.get("se_beta") is not None and self.result.get("se_mu") is not None:
+                g = self._mu_link.mu_eta(eta) * x_grid
+                se_dir = np.sqrt(self.result["se_mu"] ** 2 + (g * self.result["se_beta"][0]) ** 2)
+            kappa = self.result["kappa"] if self.model_type == "mean" else self._predict_kappa(x_grid_2d)
+            circ_sd = np.sqrt(-2.0 * np.log(np.clip(A1(np.asarray(kappa, dtype=float)), 1e-12, 1.0)))
+            self._fill_mu_bands(ax, x_grid, curve, jumps, ci=ci, pi=pi, level=level,
+                                se_dir=se_dir, circ_sd=circ_sd)
+            curve_plot = curve.astype(float).copy()
             curve_plot[jumps] = np.nan
             ax.plot(x_grid, curve_plot, color="C1", lw=2, label="fit")
             ax.plot(x_grid, curve_plot + 2 * np.pi, color="C1", lw=2)
         else:  # kappa-only: conditional mean is the constant μ.
             mu = self.result["mu"]
+            curve = np.full_like(x_grid, np.mod(mu, 2 * np.pi))
+            se_dir = (np.full_like(x_grid, self.result["se_mu"])
+                      if self.result.get("se_mu") is not None else None)
+            kappa = self._predict_kappa(x_grid_2d)
+            circ_sd = np.sqrt(-2.0 * np.log(np.clip(A1(kappa), 1e-12, 1.0)))
+            self._fill_mu_bands(ax, x_grid, curve, np.array([], dtype=int),
+                                ci=ci, pi=pi, level=level, se_dir=se_dir, circ_sd=circ_sd)
             ax.axhline(mu, color="C1", lw=2, label=f"μ = {mu:.3f}")
             ax.axhline(mu + 2 * np.pi, color="C1", lw=2)
 
@@ -666,7 +1635,10 @@ def plot(
         ax.legend(loc="best", frameon=False)
 
         ax = axes[1]
-        if self.model_type == "kappa":
+        # Right panel = fitted concentration when κ depends on X (kappa AND
+        # mixed both model κ(X) = exp(α + Xγ)); residuals when κ is constant
+        # (mean). Same rule the gam overlay uses, so all models read alike.
+        if self.model_type in ("kappa", "mixed"):
             kappa_curve = self._predict_kappa(x_grid_2d)
             ax.plot(x_grid, kappa_curve, color="C1", lw=2)
             ax.set_ylabel("κ̂(X) = exp(α + Xγ)")
@@ -683,12 +1655,167 @@ def plot(
         return fig
 
     def _fitted_mean(self) -> np.ndarray:
-        """Conditional mean angle at the training X (constant μ for kappa-only)."""
+        """Conditional mean angle at the training data (constant μ for
+        kappa-only; per-observation fitted direction for the gam backend)."""
+        if self.backend == "gam":
+            return self.result["mu"]
         mu = self.result["mu"]
         if self.model_type == "kappa":
             return np.full(self.theta.shape, mu)
         beta = self.result["beta"]
-        return mu + 2 * np.arctan(self.X @ beta)
+        return mu + self._mu_link.linkinv(self.X @ beta)
+
+    def _gam_variables(self) -> List[str]:
+        """Data columns referenced by the gam formulas' RHSs, formula order."""
+        cols = set(self.data.columns)
+        seen: List[str] = []
+        for f in self.formula:
+            rhs = f.split("~", 1)[1] if "~" in f else f
+            for m in re.finditer(r"[A-Za-z_]\w*", rhs):
+                v = m.group(0)
+                if v in cols and v not in seen:
+                    seen.append(v)
+        return seen
+
+    def _fill_mu_bands(self, ax, x_grid, curve, jumps, *, ci, pi, level,
+                       se_dir=None, circ_sd=None) -> None:
+        """Draw ±band(s) around a (possibly wrapping) μ̂(x) curve on the
+        ``[0, 4π]`` overlay, shared by the fisher-lee and gam backends: a ±1
+        circular-SD *dispersion* band (``pi``, wide, from ``circ_sd``) and/or
+        a ``level`` *confidence* band (``ci``, narrow, from ``se_dir``), each
+        replicated at +2π and broken at the same wrap points as the curve.
+        A band whose width array is ``None`` is skipped."""
+        def _fill(delta, color, alpha, label):
+            lo = (curve - delta).astype(float)
+            hi = (curve + delta).astype(float)
+            lo[jumps] = np.nan
+            hi[jumps] = np.nan
+            ax.fill_between(x_grid, lo, hi, color=color, alpha=alpha, lw=0, label=label)
+            ax.fill_between(x_grid, lo + 2 * np.pi, hi + 2 * np.pi, color=color, alpha=alpha, lw=0)
+
+        if pi and circ_sd is not None:
+            _fill(np.broadcast_to(circ_sd, x_grid.shape), "C1", 0.12, "±1 circ-SD")
+        if ci and se_dir is not None:
+            z = float(norm.ppf(0.5 + level / 2.0))
+            _fill(z * np.broadcast_to(se_dir, x_grid.shape), "C3", 0.25,
+                  f"{int(round(level * 100))}% CI")
+
+    def _gam_direction_se(self, grid_df) -> Optional[np.ndarray]:
+        """Delta-method se of the fitted mean direction on the grid, in
+        radians — hea's ``se.fit`` for the location LP. Returns ``None`` when
+        the direction is not a single fitted parameter (e.g. projected
+        normal, where it is ``atan2`` of two LPs and would need a joint delta
+        method)."""
+        loc = self.family.dist.params_by_role().get("location", [])
+        if len(loc) != 1:
+            return None
+        li = self.family.params.index(loc[0])
+        pred = self.gam_fit.predict(newdata=grid_df, se_fit=True)
+        cols = pred.columns  # [fit_0..fit_{k-1}, se_0..se_{k-1}]
+        return np.asarray(
+            pred[cols[self.family.n_lp + li]].to_numpy(), dtype=float
+        )
+
+    def _gam_resultant_length(self, params) -> Optional[np.ndarray]:
+        """Mean resultant length R̂(x) from the fitted concentration, for the
+        ±1 circular-SD dispersion band. ``A1(κ̂)`` for a κ-concentration
+        (von Mises), ``ρ̂`` itself for a ρ-concentration (wrapped Cauchy);
+        ``None`` when there is no concentration role mapping known."""
+        conc = self.family.dist.params_by_role().get("concentration", [])
+        if not conc:
+            return None
+        name = conc[0]
+        if name == "kappa":
+            return A1(np.asarray(params[name], dtype=float))
+        if name == "rho":
+            return np.asarray(params[name], dtype=float)
+        return None
+
+    def _plot_gam(self, plt, figsize, n_curve, ci, pi, level, axes):
+        """Two-panel figure for the distributional gam backend.
+
+        One shared predictor: fit overlay (data + μ̂(x) curve, replicated at
+        +2π) with an optional confidence band (``ci``) and ±1 circular-SD
+        dispersion band (``pi``), and the fitted concentration curve — κ̂(x)
+        for a concentration family, ‖μ̂(x)‖ for the projected normal.
+        Multiple predictors: the residual diagnostic pair instead.
+        """
+        if axes is None:
+            fig, axes = plt.subplots(1, 2, figsize=figsize or (11, 5))
+        else:
+            axes = list(axes)
+            if len(axes) != 2:
+                raise ValueError("`axes` must be a sequence of length 2.")
+            fig = axes[0].figure
+
+        variables = self._gam_variables()
+        if len(variables) != 1:
+            self._plot_residual_diagnostic(axes)
+            fig.tight_layout()
+            return fig
+
+        var = variables[0]
+        x_data = np.asarray(self.data[var].to_numpy(), dtype=float)
+        theta_data = np.mod(self.theta, 2 * np.pi)
+        x_grid = np.linspace(x_data.min(), x_data.max(), n_curve)
+        grid_df = pl.DataFrame({var: x_grid})
+        params = self.predict_params(grid_df)
+        fv = np.column_stack([params[nm] for nm in self.family.params])
+        curve = np.mod(self.family._fitted_direction(fv), 2 * np.pi)
+        jumps = np.where(np.abs(np.diff(curve)) > np.pi)[0]
+
+        ax = axes[0]
+        # Bands first (under the curve and data). Dispersion (wide) below CI.
+        R = self._gam_resultant_length(params) if pi else None
+        circ_sd = None if R is None else np.sqrt(-2.0 * np.log(np.clip(R, 1e-12, 1.0)))
+        se_dir = self._gam_direction_se(grid_df) if ci else None
+        self._fill_mu_bands(ax, x_grid, curve, jumps, ci=ci, pi=pi, level=level,
+                            se_dir=se_dir, circ_sd=circ_sd)
+
+        curve_plot = curve.astype(float).copy()
+        curve_plot[jumps] = np.nan
+        ax.plot(x_grid, curve_plot, color="C1", lw=2, label="fit")
+        ax.plot(x_grid, curve_plot + 2 * np.pi, color="C1", lw=2)
+        ax.scatter(x_data, theta_data, color="C0", s=20, alpha=0.6, edgecolors="none", label="data")
+        ax.scatter(x_data, theta_data + 2 * np.pi, color="C0", s=20, alpha=0.6, edgecolors="none")
+        ax.set_ylim(0, 4 * np.pi)
+        ax.set_yticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi])
+        ax.set_yticklabels(["0", "π", "2π", "3π", "4π"])
+        ax.set_xlabel(var)
+        ax.set_ylabel("θ")
+        ax.set_title(f"Fit overlay ({self.family.name})")
+        ax.legend(loc="best", frameon=False)
+
+        # Right panel = the fitted concentration when it depends on the
+        # predictor; residuals when it is constant (an intercept-only κ LP).
+        # κ̂(X) for a concentration family, ‖μ̂(X)‖ for the projected normal.
+        # Same rule as the fisher-lee overlay, so all CL plots read alike.
+        ax = axes[1]
+        roles = self.family.dist.params_by_role()
+        conc = roles.get("concentration", [])
+        loc = roles.get("location", [])
+        if conc:
+            proxy, label, title = params[conc[0]], f"{conc[0]}̂({var})", "Fitted concentration"
+        elif len(loc) == 2:
+            proxy = np.hypot(params[loc[0]], params[loc[1]])
+            label, title = f"‖μ̂‖({var})", "Fitted resultant length (concentration proxy)"
+        else:
+            proxy = None
+        varies = proxy is not None and np.ptp(proxy) > 1e-8 * max(1.0, float(np.mean(np.abs(proxy))))
+        if varies:
+            ax.plot(x_grid, proxy, color="C1", lw=2)
+            ax.set_ylabel(label)
+            ax.set_title(title)
+        else:
+            residuals = np.angle(np.exp(1j * (self.theta - self._fitted_mean())))
+            ax.scatter(x_data, residuals, color="C0", s=20, alpha=0.6, edgecolors="none")
+            ax.axhline(0.0, color="k", lw=0.5)
+            ax.set_ylabel("Residual (rad)")
+            ax.set_title("Residuals vs " + var)
+        ax.set_xlabel(var)
+
+        fig.tight_layout()
+        return fig
 
     def _plot_residual_diagnostic(self, axes) -> None:
         residuals = np.angle(np.exp(1j * (self.theta - self._fitted_mean())))
@@ -717,6 +1844,26 @@ def summary(self):
         if self.result is None:
             raise ValueError("Model must be fitted before summarizing.")
 
+        if self.backend == "gam":
+            # hea.gam.summary() prints the mgcv-style block (parametric
+            # coefficients + approximate smooth significance); prepend the
+            # LP ↔ parameter mapping and append the fitted-parameter ranges.
+            print("\nCircular-Linear Regression — distributional gam backend")
+            print(f"Family: {self.family!r}")
+            for f, nm in zip(self.formula, self.family.params):
+                print(f"  LP[{nm}]: {f.strip()}")
+            print()
+            self.gam_fit.summary()
+            fv = self.result["fitted_params"]
+            print("\nFitted parameters (per observation):")
+            for j, nm in enumerate(self.family.params):
+                col = fv[:, j]
+                print(
+                    f"  {nm:<6s} min {np.min(col): .4f}   "
+                    f"median {np.median(col): .4f}   max {np.max(col): .4f}"
+                )
+            return
+
         # Title based on model type
         if self.model_type == "mean":
             print("\nCircular Regression for the Mean Direction\n")
@@ -821,7 +1968,10 @@ class CCRegression:
     """
     Circular-Circular Regression.
 
-    Fits a circular response to circular predictors using a specified order of harmonics.
+    Fits a circular response to circular predictors. A harmonic formula
+    (``"theta ~ psi"``, ``order=K``) uses two ``hea.lm`` fits; a **smooth**
+    formula (``"theta ~ s(psi, bs='cc')"``) dispatches to two ``hea.gam`` fits
+    on the cos/sin embedding — the nonparametric counterpart.
 
     Parameters
     ----------
@@ -831,8 +1981,16 @@ class CCRegression:
         A numpy array of circular predictor values in radians.
     order : int, optional
         Order of harmonics to include in the model (default is 1).
+        Parametric backend only.
     level : float, optional
         Significance level for testing higher-order terms (default is 0.05).
+    knots : dict, optional
+        Smooth-backend only. Per-variable boundary knots for ``hea.gam``;
+        cyclic smooths (``bs='cc'``/``'cp'``) default to the period
+        ``[0, 2π]`` (override via this argument).
+    method : str, optional
+        Smooth-backend only. Smoothing-parameter selection for ``hea.gam``
+        (default ``"REML"``).
 
     Attributes
     ----------
@@ -860,9 +2018,10 @@ class CCRegression:
         of higher-order terms.
     predict(x)
         Predict the circular response at new ``x``.
-    plot(figsize=None, n_curve=200, axes=None)
-        Two-panel diagnostic figure (fit overlay for 1-D ``x``; residuals
-        vs fitted + histogram for multi-D).
+    plot(figsize=None, n_curve=200, axes=None, band=True, polar=False)
+        Two-panel diagnostic figure (fit overlay ± circular-SD band, or a
+        polar fit clock, for 1-D ``x``; residuals vs fitted + histogram
+        for multi-D).
 
     Notes
     -----
@@ -872,25 +2031,69 @@ class CCRegression:
     References
     ----------
     - Jammalamadaka, S. R., & Sengupta, A. (2001) Topics in Circular Statistics. World Scientific.
-    - Pewsey, A., Neuhäuser, M., & Ruxton, G. D. (2014) Circular Statistics in R. Oxford University Press.
+    - Pewsey, A., Neuhäuser, M., & Ruxton, G. D. (2013) Circular Statistics in R. Oxford University Press.
     """
 
     def __init__(
         self,
         formula: Optional[str] = None,
-        data: Optional[pd.DataFrame] = None,
+        data: Optional[pl.DataFrame] = None,
         theta: Optional[np.ndarray] = None,
         x: Optional[np.ndarray] = None,
         order: int = 1,
         level: float = 0.05,
+        *,
+        knots: Optional[dict] = None,
+        method: str = "REML",
+        **gam_kwargs,
     ):
-        if formula and data is not None:
+        self.formula = formula
+        # Backend dispatch: a smooth term (s()/te()/…) in the formula → two
+        # cyclic-smooth gam fits on the cos/sin embedding; otherwise the
+        # parametric harmonic path (two hea.lm fits). Same embedding + circular
+        # reassembly either way — gam is a basis swap, not a new model.
+        self.backend = "gam" if (formula and _has_smooth(formula)) else "lm"
+        self._knots = knots
+        self._method = method
+        self._gam_kwargs = gam_kwargs
+
+        if self.backend == "gam":
+            if data is None:
+                raise ValueError(
+                    "The smooth (gam) backend requires a formula and data."
+                )
+            self.data = _to_polars(data)
+            self._gam_response = formula.split("~", 1)[0].strip()
+            self._gam_rhs = formula.split("~", 1)[1].strip()
+            self.feature_names = _smooth_vars(self._gam_rhs)
+            if not self.feature_names:
+                raise ValueError(
+                    f"No smooth predictor found in formula: {formula!r}"
+                )
+            # Default cyclic-smooth knots to the circular period [0, 2π].
+            self._knots = _resolve_cyclic_knots(self._gam_rhs, knots)
+            theta_arr = self.data[self._gam_response].to_numpy()
+            x_arr = self.data[self.feature_names].to_numpy()
+            self.theta = self._validate_input(theta_arr)
+            self.x = self._validate_input(x_arr)
+            if self.x.ndim == 1:
+                self.x = self.x[:, None]
+        elif formula and data is not None:
+            if knots is not None or gam_kwargs:
+                raise ValueError(
+                    "knots=/gam options only apply to smooth formulas "
+                    "(s()/te()/…); this harmonic formula uses hea.lm."
+                )
             theta_arr, x_arr, self.feature_names = self._parse_formula(formula, data)
             self.theta = self._validate_input(theta_arr)
             self.x = self._validate_input(x_arr)
             if self.x.ndim == 1:
                 self.x = self.x[:, None]
         elif theta is not None and x is not None:
+            if knots is not None or gam_kwargs:
+                raise ValueError(
+                    "knots=/gam options require a formula with a smooth term."
+                )
             self.theta = self._validate_input(theta)
             self.x = self._validate_input(x)
             if self.x.ndim == 1:
@@ -914,12 +2117,16 @@ def __init__(
         if not (0 < self.level < 1):
             raise ValueError("`level` must lie between 0 and 1.")
 
-        n_params = 1 + 2 * self.x.shape[1] * self.order
-        if self.theta.size <= n_params:
-            raise ValueError(
-                f"order={self.order} requires more than {n_params} observations "
-                f"(got {self.theta.size}); reduce `order` or provide more data."
-            )
+        # The harmonic order / observation-count check is specific to the
+        # parametric backend; the smooth backend's complexity is set by edf,
+        # not by `order`.
+        if self.backend == "lm":
+            n_params = 1 + 2 * self.x.shape[1] * self.order
+            if self.theta.size <= n_params:
+                raise ValueError(
+                    f"order={self.order} requires more than {n_params} observations "
+                    f"(got {self.theta.size}); reduce `order` or provide more data."
+                )
 
         # Fit the model
         self.result = self._fit()
@@ -948,8 +2155,9 @@ def _validate_input(arr: np.ndarray) -> np.ndarray:
         return np.mod(arr_np, 2 * np.pi)
 
     def _parse_formula(
-        self, formula: str, data: pd.DataFrame
+        self, formula: str, data: pl.DataFrame
     ) -> Tuple[np.ndarray, np.ndarray, List[str]]:
+        data = _to_polars(data)
         parts = formula.split("~")
         if len(parts) != 2:
             raise ValueError(
@@ -963,63 +2171,72 @@ def _parse_formula(
         X = data[x_cols].to_numpy()
         return theta, X, x_cols
 
-    def _design_matrix(self, x: np.ndarray) -> np.ndarray:
-        """Harmonic design matrix [1 | cos(kx_j) | sin(kx_j)] for given x."""
-        if x.ndim == 1:
-            x = x[:, None]
-        n, n_features = x.shape
-        cos_terms, sin_terms = [], []
-        for j in range(n_features):
-            for k in range(1, self.order + 1):
-                cos_terms.append(np.cos(k * x[:, j]))
-                sin_terms.append(np.sin(k * x[:, j]))
-        return np.column_stack([np.ones(n)] + cos_terms + sin_terms)
-
     def _fit(self):
+        if self.backend == "gam":
+            return self._fit_gam()
         n = self.x.shape[0]
         order = self.order
         n_features = self.x.shape[1]
+        feats = list(self.feature_names)
 
-        # Track which (feature, harmonic) each design column corresponds to.
-        cos_labels: List[Tuple[int, int]] = []
-        sin_labels: List[Tuple[int, int]] = []
-        for j in range(n_features):
-            for k in range(1, order + 1):
-                cos_labels.append((j, k))
-                sin_labels.append((j, k))
+        # (feature, harmonic) labels for each cos/sin block column.
+        cos_labels: List[Tuple[int, int]] = [
+            (j, k) for j in range(n_features) for k in range(1, order + 1)
+        ]
+        sin_labels = list(cos_labels)
 
         Y_cos = np.cos(self.theta)
         Y_sin = np.sin(self.theta)
 
-        X = self._design_matrix(self.x)
-        beta_cos, _, _, _ = lstsq(X, Y_cos)
-        beta_sin, _, _, _ = lstsq(X, Y_sin)
+        # Two OLS fits via hea.lm on a harmonic design (period=2π — x is angular):
+        # the cos/sin embedding of the circular response. β/SE are reordered from
+        # hea's interleaved columns into the legacy [intercept | cos-block |
+        # sin-block] order via the cos{j}/sin{j} suffixes. The circular reassembly
+        # (μ̂, ρ, residual κ̂, higher-order test) stays here in pycircstat2.
+        df_fit = pl.DataFrame(
+            {**{f: self.x[:, i] for i, f in enumerate(feats)},
+             "cos_t": Y_cos, "sin_t": Y_sin}
+        )
+        rhs = " + ".join(
+            f"harmonic({f}, k={order}, period={2 * np.pi})" for f in feats
+        )
+        self._lm_cos = _hea_lm(f"cos_t ~ {rhs}", df_fit)
+        self._lm_sin = _hea_lm(f"sin_t ~ {rhs}", df_fit)
+        self._feature_cols = feats
 
-        # Fitted values
-        cos_fit = X @ beta_cos
-        sin_fit = X @ beta_sin
+        beta_cos = _harmonic_block_order(
+            dict(zip(self._lm_cos.bhat.columns, self._lm_cos.bhat.row(0))), feats, order
+        )
+        beta_sin = _harmonic_block_order(
+            dict(zip(self._lm_sin.bhat.columns, self._lm_sin.bhat.row(0))), feats, order
+        )
+
+        cos_fit = _ravel(self._lm_cos.yhat)
+        sin_fit = _ravel(self._lm_sin.yhat)
         fitted = np.mod(np.arctan2(sin_fit, cos_fit), 2 * np.pi)
 
         # Residuals (angular for diagnostics + raw OLS residuals on cos/sin)
         residuals = np.angle(np.exp(1j * (self.theta - fitted)))
-        residual_cos = Y_cos - cos_fit
-        residual_sin = Y_sin - sin_fit
+        residual_cos = _ravel(self._lm_cos.residuals)
+        residual_sin = _ravel(self._lm_sin.residuals)
 
         # Circular correlation coefficient
         rho = float(np.clip(np.sqrt(np.mean(cos_fit**2 + sin_fit**2)), 0.0, 1.0))
 
-        # Per-coefficient OLS SEs for the cos/sin sub-models. Used by summary()
-        # to print a CL/LC-style coefficient table; not part of the R parity
-        # surface (lm.circular.cc returns only the coefficient matrix).
-        XtX_inv = _safe_inverse(X.T @ X)
-        diag_inv = np.maximum(np.diag(XtX_inv), 0.0)
-        df_resid = max(n - X.shape[1], 1)
-        sigma2_cos = float(residual_cos @ residual_cos) / df_resid
-        sigma2_sin = float(residual_sin @ residual_sin) / df_resid
-        se_beta_cos = np.sqrt(sigma2_cos * diag_inv)
-        se_beta_sin = np.sqrt(sigma2_sin * diag_inv)
-
-        # Test higher-order terms
+        # Per-coefficient SEs straight from each lm's covariance (V_bhat =
+        # σ̂²(XᵀX)⁻¹), reordered to the same block order as the coefficients.
+        def _se_by_name(lm_fit):
+            cov = np.asarray(lm_fit.V_bhat, dtype=float)
+            return {nm: float(np.sqrt(max(cov[i, i], 0.0)))
+                    for i, nm in enumerate(lm_fit.column_names)}
+
+        se_beta_cos = _harmonic_block_order(_se_by_name(self._lm_cos), feats, order)
+        se_beta_sin = _harmonic_block_order(_se_by_name(self._lm_sin), feats, order)
+        df_resid = max(n - (1 + 2 * n_features * order), 1)
+
+        # Higher-order test (Jammalamadaka & Sengupta 2001): do the order+1
+        # harmonics add signal? Uses the fitted design's hat matrix (projection
+        # is column-order-invariant, so hea's interleaved X is fine) + residuals.
         higher_order_cos = []
         higher_order_sin = []
         for j in range(n_features):
@@ -1031,12 +2248,10 @@ def _fit(self):
         else:
             W = np.empty((n, 0))
 
-        # Projection matrix for the current model
         if W.size:
-            M = X @ XtX_inv @ X.T
-            H = W.T @ (np.eye(n) - M) @ W
-            H_inv = _safe_inverse(H)
-            N = W @ H_inv @ W.T
+            X = np.asarray(self._lm_cos.X.to_numpy(), dtype=float)
+            M = X @ _safe_inverse(X.T @ X) @ X.T
+            N = W @ _safe_inverse(W.T @ (np.eye(n) - M) @ W) @ W.T
 
             denom_cos = float(residual_cos @ residual_cos)
             denom_sin = float(residual_sin @ residual_sin)
@@ -1102,6 +2317,76 @@ def _fit(self):
             "message": message,
         }
 
+    def _fit_gam(self) -> dict:
+        """Smooth (gam) backend: two cyclic-smooth Gaussian gam fits on the
+        cos/sin embedding of the circular response, reassembled into
+        ``μ̂ = arctan2(sin_fit, cos_fit)`` with a residual concentration κ̂.
+
+        Identical embedding to the parametric path, with penalized smooths
+        instead of harmonics; ``knots``/``method``/options pass straight to
+        ``hea.gam``. The higher-order harmonic χ² test does not apply (smooth
+        complexity is selected by REML/GCV), so ``p_values`` are NaN.
+        """
+        Y_cos = np.cos(self.theta)
+        Y_sin = np.sin(self.theta)
+        df_fit = self.data.with_columns(
+            pl.Series("cos_t", Y_cos), pl.Series("sin_t", Y_sin)
+        )
+        self._gam_cos = _hea_gam(
+            f"cos_t ~ {self._gam_rhs}", df_fit,
+            knots=self._knots, method=self._method, **self._gam_kwargs,
+        )
+        self._gam_sin = _hea_gam(
+            f"sin_t ~ {self._gam_rhs}", df_fit,
+            knots=self._knots, method=self._method, **self._gam_kwargs,
+        )
+
+        cos_fit = np.asarray(self._gam_cos.fitted_values, dtype=float)
+        sin_fit = np.asarray(self._gam_sin.fitted_values, dtype=float)
+        fitted = np.mod(np.arctan2(sin_fit, cos_fit), 2 * np.pi)
+        residuals = np.angle(np.exp(1j * (self.theta - fitted)))
+
+        rho = float(np.clip(np.sqrt(np.mean(cos_fit**2 + sin_fit**2)), 0.0, 1.0))
+
+        A_k = float(np.mean(np.cos(residuals)))
+        if A_k < 0:
+            warnings.warn(
+                f"Mean residual cosine A_k={A_k:.4f} is negative — residuals "
+                "are systematically anti-aligned with the fitted direction. "
+                "κ has been clamped to 0; check for sign errors or model "
+                "misspecification.",
+                UserWarning,
+                stacklevel=3,
+            )
+        kappa_residual = float(A1inv(A_k))
+
+        return {
+            "rho": rho,
+            "fitted": fitted,
+            "residuals": residuals,
+            "coefficients": None,
+            "se_coefficients": None,
+            "edf_total": {
+                "cos": float(self._gam_cos.edf_total),
+                "sin": float(self._gam_sin.edf_total),
+            },
+            "edf_by_smooth": {
+                "cos": {k: float(v) for k, v in dict(self._gam_cos.edf_by_smooth).items()},
+                "sin": {k: float(v) for k, v in dict(self._gam_sin.edf_by_smooth).items()},
+            },
+            "deviance_explained": {
+                "cos": float(self._gam_cos.deviance_explained),
+                "sin": float(self._gam_sin.deviance_explained),
+            },
+            "p_values": np.array([np.nan, np.nan], dtype=float),
+            "A_k": A_k,
+            "kappa": kappa_residual,
+            "message": (
+                "Smooth (gam) backend: the higher-order harmonic test does not "
+                "apply; smoothness is selected by REML/GCV per coordinate."
+            ),
+        }
+
     def predict(self, x: np.ndarray) -> np.ndarray:
         """Predict the circular response at new predictor values.
 
@@ -1125,16 +2410,45 @@ def predict(self, x: np.ndarray) -> np.ndarray:
                 f"{x_arr.shape[1]}."
             )
         x_arr = np.mod(x_arr, 2 * np.pi)
-        design = self._design_matrix(x_arr)
-        cos_pred = design @ self.result["coefficients"]["cos"]
-        sin_pred = design @ self.result["coefficients"]["sin"]
+        cos_pred, sin_pred = self._predict_embedding(x_arr)
         return np.mod(np.arctan2(sin_pred, cos_pred), 2 * np.pi)
 
+    def _predict_embedding(
+        self, x_arr: np.ndarray
+    ) -> Tuple[np.ndarray, np.ndarray]:
+        """Predicted cos/sin embedding components at predictor rows.
+
+        The reassembled direction is ``arctan2(sin, cos)``; the vector
+        length ``hypot(cos, sin)`` estimates the conditional resultant
+        ``ρ̂(x)`` — the model's conditional concentration (Jammalamadaka &
+        Sengupta 2001), which ``arctan2`` discards.
+        """
+        if self.backend == "gam":
+            newdata = pl.DataFrame(
+                {f: x_arr[:, i] for i, f in enumerate(self.feature_names)}
+            )
+            cos_pred = self._gam_cos.predict(newdata=newdata)["fit"].to_numpy()
+            sin_pred = self._gam_sin.predict(newdata=newdata)["fit"].to_numpy()
+            return (
+                np.asarray(cos_pred, dtype=float),
+                np.asarray(sin_pred, dtype=float),
+            )
+        newdata = pl.DataFrame(
+            {f: x_arr[:, i] for i, f in enumerate(self._feature_cols)}
+        )
+        return (
+            _ravel(self._lm_cos.predict(newdata=newdata)),
+            _ravel(self._lm_sin.predict(newdata=newdata)),
+        )
+
     def plot(
         self,
         figsize: Optional[Tuple[float, float]] = None,
         n_curve: int = 200,
         axes=None,
+        *,
+        band: bool = True,
+        polar: bool = False,
     ):
         """Two-panel diagnostic figure.
 
@@ -1144,9 +2458,38 @@ def plot(
         relationship; the right panel shows the wrapped residuals against
         the predictor.
 
+        The fitted direction ``μ̂(x) = arctan2(ŝ, ĉ)`` discards the length
+        of the fitted (cos, sin) vector, so by default the overlay also
+        shades ``μ̂ ± 1`` circular standard deviation ``√(−2 ln ρ̂(x))``
+        implied by the conditional resultant ``ρ̂(x) = ‖(ĉ, ŝ)‖``. This is
+        a model-implied *spread* band, not a confidence band for ``μ̂``: it
+        widens (clipped at π) where ``ρ̂ → 0`` and the direction becomes
+        ill-defined, and it is what visually separates the lm and gam
+        backends — their ``μ̂`` curves often almost coincide while their
+        shrunken amplitudes differ.
+
         For multiple circular predictors, the left panel shows residuals
         vs the fitted angle and the right panel a residual histogram.
 
+        Parameters
+        ----------
+        figsize : tuple, optional
+            Matplotlib figure size; defaults to ``(11, 5)``.
+        n_curve : int
+            Number of grid points used to draw the fitted curve.
+        axes : sequence of matplotlib Axes, optional
+            Two pre-existing axes to draw into (the caller owns their
+            projections). If omitted, a fresh figure is created.
+        band : bool
+            Shade the ``μ̂ ± 1`` circular-SD band on the cartesian overlay
+            (single predictor only). Default True.
+        polar : bool
+            Single predictor only: replace the cartesian overlay with a
+            compass clock — each rim position is a predictor angle ``x``
+            with an arrow pointing in the fitted direction ``μ̂(x)`` and
+            length ``ρ̂(x)``, so a vanishing arrow marks an ill-defined
+            direction. Data are fixed-length arrows just outside the rim.
+
         Returns
         -------
         matplotlib.figure.Figure
@@ -1154,6 +2497,8 @@ def plot(
         import matplotlib.pyplot as plt
 
         n_features = self.x.shape[1]
+        if polar and n_features != 1:
+            raise ValueError("polar=True requires a single circular predictor.")
 
         if axes is None:
             fig, axes = plt.subplots(1, 2, figsize=figsize or (11, 5))
@@ -1167,29 +2512,60 @@ def plot(
             x_data = self.x[:, 0]
             theta_data = self.theta
             residuals = self.result["residuals"]
-            x_grid = np.linspace(0.0, 2 * np.pi, n_curve)
-            theta_pred = self.predict(x_grid)
-            # Break the curve where it wraps so plot() doesn't draw a
-            # vertical jump connecting 2π to 0.
-            theta_plot = theta_pred.astype(float).copy()
-            jumps = np.where(np.abs(np.diff(theta_pred)) > np.pi)[0]
-            theta_plot[jumps] = np.nan
 
-            ax = axes[0]
-            ax.plot(x_grid, theta_plot, color="C1", lw=2, label="fit")
-            ax.plot(x_grid, theta_plot + 2 * np.pi, color="C1", lw=2)
-            ax.scatter(x_data, theta_data, color="C0", s=20, alpha=0.6, edgecolors="none", label="data")
-            ax.scatter(x_data, theta_data + 2 * np.pi, color="C0", s=20, alpha=0.6, edgecolors="none")
-            ax.set_xlim(0, 2 * np.pi)
-            ax.set_ylim(0, 4 * np.pi)
-            ax.set_xticks([0, np.pi / 2, np.pi, 3 * np.pi / 2, 2 * np.pi])
-            ax.set_xticklabels(["0", "π/2", "π", "3π/2", "2π"])
-            ax.set_yticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi])
-            ax.set_yticklabels(["0", "π", "2π", "3π", "4π"])
-            ax.set_xlabel(self.feature_names[0])
-            ax.set_ylabel("θ")
-            ax.set_title("Fit overlay")
-            ax.legend(loc="best", frameon=False)
+            if polar:
+                self._draw_polar_clock(axes[0], x_data, theta_data)
+            else:
+                x_grid = np.linspace(0.0, 2 * np.pi, n_curve)
+                cos_fit, sin_fit = self._predict_embedding(x_grid[:, None])
+                # Unwrap the fitted direction into a continuous curve (the
+                # fit is smooth on the circle; only the flat chart has a
+                # 0/2π seam), then draw it at every 2π offset that crosses
+                # the doubled [0, 4π] window — branches run off the panel
+                # edges instead of breaking mid-panel.
+                theta_curve = np.unwrap(np.arctan2(sin_fit, cos_fit))
+                # Circular SD implied by the conditional resultant; π
+                # ("any direction") once ρ̂ falls below exp(−π²/2).
+                rho_grid = np.clip(np.hypot(cos_fit, sin_fit), 1e-9, 1.0)
+                sd_grid = np.minimum(np.sqrt(-2.0 * np.log(rho_grid)), np.pi)
+
+                ax = axes[0]
+                two_pi = 2 * np.pi
+                lo = float(np.min(theta_curve - (sd_grid if band else 0.0)))
+                hi = float(np.max(theta_curve + (sd_grid if band else 0.0)))
+                k_min = int(np.ceil((0.0 - hi) / two_pi))
+                k_max = int(np.floor((4 * np.pi - lo) / two_pi))
+                for k in range(k_min, k_max + 1):
+                    offset = k * two_pi
+                    if band:
+                        ax.fill_between(
+                            x_grid,
+                            theta_curve - sd_grid + offset,
+                            theta_curve + sd_grid + offset,
+                            color="C1",
+                            alpha=0.18,
+                            linewidth=0,
+                            label="μ̂ ± 1 circular SD" if k == k_min else None,
+                        )
+                    ax.plot(
+                        x_grid,
+                        theta_curve + offset,
+                        color="C1",
+                        lw=2,
+                        label="fit" if k == k_min else None,
+                    )
+                ax.scatter(x_data, theta_data, color="C0", s=20, alpha=0.6, edgecolors="none", label="data")
+                ax.scatter(x_data, theta_data + 2 * np.pi, color="C0", s=20, alpha=0.6, edgecolors="none")
+                ax.set_xlim(0, 2 * np.pi)
+                ax.set_ylim(0, 4 * np.pi)
+                ax.set_xticks([0, np.pi / 2, np.pi, 3 * np.pi / 2, 2 * np.pi])
+                ax.set_xticklabels(["0", "π/2", "π", "3π/2", "2π"])
+                ax.set_yticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi])
+                ax.set_yticklabels(["0", "π", "2π", "3π", "4π"])
+                ax.set_xlabel(self.feature_names[0])
+                ax.set_ylabel("θ")
+                ax.set_title("Fit overlay")
+                ax.legend(loc="best", frameon=False)
 
             ax = axes[1]
             ax.scatter(x_data, residuals, color="C0", s=20, alpha=0.6, edgecolors="none")
@@ -1221,10 +2597,63 @@ def plot(
         fig.tight_layout()
         return fig
 
+    def _draw_polar_clock(
+        self,
+        ax,
+        x_data: np.ndarray,
+        theta_data: np.ndarray,
+        n_arrow: int = 36,
+    ) -> None:
+        """Compass-frame fit panel for a single circular predictor.
+
+        Each rim position is a predictor angle ``x``; the arrow anchored
+        there points in the fitted direction ``μ̂(x)`` with length
+        ``ρ̂(x)``, so a vanishing arrow marks an ill-defined direction.
+        Observations are fixed-length arrows just outside the rim at their
+        predictor angle, pointing in their observed direction.
+        """
+        grid = np.linspace(0.0, 2 * np.pi, n_arrow, endpoint=False)
+        cos_fit, sin_fit = self._predict_embedding(grid[:, None])
+        mu = np.arctan2(sin_fit, cos_fit)
+        rho = np.clip(np.hypot(cos_fit, sin_fit), 0.0, 1.0)
+
+        rim = np.linspace(0.0, 2 * np.pi, 256)
+        ax.plot(np.cos(rim), np.sin(rim), color="0.85", lw=1.0, zorder=1)
+
+        fit_scale = 0.5  # display length of a ρ̂ = 1 arrow
+        ax.quiver(
+            np.cos(grid), np.sin(grid),
+            fit_scale * rho * np.cos(mu), fit_scale * rho * np.sin(mu),
+            angles="xy", scale_units="xy", scale=1.0,
+            color="C1", width=0.008, zorder=3,
+            label="fit: μ̂(x), length ρ̂(x)",
+        )
+        data_anchor, data_len = 1.12, 0.18
+        ax.quiver(
+            data_anchor * np.cos(x_data), data_anchor * np.sin(x_data),
+            data_len * np.cos(theta_data), data_len * np.sin(theta_data),
+            angles="xy", scale_units="xy", scale=1.0,
+            color="C0", alpha=0.6, width=0.005, zorder=2,
+            label="data: θ at rim x",
+        )
+        for ang, lab in ((0.0, "0"), (np.pi / 2, "π/2"), (np.pi, "π"), (3 * np.pi / 2, "3π/2")):
+            ax.text(
+                1.45 * np.cos(ang), 1.45 * np.sin(ang), lab,
+                ha="center", va="center", color="0.4",
+            )
+        ax.set_aspect("equal")
+        ax.set_xlim(-1.6, 1.6)
+        ax.set_ylim(-1.6, 1.6)
+        ax.set_axis_off()
+        ax.set_title("Fit clock: arrow = μ̂(x), length = ρ̂(x)")
+        ax.legend(loc="lower left", frameon=False, fontsize=8)
+
     def summary(self):
         """
         Print a summary of the regression results.
         """
+        if self.backend == "gam":
+            return self._summary_gam()
         print("\nCircular-Circular Regression\n")
         print(f"Circular Correlation Coefficient (rho): {self.result['rho']:.5f}")
         print(f"Mean Residual Cosine (A_k):             {self.result['A_k']:.5f}")
@@ -1283,6 +2712,24 @@ def _print_block(title: str, coefs: np.ndarray, ses: np.ndarray) -> None:
             "test uses χ² (Jammalamadaka & Sengupta 2001).\n"
         )
 
+    def _summary_gam(self) -> None:
+        """Summary for the smooth (gam) backend: circular correlation, residual
+        concentration, and the per-coordinate smooth effective dof / deviance
+        explained (no harmonic coefficient table, no higher-order χ² test)."""
+        print("\nCircular-Circular Regression (smooth / gam backend)\n")
+        print(f"Circular Correlation Coefficient (rho): {self.result['rho']:.5f}")
+        print(f"Mean Residual Cosine (A_k):             {self.result['A_k']:.5f}")
+        print(f"Residual Concentration (kappa):         {self.result['kappa']:.5f}\n")
+
+        print("Per-coordinate smooths:\n")
+        for coord in ("cos", "sin"):
+            edf = self.result["edf_by_smooth"][coord]
+            dev = self.result["deviance_explained"][coord]
+            terms = ", ".join(f"{name} edf={val:.2f}" for name, val in edf.items())
+            print(f"  {coord}(θ):  {terms};  deviance explained {dev * 100:.1f}%")
+
+        print(f"\n{self.result['message']}\n")
+
 
 # Markers used by LCRegression's formula parser.
 # `[^\W\d_]\w*` matches a Python-style identifier including Unicode letters
@@ -1293,8 +2740,17 @@ def _print_block(title: str, coefs: np.ndarray, ses: np.ndarray) -> None:
     rf"harmonic\s*\(\s*({_LC_IDENT})\s*(?:,\s*(?:k\s*=\s*)?(\d+)\s*)?\)"
 )
 _LC_UNSUPPORTED_RE = re.compile(r"\b(skew|flat)\s*\(")
-# Coefficient-name pattern as emitted by hea: e.g. "cos(theta)",
-# "sin(2 * theta)", or "cos(theta * 2)" — multiplier may appear on either side.
+# Coefficient-name pattern as emitted by hea's ``harmonic()`` term, e.g.
+# "harmonic(theta, 2, period = 6.283185307179586)cos1". The (var, order, cos|sin)
+# structure is read straight off the wrapper + the ``cos{j}``/``sin{j}`` suffix —
+# no fragile multiplier parsing.
+_LC_HARMONIC_COEF_RE = re.compile(
+    rf"^harmonic\(\s*(?P<var>{_LC_IDENT})\s*,\s*(?:k\s*=\s*)?\d+\s*,\s*period\s*=\s*[^)]+\)"
+    rf"(?P<trig>cos|sin)(?P<order>\d+)$"
+)
+# Explicit cos/sin coefficient names, for formulas that write the trig terms
+# directly ("y ~ cos(theta) + sin(theta)") instead of via harmonic():
+# "cos(theta)", "sin(2 * theta)", "cos(theta * 2)" — multiplier on either side.
 _LC_TRIG_RE = re.compile(
     rf"^(cos|sin)\(\s*"
     rf"(?:(?P<lmult>\d+)\s*\*\s*(?P<lvar>{_LC_IDENT})"
@@ -1315,12 +2771,15 @@ class LCRegression:
     The right-hand side accepts either a marker that expands to a Fourier
     basis, or fully explicit ``cos(...) / sin(...)`` terms (or both).
 
-    - ``"y ~ harmonic(theta)"`` — basic cosine model (Pewsey et al. 2014, §8.4.1)
+    - ``"y ~ harmonic(theta)"`` — basic cosine model (Pewsey et al. 2013, §8.4.1)
     - ``"y ~ harmonic(theta, k=K)"`` — extended model with K harmonics (§8.4.2)
     - ``"y ~ cos(theta) + sin(theta) + cos(3*theta) + sin(3*theta)"`` —
       fully explicit; useful for non-contiguous harmonic orders
     - ``"y ~ harmonic(theta, k=2) + temperature"`` — mix marker with extra
       linear covariates
+    - ``"y ~ s(theta, bs='cc')"`` — a **smooth** term dispatches the fit to
+      ``hea.models.gam`` (penalized spline) instead of ``hea.lm``; a cyclic
+      basis's period defaults to ``[0, 2π]`` (override via ``knots=``).
 
     Markers ``skew(theta)`` and ``flat(theta)`` are reserved for the
     nonlinear models in §8.4.3 / §8.4.4 and currently raise
@@ -1333,6 +2792,14 @@ class LCRegression:
         R-style formula. See above.
     data : pandas.DataFrame or polars.DataFrame
         Input data. Pandas inputs are converted to polars internally.
+    knots : dict, optional
+        Smooth-backend only. Per-variable boundary knots forwarded to
+        ``hea.gam``. Cyclic smooths (``bs='cc'``/``'cp'``) default to the
+        circular period ``[0, 2π]`` — pass this only to override (e.g. a
+        different period). Rejected for parametric formulas.
+    method : str, optional
+        Smooth-backend only. Smoothing-parameter selection for ``hea.gam``
+        (default ``"REML"``).
 
     Attributes
     ----------
@@ -1361,14 +2828,18 @@ class LCRegression:
 
     References
     ----------
-    Pewsey, A., Neuhäuser, M., Ruxton, G. D. (2014). *Circular Statistics
+    Pewsey, A., Neuhäuser, M., Ruxton, G. D. (2013). *Circular Statistics
     in R*. Oxford University Press, §8.4.
     """
 
     def __init__(
         self,
         formula: str,
-        data: Union[pd.DataFrame, "pl.DataFrame"],
+        data: pl.DataFrame,
+        *,
+        knots: Optional[dict] = None,
+        method: str = "REML",
+        **gam_kwargs,
     ):
         if not isinstance(formula, str) or "~" not in formula:
             raise ValueError(
@@ -1377,20 +2848,32 @@ def __init__(
 
         self.formula = formula
         self.response = formula.split("~", 1)[0].strip()
-        self.data = self._to_polars(data)
+        self.data = _to_polars(data)
         self.expanded_formula = self._expand_formula(formula)
-        self.lm_fit = _hea_lm(self.expanded_formula, self.data)
-        self.result = self._build_result()
 
-    @staticmethod
-    def _to_polars(data: Union[pd.DataFrame, "pl.DataFrame"]) -> "pl.DataFrame":
-        if isinstance(data, pl.DataFrame):
-            return data
-        if isinstance(data, pd.DataFrame):
-            return pl.from_pandas(data)
-        raise TypeError(
-            f"`data` must be a pandas or polars DataFrame; got {type(data).__name__}"
-        )
+        # Backend dispatch: a smooth term (s()/te()/…) → penalized hea.gam;
+        # a parametric RHS (harmonic()/cos()+sin()) → hea.lm. The circular
+        # semantics (amplitude/phase for lm; the cyclic smooth for gam) and a
+        # single circular-aware plot()/summary() wrap whichever backend ran.
+        self.backend = "gam" if _has_smooth(self.expanded_formula) else "lm"
+        if self.backend == "gam":
+            self.lm_fit = None
+            self.gam_fit = _hea_gam(
+                self.expanded_formula,
+                self.data,
+                knots=_resolve_cyclic_knots(self.expanded_formula, knots),
+                method=method,
+                **gam_kwargs,
+            )
+        else:
+            if knots is not None or gam_kwargs:
+                raise ValueError(
+                    "knots=/gam options only apply to smooth formulas "
+                    "(s()/te()/…); this parametric formula uses hea.lm."
+                )
+            self.gam_fit = None
+            self.lm_fit = _hea_lm(self.expanded_formula, self.data)
+        self.result = self._build_result()
 
     @staticmethod
     def _expand_formula(formula: str) -> str:
@@ -1406,18 +2889,24 @@ def _expand(match: "re.Match[str]") -> str:
             k = int(match.group(2)) if match.group(2) else 1
             if k < 1:
                 raise ValueError(f"harmonic(..., k={k}): k must be a positive integer.")
+            # Expand to explicit cos/sin terms (cos(θ) + sin(θ) + cos(2 * θ) + …)
+            # so the fitted coefficient names read cleanly in lm.summary() —
+            # ``cos(θ)``, ``sin(2 * θ)`` — rather than hea's verbose
+            # ``harmonic(θ, k=…, period=…)cos1`` labels. The fit is identical
+            # either way (same cos/sin design); only the names differ.
             terms = []
             for j in range(1, k + 1):
-                if j == 1:
-                    terms.append(f"cos({col}) + sin({col})")
-                else:
-                    terms.append(f"cos({j}*{col}) + sin({j}*{col})")
+                factor = col if j == 1 else f"{j} * {col}"
+                terms.append(f"cos({factor})")
+                terms.append(f"sin({factor})")
             return " + ".join(terms)
 
         expanded_rhs = _LC_HARMONIC_RE.sub(_expand, rhs)
         return f"{lhs.strip()} ~ {expanded_rhs.strip()}"
 
     def _build_result(self) -> dict:
+        if self.backend == "gam":
+            return self._build_result_gam()
         bhat_df = self.lm_fit.bhat
         coef_names = list(bhat_df.columns)
         coef_values = list(bhat_df.row(0))
@@ -1430,16 +2919,20 @@ def _build_result(self) -> dict:
         # Group cos/sin terms by (variable, multiplier).
         groups: dict = {}
         for name, value in coefficients.items():
-            m = _LC_TRIG_RE.match(name)
-            if not m:
-                continue
-            func = m.group(1)
-            if m.group("lvar") is not None:
-                var = m.group("lvar")
-                k = int(m.group("lmult"))
+            m = _LC_HARMONIC_COEF_RE.match(name)
+            if m:
+                func, var, k = m.group("trig"), m.group("var"), int(m.group("order"))
             else:
-                var = m.group("rvar")
-                k = int(m.group("rmult")) if m.group("rmult") else 1
+                # explicit cos()/sin() terms written directly in the formula
+                m = _LC_TRIG_RE.match(name)
+                if not m:
+                    continue
+                func = m.group(1)
+                if m.group("lvar") is not None:
+                    var, k = m.group("lvar"), int(m.group("lmult"))
+                else:
+                    var = m.group("rvar")
+                    k = int(m.group("rmult")) if m.group("rmult") else 1
             slot = groups.setdefault((var, k), {})
             slot[func] = (name, value)
 
@@ -1506,12 +2999,39 @@ def _build_result(self) -> dict:
             "residuals": np.asarray(residuals, dtype=float),
         }
 
+    def _build_result_gam(self) -> dict:
+        """Result dict for the smooth (gam) backend.
+
+        Mirrors the lm result's common keys (``fitted``/``residuals``/fit
+        metrics) so downstream code is backend-agnostic, and adds the smooth
+        summaries (``edf_total``, ``edf_by_smooth``, ``deviance_explained``).
+        There is no harmonic amplitude/phase decomposition — that is specific
+        to the parametric backend — so ``harmonics`` is empty.
+        """
+        g = self.gam_fit
+        return {
+            "coefficients": dict(zip(g.bhat.columns, g.bhat.row(0))),
+            "harmonics": [],
+            "edf_total": float(g.edf_total),
+            "edf_by_smooth": {k: float(v) for k, v in dict(g.edf_by_smooth).items()},
+            "sigma": float(g.sigma),
+            "r_squared": float(g.r_squared),
+            "deviance_explained": float(g.deviance_explained),
+            "aic": float(g.AIC),
+            "bic": float(g.BIC),
+            "fitted": np.asarray(g.fitted_values, dtype=float),
+            "residuals": np.asarray(g.residuals, dtype=float),
+        }
+
     def predict(
-        self, data: Union[pd.DataFrame, "pl.DataFrame"]
+        self, data: pl.DataFrame
     ) -> np.ndarray:
         """Predict the linear response for new values of the regressors."""
-        new = self._to_polars(data)
-        out = self.lm_fit.predict(new=new)
+        new = _to_polars(data)
+        if self.backend == "gam":
+            out = self.gam_fit.predict(newdata=new)["fit"].to_numpy()
+            return np.asarray(out, dtype=float)
+        out = self.lm_fit.predict(newdata=new)
         if isinstance(out, pl.DataFrame):
             out = out.to_numpy().ravel()
         return np.asarray(out, dtype=float)
@@ -1530,8 +3050,16 @@ def summary(self) -> None:
             print(f"Expanded formula: {self.expanded_formula}")
         print()
 
-        # hea.lm.summary() prints to stdout and returns None.
-        self.lm_fit.summary()
+        if self.backend == "gam":
+            # hea.gam.summary() prints the mgcv-style block itself (parametric
+            # coefficients + approximate smooth significance with edf + deviance
+            # explained). No harmonic table — the smooth replaces the harmonics.
+            self.gam_fit.summary()
+            return
+
+        # hea.models.lm.summary() returns a SummaryLm whose __repr__ is the
+        # R-style regression block (it no longer prints to stdout itself).
+        print(self.lm_fit.summary())
 
         if self.result["harmonics"]:
             self._print_harmonic_table()
@@ -1587,6 +3115,7 @@ def plot(
         ci: bool = True,
         pi: bool = False,
         level: float = 0.95,
+        polar: bool = False,
         axes=None,
     ):
         """Two-panel diagnostic figure.
@@ -1607,9 +3136,13 @@ def plot(
             Number of θ points used to draw the fitted curve.
         ci, pi : bool
             Whether to shade a confidence band (``ci``) and/or prediction
-            band (``pi``) at the requested ``level``.
+            band (``pi``) at the requested ``level``. The prediction band
+            (``pi``) is only available for the parametric (lm) backend.
         level : float
             Coverage probability for the bands (default 0.95).
+        polar : bool
+            Draw the left panel on polar axes (θ as angle, response as
+            radius) — natural for a circular predictor over its full period.
         axes : sequence of matplotlib Axes, optional
             Two pre-existing axes to draw into. If omitted, a fresh figure
             is created.
@@ -1620,6 +3153,16 @@ def plot(
         """
         import matplotlib.pyplot as plt
 
+        if self.backend == "gam":
+            return self._plot_gam(
+                figsize=figsize,
+                n_curve=n_curve,
+                ci=ci,
+                level=level,
+                polar=polar,
+                axes=axes,
+            )
+
         if not self.result["harmonics"]:
             raise ValueError(
                 "plot() requires at least one matched cos/sin pair "
@@ -1659,43 +3202,109 @@ def plot(
             arr = self.lm_fit.compute_pi_yhat(yhat=yhat_df, Xnew=grid_df, alpha=alpha).to_numpy()
             pi_lo, pi_hi = arr[:, 0], arr[:, 1]
 
-        if axes is None:
-            fig, axes = plt.subplots(1, 2, figsize=figsize or (11, 4.5))
-        else:
-            axes = list(axes)
-            if len(axes) != 2:
-                raise ValueError("`axes` must be a sequence of length 2.")
-            fig = axes[0].figure
-
-        ax = axes[0]
+        bands = []
         if pi_lo is not None:
-            ax.fill_between(
-                theta_grid, pi_lo, pi_hi,
-                color="C1", alpha=0.15, label=f"{int(level*100)}% PI",
-            )
+            bands.append((pi_lo, pi_hi, 0.15, f"{int(level * 100)}% PI"))
         if ci_lo is not None:
-            ax.fill_between(
-                theta_grid, ci_lo, ci_hi,
-                color="C1", alpha=0.30, label=f"{int(level*100)}% CI",
-            )
+            bands.append((ci_lo, ci_hi, 0.30, f"{int(level * 100)}% CI"))
+
+        fig, ax0, ax1 = _two_panel_axes(plt, figsize, polar, axes)
+        self._draw_fit_panel(
+            ax0, theta_var, theta_data, y_data, theta_grid, yhat, bands, polar
+        )
+
+        ax1.scatter(fitted, residuals, color="C0", s=20, alpha=0.6, edgecolors="none")
+        ax1.axhline(0.0, color="k", lw=0.5)
+        ax1.set_xlabel("Fitted")
+        ax1.set_ylabel("Residual")
+        ax1.set_title("Residuals vs fitted")
+
+        fig.tight_layout()
+        return fig
+
+    def _draw_fit_panel(
+        self, ax, theta_var, theta_data, y_data, theta_grid, yhat, bands, polar
+    ) -> None:
+        """Draw the left 'Fit overlay' panel shared by both backends.
+
+        Data scatter + fitted response-scale curve, with optional shaded
+        ``bands`` — each a ``(lo, hi, alpha, label)`` tuple, drawn in order so
+        a wider PI sits under a narrower CI. Cartesian by default; ``polar``
+        puts θ on the angular axis and the response on the radius.
+        """
+        for lo, hi, band_alpha, label in bands:
+            ax.fill_between(theta_grid, lo, hi, color="C1", alpha=band_alpha, label=label)
         ax.plot(theta_grid, yhat, color="C1", lw=2, label="fit")
-        ax.scatter(theta_data, y_data, color="C0", s=20, alpha=0.6, edgecolors="none", label="data")
-        ax.set_xlabel(theta_var)
-        ax.set_ylabel(self.response)
+        ax.scatter(
+            theta_data, y_data, color="C0", s=20, alpha=0.6,
+            edgecolors="none", label="data",
+        )
         ax.set_title("Fit overlay")
         ax.legend(loc="best", frameon=False)
+        if polar:
+            ax.set_xticks([0, np.pi / 2, np.pi, 3 * np.pi / 2])
+            ax.set_xticklabels(["0", "π/2", "π", "3π/2"])
+        else:
+            ax.set_xlabel(theta_var)
+            ax.set_ylabel(self.response)
+            # If the grid covers a full 2π span, mark the canonical ticks.
+            if float(theta_grid.min()) <= 0 and float(theta_grid.max()) >= 2 * np.pi:
+                ax.set_xticks([0, np.pi / 2, np.pi, 3 * np.pi / 2, 2 * np.pi])
+                ax.set_xticklabels(["0", "π/2", "π", "3π/2", "2π"])
+
+    def _gam_predictor(self) -> str:
+        """The (first) smooth predictor variable — the θ axis for plotting."""
+        vs = _smooth_vars(self.expanded_formula.split("~", 1)[1])
+        if not vs:
+            raise ValueError("No smooth predictor found in the formula.")
+        return vs[0]
+
+    def _plot_gam(self, figsize, n_curve, ci, level, polar, axes):
+        """Circular-aware overlay for the smooth (gam) backend.
+
+        The response-scale smooth is drawn over the raw scatter on a
+        period-aware grid ``[0, 2π]`` (a cyclic smooth is defined over the full
+        period), with a Wald confidence band from ``se.fit``; the right panel
+        is residuals vs fitted. Extra covariates are held at their column mean
+        (numeric) or mode (categorical).
+        """
+        import matplotlib.pyplot as plt
 
-        # If the grid covers a full 2π span, mark the canonical ticks.
-        if t_lo <= 0 and t_hi >= 2 * np.pi:
-            ax.set_xticks([0, np.pi / 2, np.pi, 3 * np.pi / 2, 2 * np.pi])
-            ax.set_xticklabels(["0", "π/2", "π", "3π/2", "2π"])
+        theta_var = self._gam_predictor()
+        theta_data = self.data[theta_var].to_numpy()
+        y_data = self.data[self.response].to_numpy()
+        fitted = self.result["fitted"]
+        residuals = self.result["residuals"]
 
-        ax = axes[1]
-        ax.scatter(fitted, residuals, color="C0", s=20, alpha=0.6, edgecolors="none")
-        ax.axhline(0.0, color="k", lw=0.5)
-        ax.set_xlabel("Fitted")
-        ax.set_ylabel("Residual")
-        ax.set_title("Residuals vs fitted")
+        theta_grid = np.linspace(0.0, 2 * np.pi, n_curve)
+        grid: dict = {theta_var: theta_grid}
+        for col in self.data.columns:
+            if col in (theta_var, self.response):
+                continue
+            series = self.data[col]
+            if series.dtype.is_numeric():
+                grid[col] = np.full(n_curve, float(series.mean()))
+            else:
+                grid[col] = [series.mode()[0]] * n_curve
+        grid_df = pl.DataFrame(grid)
+
+        yhat, ci_lo, ci_hi = _gam_predict_ci(
+            self.gam_fit, grid_df, level if ci else 0.0
+        )
+        bands = []
+        if ci_lo is not None:
+            bands.append((ci_lo, ci_hi, 0.30, f"{int(level * 100)}% CI"))
+
+        fig, ax0, ax1 = _two_panel_axes(plt, figsize, polar, axes)
+        self._draw_fit_panel(
+            ax0, theta_var, theta_data, y_data, theta_grid, yhat, bands, polar
+        )
+
+        ax1.scatter(fitted, residuals, color="C0", s=20, alpha=0.6, edgecolors="none")
+        ax1.axhline(0.0, color="k", lw=0.5)
+        ax1.set_xlabel("Fitted")
+        ax1.set_ylabel("Residual")
+        ax1.set_title("Residuals vs fitted")
 
         fig.tight_layout()
         return fig
diff --git a/pycircstat2/utils.py b/pycircstat2/utils.py
index 7a89488..9837589 100644
--- a/pycircstat2/utils.py
+++ b/pycircstat2/utils.py
@@ -3,7 +3,7 @@
 from typing import Union
 
 import numpy as np
-import pandas as pd
+import hea.io
 from scipy.special import i0e, i1e
 
 
@@ -137,7 +137,7 @@ def load_data(
     name: str,
     source: str = "fisher",
     print_meta: bool = False,
-) -> pd.DataFrame:
+) -> "hea.DataFrame":
     __source__ = ["fisher", "zar", "mardia", "pewsey", "jammalamadaka"]
 
     # check source
@@ -149,7 +149,18 @@ def load_data(
     # load data
     data_files = importlib_resources.files("pycircstat2")
     csv_path = str(data_files / f"data/{source}/{name}.csv")
-    csv_data = pd.read_csv(csv_path, index_col=0)
+    # ``hea.io.read_csv`` returns a tidyverse-capable ``hea.DataFrame`` (an IS-A
+    # subclass of ``pl.DataFrame``), so loaded datasets support tidy verbs
+    # (``mutate``/``filter``/``summarize``/``count``/…) directly — convenient in
+    # the example notebooks — while staying drop-in for any polars / array use.
+    csv_data = hea.io.read_csv(csv_path)
+    # pandas loaded these with index_col=0: the first column was the row index,
+    # excluded from the data. Polars has no index, so drop the first column to
+    # preserve that layout (columns line up by name and position with the old
+    # pandas frames). For most files this is an unnamed 1-based counter; for a
+    # few (e.g. zar/D4) it is a sample id pandas used as the index. Re-wrap so
+    # the hea.DataFrame subclass survives the native ``drop``.
+    csv_data = hea.DataFrame(csv_data.drop(csv_data.columns[0]))
 
     json_path = str(data_files / f"data/{source}/{name}.csv-metadata.json")
     with open(json_path) as f:
@@ -234,6 +245,67 @@ def A1(kappa: np.ndarray) -> np.ndarray:
     # i1e(κ)/i0e(κ) = (i1(κ) e^-κ)/(i0(κ) e^-κ) — stable for large κ where i0/i1 overflow.
     return i1e(kappa) / i0e(kappa)
 
+def A1prime(kappa: np.ndarray) -> np.ndarray:
+    r"""Derivative of the mean-resultant function ``A1(κ) = I_1(κ)/I_0(κ)``.
+
+    ``A1'(κ) = 1 − A1(κ)/κ − A1(κ)²`` — equivalently ``-∂²/∂κ² log I_0(κ)``'s
+    negative, i.e. the von Mises Fisher information for the concentration. As
+    ``κ → 0`` the ``A1/κ`` term is the removable singularity ``A1(κ)/κ → 1/2``,
+    so ``A1'(0) = 1/2``; the limit is filled in explicitly to avoid 0/0.
+    """
+    kappa = np.asarray(kappa, dtype=float)
+    a1 = A1(kappa)
+    with np.errstate(divide="ignore", invalid="ignore"):
+        out = 1.0 - np.where(kappa == 0.0, 0.5, a1 / kappa) - a1**2
+    return out[()] if out.ndim == 0 else out
+
+def A1prime2(kappa: np.ndarray) -> np.ndarray:
+    r"""Second derivative of ``A1(κ)``, by differentiating
+    ``A1' = 1 − A1/κ − A1²``:
+
+    ``A1''(κ) = −A1'/κ + A1/κ² − 2 A1 A1'``.
+
+    The ``−A1'/κ`` and ``A1/κ²`` terms are each ``≈ 1/(2κ)`` and cancel
+    catastrophically as ``κ → 0`` (the true value is ``≈ −3κ/8``), so below
+    ``κ < 0.01`` the Maclaurin series ``−3κ/8 + 5κ³/24 − 77κ⁵/1024`` is used;
+    both branches agree to ~1e-12 at the switch. ``A1''(0) = 0``.
+    """
+    kappa = np.asarray(kappa, dtype=float)
+    a1 = A1(kappa)
+    d1 = A1prime(kappa)
+    with np.errstate(divide="ignore", invalid="ignore"):
+        rec = -d1 / kappa + a1 / kappa**2 - 2.0 * a1 * d1
+    k2 = kappa * kappa
+    series = kappa * (-3.0 / 8.0 + k2 * (5.0 / 24.0 - k2 * (77.0 / 1024.0)))
+    out = np.where(kappa < 0.01, series, rec)
+    return out[()] if out.ndim == 0 else out
+
+def A1prime3(kappa: np.ndarray) -> np.ndarray:
+    r"""Third derivative of ``A1(κ)``, by differentiating ``A1''``:
+
+    ``A1'''(κ) = −A1''/κ + 2A1'/κ² − 2A1/κ³ − 2A1'² − 2 A1 A1''.``
+
+    Same removable cancellation as :func:`A1prime2` (here ``2A1'/κ²`` vs
+    ``2A1/κ³``, each ``≈ 1/κ²``): below ``κ < 0.01`` the Maclaurin series
+    ``−3/8 + 5κ²/8 − 385κ⁴/1024`` is used. ``A1'''(0) = −3/8``.
+    """
+    kappa = np.asarray(kappa, dtype=float)
+    a1 = A1(kappa)
+    d1 = A1prime(kappa)
+    d2 = A1prime2(kappa)
+    with np.errstate(divide="ignore", invalid="ignore"):
+        rec = (
+            -d2 / kappa
+            + 2.0 * d1 / kappa**2
+            - 2.0 * a1 / kappa**3
+            - 2.0 * d1 * d1
+            - 2.0 * a1 * d2
+        )
+    k2 = kappa * kappa
+    series = -3.0 / 8.0 + k2 * (5.0 / 8.0 - k2 * (385.0 / 1024.0))
+    out = np.where(kappa < 0.01, series, rec)
+    return out[()] if out.ndim == 0 else out
+
 def A1inv(R: float) -> float:
     # A1 maps kappa>=0 to [0, 1); clamp R to that range to avoid the
     # singularity at R=1 in the high-concentration branch.
diff --git a/pyproject.toml b/pyproject.toml
index ed6841a..befc899 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,9 +12,9 @@ requires-python = ">=3.10"
 dependencies = [
     "numpy",
     "scipy",
-    "pandas",
+    "polars",
     "matplotlib",
-    "hea>=0.1.2",
+    "hea>=0.1.4",
 ]
 readme = {file = "README.md", content-type = "text/markdown"}
 
@@ -27,10 +27,22 @@ dev = [
     "mkdocs-material-extensions",
     "mkdocstrings[python]",
     "watermark",
-    "polars",
+    "pandas",
     "ipykernel>=6.31.0",
 ]
 
 [tool.setuptools]
 packages = {find = {}}
 include-package-data = true
+
+# hea 0.1.4 (the general-family engine: GeneralFamily/gam.fit5, multi-formula
+# gam, gamlss_* helpers, subclassable Link) is published to PyPI, so the
+# circular-GAM work resolves it straight from [project.dependencies] — no
+# source override. For hea-side iteration, temporarily add:
+#   [tool.uv.sources]
+#   hea = { path = "../hea", editable = true }
+
+[tool.pytest.ini_options]
+# Only collect the project's own tests; skip the vendored reference copy of the
+# original pycircstat under dev/ref (it depends on `nose`).
+testpaths = ["tests"]
diff --git a/tests/test_base.py b/tests/test_base.py
index 7e30aec..ab60917 100644
--- a/tests/test_base.py
+++ b/tests/test_base.py
@@ -9,8 +9,8 @@ def test_Circular():
     # Ch26.2 Example 3 (Zar, 2010)
     data_zar_ex3_ch26 = load_data("D2", source="zar")
     circ_zar_ex3_ch26 = Circular(
-        data=data_zar_ex3_ch26["θ"].values[:],
-        w=data_zar_ex3_ch26["w"].values[:],
+        data=data_zar_ex3_ch26["θ"].to_numpy(),
+        w=data_zar_ex3_ch26["w"].to_numpy(),
         unit="degree",
     )
 
diff --git a/tests/test_correlation.py b/tests/test_correlation.py
index 69f10e0..31e8f56 100644
--- a/tests/test_correlation.py
+++ b/tests/test_correlation.py
@@ -8,8 +8,8 @@ def test_circ_corrcc():
 
     # Example 27.20 (Zar, 2010)
     d_ex20_ch27 = load_data("D20", source="zar")
-    a = Circular(data=d_ex20_ch27["Insect"].values[:])
-    b = Circular(data=d_ex20_ch27["Light"].values[:])
+    a = Circular(data=d_ex20_ch27["Insect"].to_numpy())
+    b = Circular(data=d_ex20_ch27["Light"].to_numpy())
 
     # test Fisher & Lee, 1983
     res = circ_corrcc(a, b, test=True, method="fl")
@@ -21,8 +21,8 @@ def test_circ_corrcc():
 
     # Example 27.22 (Zar, 2010)
     d_ex22_ch27 = load_data("D22", source="zar")
-    a = Circular(data=d_ex22_ch27["evening"].values[:])
-    b = Circular(data=d_ex22_ch27["morning"].values[:])
+    a = Circular(data=d_ex22_ch27["evening"].to_numpy())
+    b = Circular(data=d_ex22_ch27["morning"].to_numpy())
 
     # test nonparametric
     res = circ_corrcc(a, b, test=True, method="nonparametric")
@@ -32,8 +32,8 @@ def test_circ_corrcc():
 
     # test Jammalamadaka & SenGupta, 2001
     d_milwaukee = load_data("milwaukee", source="jammalamadaka")
-    theta = np.deg2rad(d_milwaukee["theta"].values[:])
-    psi = np.deg2rad(d_milwaukee["psi"].values[:])
+    theta = np.deg2rad(d_milwaukee["theta"].to_numpy())
+    psi = np.deg2rad(d_milwaukee["psi"].to_numpy())
 
     res = circ_corrcc(theta, psi, test=True, method="js")
     np.testing.assert_approx_equal(res.r, 0.2704648, significant=4)
@@ -46,8 +46,8 @@ def test_circ_corrcl():
 
     # Example 27.21 (Zar, 2010)
     d_ex21_ch27 = load_data("D21", source="zar")
-    a = Circular(data=d_ex21_ch27["θ"].values[:]).alpha
-    x = d_ex21_ch27["X"].values[:]
+    a = Circular(data=d_ex21_ch27["θ"].to_numpy()).alpha
+    x = d_ex21_ch27["X"].to_numpy()
 
     res = circ_corrcl(a, x)
     np.testing.assert_approx_equal(res.r, 0.9854, significant=4)
diff --git a/tests/test_descriptive.py b/tests/test_descriptive.py
index 0c8e692..afdd254 100644
--- a/tests/test_descriptive.py
+++ b/tests/test_descriptive.py
@@ -1,4 +1,5 @@
 import numpy as np
+import polars as pl
 import pytest
 
 from pycircstat2 import Circular, load_data
@@ -30,7 +31,7 @@
 def test_circ_mean():
     # Example 26.4 (Zar, 2010)
     data_zar_ex4_ch26 = load_data("D1", source="zar")
-    circ_zar_ex4_ch26 = Circular(data=data_zar_ex4_ch26["θ"].values[:])
+    circ_zar_ex4_ch26 = Circular(data=data_zar_ex4_ch26["θ"].to_numpy())
     m, r = circ_mean_and_r(alpha=circ_zar_ex4_ch26.alpha, w=circ_zar_ex4_ch26.w)
 
     np.testing.assert_approx_equal(np.rad2deg(m), 99, significant=1)
@@ -39,7 +40,7 @@ def test_circ_mean():
     # ch26 Example 5 (Zar, 2010)
     data_zar_ex5_ch26 = load_data("D2", source="zar")
     circ_zar_ex5_ch26 = Circular(
-        data=data_zar_ex5_ch26["θ"].values[:], w=data_zar_ex5_ch26["w"].values[:]
+        data=data_zar_ex5_ch26["θ"].to_numpy(), w=data_zar_ex5_ch26["w"].to_numpy()
     )
     m, r = circ_mean_and_r(alpha=circ_zar_ex5_ch26.alpha, w=circ_zar_ex5_ch26.w)
 
@@ -49,7 +50,7 @@ def test_circ_mean():
 
 def test_circ_std():
     data_zar_ex4_ch26 = load_data("D1", source="zar")
-    circ_zar_ex4_ch26 = Circular(data=data_zar_ex4_ch26["θ"].values[:])
+    circ_zar_ex4_ch26 = Circular(data=data_zar_ex4_ch26["θ"].to_numpy())
 
     # Angular dispersion from Ch26.5 (Zar, 2010)
     # Part of Ch26 Example 4, using data from Ch26 Example 2
@@ -68,7 +69,7 @@ def test_circ_std():
 
     data_zar_ex5_ch26 = load_data("D2", source="zar")
     circ_zar_ex5_ch26 = Circular(
-        data=data_zar_ex5_ch26["θ"].values[:], w=data_zar_ex5_ch26["w"].values[:]
+        data=data_zar_ex5_ch26["θ"].to_numpy(), w=data_zar_ex5_ch26["w"].to_numpy()
     )
 
     # compute directly from r
@@ -91,7 +92,7 @@ def test_circ_std():
 def test_circ_median():
     # Ch26.6 P657 (Zar, 2010)
     data_zar_ex2_ch26 = load_data("D1", source="zar")
-    circ_zar_ex2_ch26 = Circular(data=data_zar_ex2_ch26["θ"].values[:])
+    circ_zar_ex2_ch26 = Circular(data=data_zar_ex2_ch26["θ"].to_numpy())
     median = circ_median(
         alpha=circ_zar_ex2_ch26.alpha,
         method="deviation",
@@ -101,7 +102,7 @@ def test_circ_median():
     np.testing.assert_approx_equal(np.rad2deg(median), 103.0, significant=1)
 
     # Ch26.6 P657 (Zar, 2010) droped the first point
-    circ_zar_ex2_ch26_odd = Circular(data=data_zar_ex2_ch26["θ"].values[:][1:])
+    circ_zar_ex2_ch26_odd = Circular(data=data_zar_ex2_ch26["θ"].to_numpy()[1:])
     median = circ_median(
         alpha=circ_zar_ex2_ch26_odd.alpha,
         method="deviation",
@@ -112,7 +113,7 @@ def test_circ_median():
 
     # mallard data (mardia, 1972)
     data_mallard = load_data("mallard", source="mardia")
-    circ_mallard = Circular(data=data_mallard["θ"].values[:], w=data_mallard["w"].values[:])
+    circ_mallard = Circular(data=data_mallard["θ"].to_numpy(), w=data_mallard["w"].to_numpy())
     median = circ_median(
         alpha=circ_mallard.alpha,
         w=circ_mallard.w,
@@ -252,9 +253,9 @@ def test_circ_mean_deviation():
 
     d22 = load_data("B10", source="fisher")
 
-    d22s1 = np.deg2rad(d22[d22["set"] == 1]["θ"].values[:])
-    d22s2 = np.deg2rad(d22[d22["set"] == 2]["θ"].values[:])
-    d22s3 = np.deg2rad(d22[d22["set"] == 3]["θ"].values[:])
+    d22s1 = np.deg2rad(d22.filter(pl.col("set") == 1)["θ"].to_numpy())
+    d22s2 = np.deg2rad(d22.filter(pl.col("set") == 2)["θ"].to_numpy())
+    d22s3 = np.deg2rad(d22.filter(pl.col("set") == 3)["θ"].to_numpy())
 
     np.testing.assert_allclose(
         circ_mean_deviation(d22s1, d22s1),
@@ -275,7 +276,7 @@ def test_circ_mean_deviation():
 def test_circ_mean_ci():
     # method: approximate (from P619, Zar, 2010)
     data_zar_ex4_ch26 = load_data("D1", source="zar")
-    circ_zar_ex4_ch26 = Circular(data=data_zar_ex4_ch26["θ"].values[:])
+    circ_zar_ex4_ch26 = Circular(data=data_zar_ex4_ch26["θ"].to_numpy())
 
     # computed directly from r and n
     lb, ub = circ_mean_ci(
@@ -299,7 +300,7 @@ def test_circ_mean_ci():
 
     # method: dispersion (from P78, Fisher, 1993)
     d_ex3 = load_data("B6", "fisher")
-    c_ex3_s2 = Circular(np.sort(d_ex3[d_ex3.set == 2]["θ"].values[:]))
+    c_ex3_s2 = Circular(np.sort(d_ex3.filter(pl.col("set") == 2)["θ"].to_numpy()))
     lb, ub = circ_mean_ci(method="dispersion", alpha=c_ex3_s2.alpha)
     np.testing.assert_approx_equal(np.rad2deg(lb), 232.7, significant=4)
     np.testing.assert_approx_equal(np.rad2deg(ub), 262.5, significant=4)
@@ -325,14 +326,14 @@ def test_circ_mean_ci():
 def test_circ_median_ci():
     d_ex3 = load_data("B6", "fisher")
     c_ex3_s0 = Circular(
-        data=np.sort(d_ex3[d_ex3.set == 2]["θ"].values[:][:10]),
+        data=np.sort(d_ex3.filter(pl.col("set") == 2)["θ"].to_numpy()[:10]),
         kwargs_median={"method": "count"},
     )
     c_ex3_s1 = Circular(
-        data=np.sort(d_ex3[d_ex3.set == 2]["θ"].values[:][:20]),
+        data=np.sort(d_ex3.filter(pl.col("set") == 2)["θ"].to_numpy()[:20]),
         kwargs_median={"method": "deviation"},
     )
-    c_ex3_s2 = Circular(data=np.sort(d_ex3[d_ex3.set == 2]["θ"].values[:]))
+    c_ex3_s2 = Circular(data=np.sort(d_ex3.filter(pl.col("set") == 2)["θ"].to_numpy()))
 
     # n is too small for proper estimation of median ci
     lb, ub, ci = circ_median_ci(median=float(c_ex3_s0.median), alpha=c_ex3_s0.alpha)
@@ -361,8 +362,8 @@ def test_circ_median_ci_idx_wrap():
 
 def test_circ_mean_and_r_of_means():
     data = load_data("D4", source="zar")
-    ms = np.deg2rad(data.values[:][:, 0])
-    rs = data.values[:][:, 1]
+    ms = np.deg2rad(data.to_numpy()[:, 0])
+    rs = data.to_numpy()[:, 1]
 
     m, r = circ_mean_and_r_of_means(ms=ms, rs=rs)
     np.testing.assert_approx_equal(np.rad2deg(m), 152.0, significant=3)
@@ -370,30 +371,30 @@ def test_circ_mean_and_r_of_means():
 
 
 def test_circ_skewness():
-    b11 = load_data("B11", source="fisher")["θ"].values[:]
+    b11 = load_data("B11", source="fisher")["θ"].to_numpy()
     c11 = Circular(data=b11)
     skewness = circ_skewness(alpha=c11.alpha)
     np.testing.assert_approx_equal(skewness, -0.92, significant=2)
 
 
 def test_circ_kurtosis():
-    b11 = load_data("B11", source="fisher")["θ"].values[:]
+    b11 = load_data("B11", source="fisher")["θ"].to_numpy()
     c11 = Circular(data=b11)
     kurtosis = circ_kurtosis(alpha=c11.alpha)
     np.testing.assert_approx_equal(kurtosis, 6.64, significant=3)
 
 
 def test_circ_dispersion():
-    b11 = load_data("B11", source="fisher")["θ"].values[:]
+    b11 = load_data("B11", source="fisher")["θ"].to_numpy()
     c11 = Circular(data=b11)
     dispersion = circ_dispersion(alpha=c11.alpha)
     np.testing.assert_approx_equal(dispersion, 0.24, significant=2)
 
 
 def test_circ_moment():
-    # Section 3.2, Pewsey (2014) P24
+    # Section 3.2, Pewsey (2013) P24
 
-    b11 = load_data("B11", source="fisher")["θ"].values[:]
+    b11 = load_data("B11", source="fisher")["θ"].to_numpy()
     c11 = Circular(data=b11)
 
     # first moment == mean
@@ -418,7 +419,7 @@ def test_circ_moment():
 def test_compute_smooth_params():
     from pycircstat2.utils import time2float
 
-    d_fisher_b1 = load_data("B1", source="fisher")["time"].values[:]
+    d_fisher_b1 = load_data("B1", source="fisher")["time"].to_numpy()
     c_fisher_b1 = Circular(time2float(d_fisher_b1), unit="hour")
     h0 = compute_smooth_params(c_fisher_b1.r, c_fisher_b1.n)
     np.testing.assert_approx_equal(h0, 1.06, significant=2)
diff --git a/tests/test_distributions.py b/tests/test_distributions.py
index 9894e6e..033942e 100644
--- a/tests/test_distributions.py
+++ b/tests/test_distributions.py
@@ -298,13 +298,16 @@ def test_circular_cdf_is_periodic():
             0.1, mu=np.array([0.0, 0.1]), kappa=1.0, nu=0.1
         ),
     ),
+    # jonespewsey + sineskewed pdf/logpdf now ACCEPT per-obs parameter arrays
+    # (the Phase-1 regression contract); cdf and the other methods remain
+    # scalar-only, so the guard pins those instead.
     (
-        "jonespewsey",
-        lambda: jonespewsey.pdf(0.1, mu=0.0, kappa=np.array([1.0, 1.1]), psi=0.1),
+        "jonespewsey_cdf",
+        lambda: jonespewsey.cdf(0.1, mu=0.0, kappa=np.array([1.0, 1.1]), psi=0.1),
     ),
     (
-        "jonespewsey_sineskewed",
-        lambda: jonespewsey_sineskewed.pdf(
+        "jonespewsey_sineskewed_cdf",
+        lambda: jonespewsey_sineskewed.cdf(
             0.1, xi=0.0, kappa=np.array([1.0, 1.1]), psi=0.1, lmbd=0.1
         ),
     ),
@@ -1762,10 +1765,619 @@ def test_katojones_gamma_rho_close_to_one():
     _assert_monotonic_cdf_ppf(dist, theta, np.linspace(0, 1, 257))
 
 
-@pytest.mark.parametrize("alpha", [1e-6, 1.999])  # Lévy-like and almost-Gaussian
+@pytest.mark.parametrize("alpha", [1e-6, 1.999])  # degenerate-small and almost-Gaussian
 def test_wrapstable_alpha_extremes(alpha):
     params = (0.0, alpha, 0.0, 0.7)  # delta, alpha, beta, gamma
     dist = wrapstable(*params)
     theta = np.linspace(0, 2 * np.pi, 257)
-    _check_pdf_normalizes(dist, params=None, atol=5e-5 if alpha < 0.01 else 1e-6)
+    if alpha < 0.01:
+        # Degenerate small-α corner (validation plan §5): ρ_p ≈ e^{−1}
+        # never decays, the series hits _WRAPSTABLE_MAX_TERMS undecayed,
+        # and the "pdf" is a ~20k-harmonic Dirichlet-kernel artifact for a
+        # law that tends to atom + uniform (no density exists). Its mass
+        # is exactly 1 by cosine orthogonality — an algebraic identity
+        # quad cannot measure through ~20k oscillations (it warned and
+        # "passed" only by panel-wise cancellation) — so this cell asserts
+        # graceful degradation: finite values and a monotone unit-range
+        # cdf below.
+        assert np.all(np.isfinite(dist.pdf(theta)))
+    else:
+        _check_pdf_normalizes(dist, params=None, atol=1e-6)
     _assert_monotonic_cdf_ppf(dist, theta, np.linspace(0, 1, 257), cdf_tol=1e-9, ppf_tol=1e-9)
+
+
+# ---------------------------------------------------------------------------
+# Phase 1 regression contract: role overlay + analytic log-density derivatives
+# ---------------------------------------------------------------------------
+
+from pycircstat2.distributions import _RegressionReady  # noqa: E402
+from pycircstat2.utils import A1, A1prime  # noqa: E402
+
+
+def test_vonmises_regression_overlay():
+    """The role overlay derives both directions and attaches the right links."""
+    assert isinstance(vonmises, _RegressionReady)
+    assert vonmises.param_roles == {"mu": "location", "kappa": "concentration"}
+    # role -> name(s), one-to-many
+    assert vonmises.params_by_role() == {
+        "location": ["mu"],
+        "concentration": ["kappa"],
+    }
+    # name -> role -> default link
+    assert vonmises.link_for("mu") == "tanhalf"
+    assert vonmises.link_for("kappa") == "log"
+
+
+def test_A1prime_matches_finite_difference_and_limit():
+    """A1'(κ) = 1 − A1/κ − A1², with the removable κ→0 limit A1'(0)=1/2."""
+    assert A1prime(0.0) == pytest.approx(0.5)
+    kappa = np.array([0.05, 0.5, 1.0, 3.0, 8.0, 25.0])
+    h = 1e-6
+    fd = (A1(kappa + h) - A1(kappa - h)) / (2 * h)
+    np.testing.assert_allclose(A1prime(kappa), fd, atol=1e-7)
+
+
+def _logpdf_vm(x, mu, kappa):
+    return vonmises.logpdf(x, mu, kappa)
+
+
+@pytest.mark.parametrize("kappa", [0.3, 1.0, 4.0, 12.0])
+@pytest.mark.parametrize("mu", [0.4, 2.5, 5.0])
+def test_vonmises_dlogpdf_matches_finite_difference(mu, kappa):
+    """l1: ∂logpdf/∂μ and ∂logpdf/∂κ against central differences of logpdf."""
+    x = np.linspace(0.1, 2 * np.pi - 0.1, 23)
+    grad = vonmises.dlogpdf(x, mu, kappa)
+
+    hm, hk = 1e-6, 1e-6
+    d_mu = (_logpdf_vm(x, mu + hm, kappa) - _logpdf_vm(x, mu - hm, kappa)) / (2 * hm)
+    d_kappa = (_logpdf_vm(x, mu, kappa + hk) - _logpdf_vm(x, mu, kappa - hk)) / (2 * hk)
+
+    np.testing.assert_allclose(grad["mu"], d_mu, atol=1e-6)
+    np.testing.assert_allclose(grad["kappa"], d_kappa, atol=1e-6)
+
+
+@pytest.mark.parametrize("kappa", [0.3, 1.0, 4.0, 12.0])
+@pytest.mark.parametrize("mu", [0.4, 2.5, 5.0])
+def test_vonmises_d2logpdf_matches_finite_difference(mu, kappa):
+    """l2: the three unique second partials against finite differences of l1."""
+    x = np.linspace(0.1, 2 * np.pi - 0.1, 23)
+    hess = vonmises.d2logpdf(x, mu, kappa)
+    hm, hk = 1e-6, 1e-6
+
+    # ∂²/∂μ² and ∂²/∂μ∂κ from differencing the μ-score; ∂²/∂κ² from the κ-score.
+    def g_mu(m, k):
+        return vonmises.dlogpdf(x, m, k)["mu"]
+
+    def g_kappa(m, k):
+        return vonmises.dlogpdf(x, m, k)["kappa"]
+
+    d_mumu = (g_mu(mu + hm, kappa) - g_mu(mu - hm, kappa)) / (2 * hm)
+    d_mukappa = (g_mu(mu, kappa + hk) - g_mu(mu, kappa - hk)) / (2 * hk)
+    d_kappakappa = (g_kappa(mu, kappa + hk) - g_kappa(mu, kappa - hk)) / (2 * hk)
+
+    np.testing.assert_allclose(hess[("mu", "mu")], d_mumu, atol=1e-6)
+    np.testing.assert_allclose(hess[("mu", "kappa")], d_mukappa, atol=1e-6)
+    np.testing.assert_allclose(hess[("kappa", "kappa")], d_kappakappa, atol=1e-6)
+    # mixed partial is symmetric: differencing the κ-score in μ agrees
+    d_kappamu = (g_kappa(mu + hm, kappa) - g_kappa(mu - hm, kappa)) / (2 * hm)
+    np.testing.assert_allclose(hess[("mu", "kappa")], d_kappamu, atol=1e-6)
+
+
+def test_vonmises_derivatives_vectorize_over_per_obs_params():
+    """logpdf and its derivatives broadcast over per-observation μ, κ arrays —
+    the hard requirement for concentration smoothing."""
+    rng = np.random.default_rng(0)
+    n = 50
+    x = rng.uniform(0, 2 * np.pi, n)
+    mu = rng.uniform(0, 2 * np.pi, n)
+    kappa = rng.uniform(0.2, 10.0, n)
+
+    grad = vonmises.dlogpdf(x, mu, kappa)
+    hess = vonmises.d2logpdf(x, mu, kappa)
+    assert grad["mu"].shape == (n,)
+    assert hess[("kappa", "kappa")].shape == (n,)
+    third = vonmises.d3logpdf(x, mu, kappa)
+    fourth = vonmises.d4logpdf(x, mu, kappa)
+    assert all(v.shape == (n,) for v in third.values())
+    assert all(v.shape == (n,) for v in fourth.values())
+    # per-obs result equals the scalar call element-by-element
+    for i in (0, 17, 49):
+        gi = vonmises.dlogpdf(x[i], mu[i], kappa[i])
+        assert gi["mu"] == pytest.approx(grad["mu"][i])
+        assert gi["kappa"] == pytest.approx(grad["kappa"][i])
+
+
+def test_vonmises_dlogpdf_equals_CL_inline_score():
+    """Pins the §3.2 equivalence: the distribution's scores are exactly the
+    expressions CLRegression computes inline (κ sin(θ−μ); cos(θ−μ) − A1(κ))."""
+    rng = np.random.default_rng(1)
+    x = rng.uniform(0, 2 * np.pi, 40)
+    mu, kappa = 1.3, 3.7
+    grad = vonmises.dlogpdf(x, mu, kappa)
+    np.testing.assert_allclose(grad["mu"], kappa * np.sin(x - mu), atol=1e-12)
+    np.testing.assert_allclose(grad["kappa"], np.cos(x - mu) - A1(kappa), atol=1e-12)
+    # CL's μ-weight is the Fisher information −E[∂²_{μμ}ℓ] = κ A1(κ); the
+    # expectation is over the model, so integrate against the vM density.
+    grid = np.linspace(0.0, 2 * np.pi, 20001)
+    f = vonmises.pdf(grid, mu, kappa)
+    obs_info = -vonmises.d2logpdf(grid, mu, kappa)[("mu", "mu")]
+    fisher_info = np.trapezoid(obs_info * f, grid)
+    assert fisher_info == pytest.approx(kappa * A1(kappa), rel=1e-3)
+
+
+# ---------------------------------------------------------------------------
+# Phase 1 regression contract: links (TanHalfLink + the get_link resolver)
+# ---------------------------------------------------------------------------
+
+from hea.family import Link as _HeaLink  # noqa: E402
+from hea.family import LogLink as _HeaLogLink  # noqa: E402
+
+from pycircstat2.distributions import TanHalfLink, get_link  # noqa: E402
+
+
+def test_get_link_resolves_contract_names():
+    """``default_links`` names resolve end-to-end to ``hea.family.Link``
+    objects (the Phase-2 face); instances pass through; unknown names raise."""
+    mu_link = get_link(vonmises.link_for("mu"))
+    kappa_link = get_link(vonmises.link_for("kappa"))
+    assert isinstance(mu_link, TanHalfLink)
+    assert isinstance(mu_link, _HeaLink)
+    assert isinstance(kappa_link, _HeaLogLink)
+    custom = TanHalfLink()
+    assert get_link(custom) is custom
+    with pytest.raises(ValueError, match="unknown link"):
+        get_link("no-such-link")
+
+
+def test_tanhalf_roundtrip_branch_and_monotonicity():
+    """``linkinv`` inverts ``link`` on the principal branch (−π, π); ``link``
+    is 2π-periodic in μ (any angle parameterization hits the principal
+    branch); the map is monotone (``mu_eta`` > 0) with range (−π, π)."""
+    link = TanHalfLink()
+    mu = np.linspace(-np.pi + 1e-3, np.pi - 1e-3, 101)
+    np.testing.assert_allclose(link.linkinv(link.link(mu)), mu, atol=1e-12)
+    np.testing.assert_allclose(
+        link.link(mu + 2 * np.pi), link.link(mu), rtol=1e-6, atol=1e-12
+    )
+    eta = np.linspace(-50.0, 50.0, 201)
+    assert np.all(np.abs(link.linkinv(eta)) < np.pi)
+    assert np.all(link.mu_eta(eta) > 0)
+
+
+def test_tanhalf_mu_eta_matches_finite_difference():
+    """``mu_eta`` = d linkinv/dη against central differences."""
+    link = TanHalfLink()
+    eta = np.linspace(-8.0, 8.0, 81)
+    h = 1e-6
+    fd = (link.linkinv(eta + h) - link.linkinv(eta - h)) / (2 * h)
+    np.testing.assert_allclose(link.mu_eta(eta), fd, atol=1e-8)
+
+
+def test_tanhalf_dlink_chain_matches_finite_difference():
+    """``d2link``/``d3link``/``d4link`` are successive μ-derivatives of
+    g(μ) = tan(μ/2): each analytic order matches a central difference of the
+    order below (the FD convention of the l1/l2 contract tests). Also pins
+    the mgcv identity ``mu_eta(g(μ)) = 1/g′(μ)``."""
+    link = TanHalfLink()
+    mu = np.linspace(-2.4, 2.4, 49)
+    h = 1e-6
+
+    def gprime(m):
+        t = np.tan(0.5 * m)
+        return 0.5 * (1.0 + t * t)
+
+    fd_g1 = (link.link(mu + h) - link.link(mu - h)) / (2 * h)
+    np.testing.assert_allclose(gprime(mu), fd_g1, rtol=1e-8)
+    np.testing.assert_allclose(link.mu_eta(link.link(mu)), 1.0 / gprime(mu), rtol=1e-12)
+
+    fd_g2 = (gprime(mu + h) - gprime(mu - h)) / (2 * h)
+    np.testing.assert_allclose(link.d2link(mu), fd_g2, rtol=1e-6, atol=1e-8)
+    fd_g3 = (link.d2link(mu + h) - link.d2link(mu - h)) / (2 * h)
+    np.testing.assert_allclose(link.d3link(mu), fd_g3, rtol=1e-6, atol=1e-8)
+    fd_g4 = (link.d3link(mu + h) - link.d3link(mu - h)) / (2 * h)
+    np.testing.assert_allclose(link.d4link(mu), fd_g4, rtol=1e-6, atol=1e-8)
+
+
+# ---------------------------------------------------------------------------
+# Phase 1 regression contract: l3/l4 depth (full outer Newton for the bridge)
+# ---------------------------------------------------------------------------
+
+from pycircstat2.utils import A1prime2, A1prime3  # noqa: E402
+
+
+def test_A1prime2_A1prime3_match_finite_difference_and_limits():
+    """A1'' and A1''' against central differences of the order below, plus
+    the κ→0 limits (0 and −3/8). The grid covers both sides of the
+    series/recurrence switch at κ = 0.01 without straddling it."""
+    assert A1prime2(0.0) == pytest.approx(0.0)
+    assert A1prime3(0.0) == pytest.approx(-3.0 / 8.0)
+    kappa = np.array([0.002, 0.02, 0.05, 0.5, 1.0, 3.0, 8.0, 25.0])
+    h = 1e-6
+    fd2 = (A1prime(kappa + h) - A1prime(kappa - h)) / (2 * h)
+    np.testing.assert_allclose(A1prime2(kappa), fd2, atol=1e-7)
+    fd3 = (A1prime2(kappa + h) - A1prime2(kappa - h)) / (2 * h)
+    np.testing.assert_allclose(A1prime3(kappa), fd3, atol=1e-6)
+
+
+@pytest.mark.parametrize("kappa", [0.3, 1.0, 4.0, 12.0])
+@pytest.mark.parametrize("mu", [0.4, 2.5, 5.0])
+def test_vonmises_d3logpdf_matches_finite_difference(mu, kappa):
+    """l3: the four unique third partials against finite differences of l2."""
+    x = np.linspace(0.1, 2 * np.pi - 0.1, 23)
+    third = vonmises.d3logpdf(x, mu, kappa)
+    h = 1e-6
+
+    def l2(m, k):
+        return vonmises.d2logpdf(x, m, k)
+
+    d_mmm = (l2(mu + h, kappa)[("mu", "mu")] - l2(mu - h, kappa)[("mu", "mu")]) / (2 * h)
+    d_mmk = (l2(mu, kappa + h)[("mu", "mu")] - l2(mu, kappa - h)[("mu", "mu")]) / (2 * h)
+    d_mkk = (l2(mu, kappa + h)[("mu", "kappa")] - l2(mu, kappa - h)[("mu", "kappa")]) / (2 * h)
+    d_kkk = (
+        l2(mu, kappa + h)[("kappa", "kappa")] - l2(mu, kappa - h)[("kappa", "kappa")]
+    ) / (2 * h)
+
+    np.testing.assert_allclose(third[("mu", "mu", "mu")], d_mmm, atol=1e-5)
+    np.testing.assert_allclose(third[("mu", "mu", "kappa")], d_mmk, atol=1e-6)
+    np.testing.assert_allclose(third[("mu", "kappa", "kappa")], d_mkk, atol=1e-6)
+    np.testing.assert_allclose(third[("kappa", "kappa", "kappa")], d_kkk, atol=1e-6)
+    # symmetry cross-check: differencing the (μ,κ) entry in μ also gives μμκ
+    d_mmk2 = (l2(mu + h, kappa)[("mu", "kappa")] - l2(mu - h, kappa)[("mu", "kappa")]) / (
+        2 * h
+    )
+    np.testing.assert_allclose(third[("mu", "mu", "kappa")], d_mmk2, atol=1e-6)
+
+
+@pytest.mark.parametrize("kappa", [0.3, 1.0, 4.0, 12.0])
+@pytest.mark.parametrize("mu", [0.4, 2.5, 5.0])
+def test_vonmises_d4logpdf_matches_finite_difference(mu, kappa):
+    """l4: the five unique fourth partials against finite differences of l3."""
+    x = np.linspace(0.1, 2 * np.pi - 0.1, 23)
+    fourth = vonmises.d4logpdf(x, mu, kappa)
+    h = 1e-6
+
+    def l3(m, k):
+        return vonmises.d3logpdf(x, m, k)
+
+    pairs = [
+        (("mu", "mu", "mu", "mu"), ("mu", "mu", "mu"), "mu"),
+        (("mu", "mu", "mu", "kappa"), ("mu", "mu", "mu"), "kappa"),
+        (("mu", "mu", "kappa", "kappa"), ("mu", "mu", "kappa"), "kappa"),
+        (("mu", "kappa", "kappa", "kappa"), ("mu", "kappa", "kappa"), "kappa"),
+        (("kappa", "kappa", "kappa", "kappa"), ("kappa", "kappa", "kappa"), "kappa"),
+    ]
+    for key4, key3, wrt in pairs:
+        if wrt == "mu":
+            fd = (l3(mu + h, kappa)[key3] - l3(mu - h, kappa)[key3]) / (2 * h)
+        else:
+            fd = (l3(mu, kappa + h)[key3] - l3(mu, kappa - h)[key3]) / (2 * h)
+        np.testing.assert_allclose(fourth[key4], fd, atol=2e-5, err_msg=str(key4))
+
+
+# ---------------------------------------------------------------------------
+# Phase 1 regression contract: wrapped Cauchy (second Tier-1 workhorse)
+# ---------------------------------------------------------------------------
+
+
+def test_wrapcauchy_regression_overlay():
+    """Role overlay: ρ is a (0,1)-bounded concentration, so it gets logit."""
+    assert isinstance(wrapcauchy, _RegressionReady)
+    assert wrapcauchy.param_roles == {"mu": "location", "rho": "concentration"}
+    assert wrapcauchy.link_for("mu") == "tanhalf"
+    assert wrapcauchy.link_for("rho") == "logit"
+    from hea.family import LogitLink as _HeaLogitLink
+
+    assert isinstance(get_link(wrapcauchy.link_for("rho")), _HeaLogitLink)
+
+
+@pytest.mark.parametrize("rho", [0.1, 0.5, 0.9])
+@pytest.mark.parametrize("mu", [0.4, 2.5, 5.0])
+def test_wrapcauchy_dlogpdf_matches_finite_difference(mu, rho):
+    """l1 against central differences of logpdf."""
+    x = np.linspace(0.1, 2 * np.pi - 0.1, 23)
+    grad = wrapcauchy.dlogpdf(x, mu, rho)
+    h = 1e-6
+    d_mu = (wrapcauchy.logpdf(x, mu + h, rho) - wrapcauchy.logpdf(x, mu - h, rho)) / (
+        2 * h
+    )
+    d_rho = (wrapcauchy.logpdf(x, mu, rho + h) - wrapcauchy.logpdf(x, mu, rho - h)) / (
+        2 * h
+    )
+    np.testing.assert_allclose(grad["mu"], d_mu, rtol=1e-5, atol=1e-6)
+    np.testing.assert_allclose(grad["rho"], d_rho, rtol=1e-5, atol=1e-6)
+
+
+@pytest.mark.parametrize("rho", [0.1, 0.5, 0.9])
+@pytest.mark.parametrize("mu", [0.4, 2.5, 5.0])
+def test_wrapcauchy_d2logpdf_matches_finite_difference(mu, rho):
+    """l2 against central differences of l1 (both μ- and ρ-differencing for
+    the mixed entry, pinning symmetry)."""
+    x = np.linspace(0.1, 2 * np.pi - 0.1, 23)
+    hess = wrapcauchy.d2logpdf(x, mu, rho)
+    h = 1e-6
+
+    def l1(m, r):
+        return wrapcauchy.dlogpdf(x, m, r)
+
+    d_mm = (l1(mu + h, rho)["mu"] - l1(mu - h, rho)["mu"]) / (2 * h)
+    d_mr = (l1(mu, rho + h)["mu"] - l1(mu, rho - h)["mu"]) / (2 * h)
+    d_rm = (l1(mu + h, rho)["rho"] - l1(mu - h, rho)["rho"]) / (2 * h)
+    d_rr = (l1(mu, rho + h)["rho"] - l1(mu, rho - h)["rho"]) / (2 * h)
+
+    np.testing.assert_allclose(hess[("mu", "mu")], d_mm, rtol=1e-5, atol=1e-6)
+    np.testing.assert_allclose(hess[("mu", "rho")], d_mr, rtol=1e-5, atol=1e-6)
+    np.testing.assert_allclose(hess[("mu", "rho")], d_rm, rtol=1e-5, atol=1e-6)
+    np.testing.assert_allclose(hess[("rho", "rho")], d_rr, rtol=1e-5, atol=1e-6)
+
+
+@pytest.mark.parametrize("rho", [0.1, 0.5, 0.9])
+@pytest.mark.parametrize("mu", [0.4, 2.5, 5.0])
+def test_wrapcauchy_d3_d4logpdf_match_finite_difference(mu, rho):
+    """l3 against FD of l2, l4 against FD of l3 — every unique key."""
+    x = np.linspace(0.1, 2 * np.pi - 0.1, 23)
+    h = 1e-5
+
+    third = wrapcauchy.d3logpdf(x, mu, rho)
+    fourth = wrapcauchy.d4logpdf(x, mu, rho)
+
+    def l2(m, r):
+        return wrapcauchy.d2logpdf(x, m, r)
+
+    def l3(m, r):
+        return wrapcauchy.d3logpdf(x, m, r)
+
+    chain3 = [
+        (("mu", "mu", "mu"), ("mu", "mu"), "mu"),
+        (("mu", "mu", "rho"), ("mu", "mu"), "rho"),
+        (("mu", "rho", "rho"), ("mu", "rho"), "rho"),
+        (("rho", "rho", "rho"), ("rho", "rho"), "rho"),
+    ]
+    for key3, key2, wrt in chain3:
+        if wrt == "mu":
+            fd = (l2(mu + h, rho)[key2] - l2(mu - h, rho)[key2]) / (2 * h)
+        else:
+            fd = (l2(mu, rho + h)[key2] - l2(mu, rho - h)[key2]) / (2 * h)
+        np.testing.assert_allclose(
+            third[key3], fd, rtol=1e-4, atol=1e-5, err_msg=str(key3)
+        )
+
+    chain4 = [
+        (("mu", "mu", "mu", "mu"), ("mu", "mu", "mu"), "mu"),
+        (("mu", "mu", "mu", "rho"), ("mu", "mu", "mu"), "rho"),
+        (("mu", "mu", "rho", "rho"), ("mu", "mu", "rho"), "rho"),
+        (("mu", "rho", "rho", "rho"), ("mu", "rho", "rho"), "rho"),
+        (("rho", "rho", "rho", "rho"), ("rho", "rho", "rho"), "rho"),
+    ]
+    for key4, key3, wrt in chain4:
+        if wrt == "mu":
+            fd = (l3(mu + h, rho)[key3] - l3(mu - h, rho)[key3]) / (2 * h)
+        else:
+            fd = (l3(mu, rho + h)[key3] - l3(mu, rho - h)[key3]) / (2 * h)
+        np.testing.assert_allclose(
+            fourth[key4], fd, rtol=1e-4, atol=1e-4, err_msg=str(key4)
+        )
+
+
+def test_wrapcauchy_derivatives_vectorize_over_per_obs_params():
+    """logpdf and l1..l4 broadcast over per-observation μ, ρ arrays."""
+    rng = np.random.default_rng(0)
+    n = 50
+    x = rng.uniform(0, 2 * np.pi, n)
+    mu = rng.uniform(0, 2 * np.pi, n)
+    rho = rng.uniform(0.05, 0.95, n)
+
+    assert wrapcauchy.logpdf(x, mu, rho).shape == (n,)
+    grad = wrapcauchy.dlogpdf(x, mu, rho)
+    assert grad["mu"].shape == (n,) and grad["rho"].shape == (n,)
+    assert all(v.shape == (n,) for v in wrapcauchy.d2logpdf(x, mu, rho).values())
+    assert all(v.shape == (n,) for v in wrapcauchy.d3logpdf(x, mu, rho).values())
+    assert all(v.shape == (n,) for v in wrapcauchy.d4logpdf(x, mu, rho).values())
+    # per-obs result equals the scalar call element-by-element
+    for i in (0, 17, 49):
+        gi = wrapcauchy.dlogpdf(x[i], mu[i], rho[i])
+        assert gi["mu"] == pytest.approx(grad["mu"][i])
+        assert gi["rho"] == pytest.approx(grad["rho"][i])
+
+
+# ---------------------------------------------------------------------------
+# Phase 1 regression contract: per-obs param vectorization (Tier-1/2 audit)
+# ---------------------------------------------------------------------------
+
+
+def test_contract_families_logpdf_vectorizes_over_per_obs_params():
+    """Tier-1/2 contract families accept per-observation parameter arrays in
+    ``logpdf`` — the requirement for smoothing a concentration/shape over X.
+    The per-obs call must match element-wise scalar calls (jonespewsey and
+    the sine-skewed extension route through the vectorized Legendre
+    normalizer; the others broadcast natively)."""
+    rng = np.random.default_rng(3)
+    n = 11
+    x = rng.uniform(0.1, 2 * np.pi - 0.1, n)
+    mu = rng.uniform(0.5, 5.5, n)
+    from pycircstat2.distributions import projectednormal as _pn
+
+    cases = [
+        (vonmises, dict(mu=mu, kappa=rng.uniform(0.3, 8.0, n))),
+        (wrapcauchy, dict(mu=mu, rho=rng.uniform(0.05, 0.9, n))),
+        (_pn, dict(mu1=rng.uniform(-2.0, 2.0, n), mu2=rng.uniform(-2.0, 2.0, n))),
+        (cartwright, dict(mu=mu, zeta=rng.uniform(0.1, 2.0, n))),
+        (
+            jonespewsey,
+            dict(mu=mu, kappa=rng.uniform(0.3, 5.0, n), psi=rng.uniform(-1.5, 1.5, n)),
+        ),
+        (
+            jonespewsey_sineskewed,
+            dict(
+                xi=mu,
+                kappa=rng.uniform(0.3, 5.0, n),
+                psi=rng.uniform(-1.5, 1.5, n),
+                lmbd=rng.uniform(-0.8, 0.8, n),
+            ),
+        ),
+        (
+            katojones,
+            dict(
+                mu=mu,
+                gamma=rng.uniform(0.05, 0.3, n),
+                rho=rng.uniform(0.05, 0.3, n),
+                lam=rng.uniform(0.0, 1.0, n),
+            ),
+        ),
+    ]
+    for dist, params in cases:
+        vec = dist.logpdf(x, **params)
+        assert np.shape(vec) == (n,), f"{dist.name}: shape {np.shape(vec)}"
+        assert np.all(np.isfinite(vec)), f"{dist.name}: non-finite logpdf"
+        for i in (0, n // 2, n - 1):
+            scal = dist.logpdf(x[i], **{k: v[i] for k, v in params.items()})
+            np.testing.assert_allclose(
+                vec[i], scal, rtol=1e-9, atol=1e-12, err_msg=dist.name
+            )
+
+
+# ---------------------------------------------------------------------------
+# Projected normal (Tier-1 workhorse, added by the Phase 1 contract)
+# ---------------------------------------------------------------------------
+
+from pycircstat2.distributions import projectednormal  # noqa: E402
+
+
+def test_projectednormal_pdf_normalizes_and_uniform_limit():
+    """∫f = 1 across concentration regimes; ‖μ‖ = 0 is the circular uniform;
+    logpdf is the exact log of pdf."""
+    grid = np.linspace(0.0, 2.0 * np.pi, 20001)
+    for m in [(0.0, 0.0), (1.5, 0.8), (4.0, -3.0), (0.05, 0.0)]:
+        f = projectednormal.pdf(grid, *m)
+        assert np.trapezoid(f, grid) == pytest.approx(1.0, abs=1e-8)
+    assert projectednormal.pdf(1.0, 0.0, 0.0) == pytest.approx(1.0 / (2.0 * np.pi))
+    x = np.linspace(0.1, 2 * np.pi - 0.1, 17)
+    np.testing.assert_allclose(
+        projectednormal.logpdf(x, 1.5, 0.8),
+        np.log(projectednormal.pdf(x, 1.5, 0.8)),
+        atol=1e-12,
+    )
+
+
+def test_projectednormal_regression_overlay():
+    """Both Cartesian components share the 'location' role (the documented
+    one-to-many case) and resolve to hea's identity link."""
+    from hea.family import IdentityLink as _HeaIdentityLink
+
+    assert isinstance(projectednormal, _RegressionReady)
+    assert projectednormal.param_roles == {"mu1": "location", "mu2": "location"}
+    assert projectednormal.params_by_role() == {"location": ["mu1", "mu2"]}
+    assert projectednormal.link_for("mu1") == "identity"
+    assert isinstance(
+        get_link(projectednormal.link_for("mu2")), _HeaIdentityLink
+    )
+
+
+@pytest.mark.parametrize("mu1,mu2", [(0.3, 0.2), (1.5, 0.8), (-1.2, 0.7), (4.0, -3.0)])
+def test_projectednormal_derivatives_match_finite_difference(mu1, mu2):
+    """The full l1..l4 chain against central differences of the order below
+    (the contract convention), including a high-concentration case where t
+    is strongly negative at the antimode (stresses the Mills-ratio path)."""
+    x = np.linspace(0.1, 2 * np.pi - 0.1, 23)
+    h = 1e-6
+
+    def fd(f, key, wrt):
+        if wrt == "mu1":
+            hi, lo = f(x, mu1 + h, mu2), f(x, mu1 - h, mu2)
+        else:
+            hi, lo = f(x, mu1, mu2 + h), f(x, mu1, mu2 - h)
+        hi = hi if key is None else hi[key]
+        lo = lo if key is None else lo[key]
+        return (hi - lo) / (2 * h)
+
+    grad = projectednormal.dlogpdf(x, mu1, mu2)
+    np.testing.assert_allclose(grad["mu1"], fd(projectednormal.logpdf, None, "mu1"), atol=1e-6)
+    np.testing.assert_allclose(grad["mu2"], fd(projectednormal.logpdf, None, "mu2"), atol=1e-6)
+
+    hess = projectednormal.d2logpdf(x, mu1, mu2)
+    np.testing.assert_allclose(hess[("mu1", "mu1")], fd(projectednormal.dlogpdf, "mu1", "mu1"), atol=1e-5)
+    np.testing.assert_allclose(hess[("mu1", "mu2")], fd(projectednormal.dlogpdf, "mu1", "mu2"), atol=1e-5)
+    np.testing.assert_allclose(hess[("mu1", "mu2")], fd(projectednormal.dlogpdf, "mu2", "mu1"), atol=1e-5)
+    np.testing.assert_allclose(hess[("mu2", "mu2")], fd(projectednormal.dlogpdf, "mu2", "mu2"), atol=1e-5)
+
+    third = projectednormal.d3logpdf(x, mu1, mu2)
+    chain3 = [
+        (("mu1", "mu1", "mu1"), ("mu1", "mu1"), "mu1"),
+        (("mu1", "mu1", "mu2"), ("mu1", "mu1"), "mu2"),
+        (("mu1", "mu2", "mu2"), ("mu1", "mu2"), "mu2"),
+        (("mu2", "mu2", "mu2"), ("mu2", "mu2"), "mu2"),
+    ]
+    for key3, key2, wrt in chain3:
+        np.testing.assert_allclose(
+            third[key3], fd(projectednormal.d2logpdf, key2, wrt),
+            atol=1e-5, err_msg=str(key3),
+        )
+
+    fourth = projectednormal.d4logpdf(x, mu1, mu2)
+    chain4 = [
+        (("mu1", "mu1", "mu1", "mu1"), ("mu1", "mu1", "mu1"), "mu1"),
+        (("mu1", "mu1", "mu1", "mu2"), ("mu1", "mu1", "mu1"), "mu2"),
+        (("mu1", "mu1", "mu2", "mu2"), ("mu1", "mu1", "mu2"), "mu2"),
+        (("mu1", "mu2", "mu2", "mu2"), ("mu1", "mu2", "mu2"), "mu2"),
+        (("mu2", "mu2", "mu2", "mu2"), ("mu2", "mu2", "mu2"), "mu2"),
+    ]
+    for key4, key3, wrt in chain4:
+        np.testing.assert_allclose(
+            fourth[key4], fd(projectednormal.d3logpdf, key3, wrt),
+            atol=2e-5, err_msg=str(key4),
+        )
+
+
+def test_projectednormal_derivatives_vectorize_over_per_obs_params():
+    """logpdf and l1..l4 broadcast over per-observation (μ₁, μ₂) arrays —
+    the two-linear-predictor regression requirement."""
+    rng = np.random.default_rng(0)
+    n = 50
+    x = rng.uniform(0, 2 * np.pi, n)
+    mu1 = rng.uniform(-2.5, 2.5, n)
+    mu2 = rng.uniform(-2.5, 2.5, n)
+
+    assert projectednormal.logpdf(x, mu1, mu2).shape == (n,)
+    grad = projectednormal.dlogpdf(x, mu1, mu2)
+    assert grad["mu1"].shape == (n,) and grad["mu2"].shape == (n,)
+    assert all(v.shape == (n,) for v in projectednormal.d2logpdf(x, mu1, mu2).values())
+    assert all(v.shape == (n,) for v in projectednormal.d3logpdf(x, mu1, mu2).values())
+    assert all(v.shape == (n,) for v in projectednormal.d4logpdf(x, mu1, mu2).values())
+    for i in (0, 17, 49):
+        gi = projectednormal.dlogpdf(x[i], mu1[i], mu2[i])
+        assert gi["mu1"] == pytest.approx(grad["mu1"][i])
+        assert gi["mu2"] == pytest.approx(grad["mu2"][i])
+
+
+def test_projectednormal_rvs_matches_density():
+    """Sampling by direct projection agrees with the closed-form density:
+    the empirical CDF tracks the numeric CDF and the sample mean direction
+    recovers atan2(μ₂, μ₁)."""
+    mu1, mu2 = 1.5, 0.8
+    s = projectednormal.rvs(mu1, mu2, size=20000, random_state=42)
+    assert np.all((s >= 0.0) & (s < 2.0 * np.pi))
+    dir_hat = float(np.angle(np.mean(np.exp(1j * s))))
+    assert dir_hat == pytest.approx(np.arctan2(mu2, mu1), abs=0.03)
+    for q in (1.0, 2.5, 4.5):
+        emp = float(np.mean(s <= q))
+        assert emp == pytest.approx(
+            float(projectednormal.cdf(q, mu1, mu2)), abs=0.02
+        )
+
+
+def test_projectednormal_cdf_monotone_and_bounded():
+    grid = np.linspace(0.0, 2.0 * np.pi, 25)
+    cdf = np.array([float(projectednormal.cdf(v, 1.2, -0.6)) for v in grid])
+    assert cdf[0] == pytest.approx(0.0, abs=1e-9)
+    assert cdf[-1] == pytest.approx(1.0, abs=1e-7)
+    assert np.all(np.diff(cdf) >= -1e-10)
+
+
+def test_projectednormal_fit_recovers_truth():
+    mu1, mu2 = 1.2, -0.8
+    s = projectednormal.rvs(mu1, mu2, size=4000, random_state=7)
+    (m1, m2), info = projectednormal.fit(s, return_info=True)
+    assert info["converged"] is True
+    assert m1 == pytest.approx(mu1, abs=0.12)
+    assert m2 == pytest.approx(mu2, abs=0.12)
+    with pytest.raises(ValueError, match="method"):
+        projectednormal.fit(s, method="moments")
diff --git a/tests/test_hypothesis.py b/tests/test_hypothesis.py
index 513c704..7dbc4ba 100644
--- a/tests/test_hypothesis.py
+++ b/tests/test_hypothesis.py
@@ -1,6 +1,7 @@
 import warnings
 
 import numpy as np
+import polars as pl
 import pytest
 
 from pycircstat2 import Circular, load_data
@@ -38,7 +39,7 @@ def test_rayleigh_test():
     # Ch27 Example 1 (Zar, 2010, P667)
     # Using data from Ch26 Example 2.
     data_zar_ex2_ch26 = load_data("D1", source="zar")
-    circ_zar_ex1_ch27 = Circular(data=data_zar_ex2_ch26["θ"].values[:])
+    circ_zar_ex1_ch27 = Circular(data=data_zar_ex2_ch26["θ"].to_numpy())
 
     # computed directly from r and n
     result = rayleigh_test(n=circ_zar_ex1_ch27.n, r=circ_zar_ex1_ch27.r)
@@ -54,7 +55,7 @@ def test_rayleigh_test():
 def test_V_test():
     # Ch27 Example 2 (Zar, 2010, P669)
     data_zar_ex2_ch27 = load_data("D7", source="zar")
-    circ_zar_ex2_ch27 = Circular(data=data_zar_ex2_ch27["θ"].values[:])
+    circ_zar_ex2_ch27 = Circular(data=data_zar_ex2_ch27["θ"].to_numpy())
 
     # computed directly from r and n
     result = V_test(
@@ -83,7 +84,7 @@ def test_one_sample_test():
     # Ch27 Example 3 (Zar, 2010, P669)
     # Using data from Ch27 Example 2
     data_zar_ex2_ch27 = load_data("D7", source="zar")
-    circ_zar_ex3_ch27 = Circular(data=data_zar_ex2_ch27["θ"].values[:], unit="degree")
+    circ_zar_ex3_ch27 = Circular(data=data_zar_ex2_ch27["θ"].to_numpy(), unit="degree")
 
     # computed directly from lb and ub
     result = one_sample_test(
@@ -102,7 +103,7 @@ def test_one_sample_test():
 
 def test_omnibus_test():
     data_zar_ex4_ch27 = load_data("D8", source="zar")
-    circ_zar_ex4_ch27 = Circular(data=data_zar_ex4_ch27["θ"].values[:], unit="degree")
+    circ_zar_ex4_ch27 = Circular(data=data_zar_ex4_ch27["θ"].to_numpy(), unit="degree")
 
     result = omnibus_test(alpha=circ_zar_ex4_ch27.alpha, scale=1)
 
@@ -127,7 +128,7 @@ def test_omnibus_test():
 
 def test_batschelet_test():
     data_zar_ex5_ch27 = load_data("D8", source="zar")
-    circ_zar_ex5_ch27 = Circular(data=data_zar_ex5_ch27["θ"].values[:], unit="degree")
+    circ_zar_ex5_ch27 = Circular(data=data_zar_ex5_ch27["θ"].to_numpy(), unit="degree")
 
     result = batschelet_test(
         angle=np.deg2rad(45),
@@ -139,7 +140,7 @@ def test_batschelet_test():
 
 def test_chisquare_test():
     d2 = load_data("D2", source="zar")
-    c2 = Circular(data=d2["θ"].values[:], w=d2["w"].values[:])
+    c2 = Circular(data=d2["θ"].to_numpy(), w=d2["w"].to_numpy())
 
     result = chisquare_test(c2.w)
     np.testing.assert_approx_equal(result.chi2, 66.543, significant=3)
@@ -148,7 +149,7 @@ def test_chisquare_test():
 
 def test_symmetry_test():
     data_zar_ex6_ch27 = load_data("D9", source="zar")
-    circ_zar_ex6_ch27 = Circular(data=data_zar_ex6_ch27["θ"].values[:], unit="degree")
+    circ_zar_ex6_ch27 = Circular(data=data_zar_ex6_ch27["θ"].to_numpy(), unit="degree")
 
     result = symmetry_test(
         median=float(circ_zar_ex6_ch27.median), alpha=circ_zar_ex6_ch27.alpha
@@ -158,8 +159,8 @@ def test_symmetry_test():
 
 def test_watson_williams_test():
     data = load_data("D10", source="zar")
-    s1 = Circular(data=data[data["sample"] == 1]["θ"].values[:])
-    s2 = Circular(data=data[data["sample"] == 2]["θ"].values[:])
+    s1 = Circular(data=data.filter(pl.col("sample") == 1)["θ"].to_numpy())
+    s2 = Circular(data=data.filter(pl.col("sample") == 2)["θ"].to_numpy())
     result = watson_williams_test([s1, s2])
 
     np.testing.assert_approx_equal(result.F, 1.61, significant=3)
@@ -171,9 +172,9 @@ def test_watson_williams_test():
     np.testing.assert_allclose(array_result.pval, result.pval, rtol=1e-6)
 
     data = load_data("D11", source="zar")
-    s1 = Circular(data=data[data["sample"] == 1]["θ"].values[:])
-    s2 = Circular(data=data[data["sample"] == 2]["θ"].values[:])
-    s3 = Circular(data=data[data["sample"] == 3]["θ"].values[:])
+    s1 = Circular(data=data.filter(pl.col("sample") == 1)["θ"].to_numpy())
+    s2 = Circular(data=data.filter(pl.col("sample") == 2)["θ"].to_numpy())
+    s3 = Circular(data=data.filter(pl.col("sample") == 3)["θ"].to_numpy())
 
     result = watson_williams_test([s1, s2, s3])
 
@@ -183,8 +184,8 @@ def test_watson_williams_test():
 
 def test_watson_u2_test():
     d = load_data("D12", source="zar")
-    c0 = Circular(data=d[d["sample"] == 1]["θ"].values[:])
-    c1 = Circular(data=d[d["sample"] == 2]["θ"].values[:])
+    c0 = Circular(data=d.filter(pl.col("sample") == 1)["θ"].to_numpy())
+    c1 = Circular(data=d.filter(pl.col("sample") == 2)["θ"].to_numpy())
     result = watson_u2_test([c0, c1])
 
     np.testing.assert_approx_equal(result.U2, 0.1458, significant=3)
@@ -197,10 +198,10 @@ def test_watson_u2_test():
 
     d = load_data("D13", source="zar")
     c0 = Circular(
-        data=d[d["sample"] == 1]["θ"].values[:], w=d[d["sample"] == 1]["w"].values[:]
+        data=d.filter(pl.col("sample") == 1)["θ"].to_numpy(), w=d.filter(pl.col("sample") == 1)["w"].to_numpy()
     )
     c1 = Circular(
-        data=d[d["sample"] == 2]["θ"].values[:], w=d[d["sample"] == 2]["w"].values[:]
+        data=d.filter(pl.col("sample") == 2)["θ"].to_numpy(), w=d.filter(pl.col("sample") == 2)["w"].to_numpy()
     )
     result = watson_u2_test([c0, c1])
 
@@ -239,8 +240,8 @@ def test_kuiper_two_test():
 
     # grouped data expand consistently with raw angles
     d = load_data("D12", source="zar")
-    c0 = Circular(data=d[d["sample"] == 1]["θ"].values[:])
-    c1 = Circular(data=d[d["sample"] == 2]["θ"].values[:])
+    c0 = Circular(data=d.filter(pl.col("sample") == 1)["θ"].to_numpy())
+    c1 = Circular(data=d.filter(pl.col("sample") == 2)["θ"].to_numpy())
     np.testing.assert_allclose(
         kuiper_two_test([c0, c1]).V, kuiper_two_test([c0.alpha, c1.alpha]).V, rtol=1e-9
     )
@@ -254,8 +255,8 @@ def test_kuiper_two_test():
 
 def test_wheeler_watson_test():
     d = load_data("D12", source="zar")
-    c0 = Circular(data=d[d["sample"] == 1]["θ"].values[:])
-    c1 = Circular(data=d[d["sample"] == 2]["θ"].values[:])
+    c0 = Circular(data=d.filter(pl.col("sample") == 1)["θ"].to_numpy())
+    c1 = Circular(data=d.filter(pl.col("sample") == 2)["θ"].to_numpy())
 
     result = wheeler_watson_test([c0, c1])
     np.testing.assert_approx_equal(result.W, 3.678, significant=3)
@@ -268,8 +269,8 @@ def test_wheeler_watson_test():
 
 def test_wallraff_test():
     d = load_data("D14", source="zar")
-    c0 = Circular(data=d[d["sex"] == "male"]["θ"].values[:])
-    c1 = Circular(data=d[d["sex"] == "female"]["θ"].values[:])
+    c0 = Circular(data=d.filter(pl.col("sex") == "male")["θ"].to_numpy())
+    c1 = Circular(data=d.filter(pl.col("sex") == "female")["θ"].to_numpy())
     result = wallraff_test(samples=[c0, c1], angle=np.deg2rad(135))
     np.testing.assert_approx_equal(result.U, 18.5, significant=3)
     assert result.pval > 0.20
@@ -281,8 +282,8 @@ def test_wallraff_test():
     from pycircstat2.utils import time2float
 
     d = load_data("D15", source="zar")
-    c0 = Circular(data=time2float(d[d["sex"] == "male"]["time"].values[:]))
-    c1 = Circular(data=time2float(d[d["sex"] == "female"]["time"].values[:]))
+    c0 = Circular(data=time2float(d.filter(pl.col("sex") == "male")["time"].to_numpy()))
+    c1 = Circular(data=time2float(d.filter(pl.col("sex") == "female")["time"].to_numpy()))
     result = wallraff_test(
         angle=np.deg2rad(time2float(["7:55", "8:15"])),
         samples=[c0, c1],
@@ -293,7 +294,7 @@ def test_wallraff_test():
 
 
 def test_kuiper_test():
-    d = load_data("B5", source="fisher")["θ"].values[:]
+    d = load_data("B5", source="fisher")["θ"].to_numpy()
     c = Circular(data=d, unit="degree", full_cycle=180)
     result = kuiper_test(alpha=c.alpha)
     np.testing.assert_approx_equal(result.V, 1.5864, significant=3)
@@ -695,7 +696,7 @@ def hktest(alpha, idp, idq, inter=True, fn=None):
         q = len(np.unique(idq))
         df = pd.DataFrame({fn[0]: idp, fn[1]: idq, "dependent": alpha})
         n = len(df)
-        tr = n * circ_r(np.asarray(df["dependent"].values))
+        tr = n * circ_r(np.asarray(df["dependent"].to_numpy()))
         kk = circ_kappa(tr / n)
 
         # both factors
@@ -730,19 +731,19 @@ def hktest(alpha, idp, idq, inter=True, fn=None):
             # total effect
             eff_t = n - tr**2 / n
             df_t = n - 1
-            m = cn.values[:].mean()
+            m = cn.to_numpy().mean()
 
             if inter:
                 # correction factor for improved F statistic
                 beta = 1 / (1 - 1 / (5 * kk) - 1 / (10 * (kk**2)))
                 # residual effects
-                eff_r = n - (cr**2.0 / cn).values[:].sum()
+                eff_r = n - (cr**2.0 / cn).to_numpy().sum()
                 df_r = p * q * (m - 1)
                 ms_r = eff_r / df_r
 
                 # interaction effects
                 eff_i = (
-                    (cr**2.0 / cn).values[:].sum()
+                    (cr**2.0 / cn).to_numpy().sum()
                     - sum(qr**2.0 / qn)
                     - sum(pr**2.0 / pn)
                     + tr**2 / n
@@ -789,7 +790,7 @@ def hktest(alpha, idp, idq, inter=True, fn=None):
             p2 = 1 - stats.chi2.cdf(chi2, df=df_2)
 
             chiI = f * (
-                (cr**2.0 / cn).values[:].sum()
+                (cr**2.0 / cn).to_numpy().sum()
                 - sum(pr**2.0 / pn)
                 - sum(qr**2.0 / qn)
                 + tr**2 / n
@@ -843,7 +844,7 @@ def hktest(alpha, idp, idq, inter=True, fn=None):
 
     # Compare ANOVA table values (ignoring index differences)
     table_orig_values = table_orig.to_numpy()
-    table_new_values = table_new.to_numpy()
+    table_new_values = table_new.drop("Source").to_numpy()
 
     assert np.allclose(
         table_orig_values, table_new_values, atol=1e-6, equal_nan=True
@@ -1078,7 +1079,7 @@ def test_wheeler_watson_three_samples():
 def test_kuiper_test_asymptotic():
     """Asymptotic mode (n_resamples=0) returns a valid p-value close to the
     Monte-Carlo one."""
-    d = load_data("B5", source="fisher")["θ"].values[:]
+    d = load_data("B5", source="fisher")["θ"].to_numpy()
     c = Circular(data=d, unit="degree", full_cycle=180)
     asymp = kuiper_test(alpha=c.alpha, n_resamples=0)
     sim = kuiper_test(alpha=c.alpha, n_resamples=9999)
@@ -1118,9 +1119,9 @@ def test_harrison_kanji_inter_false():
     assert 0.0 <= p_a <= 1.0 and 0.0 <= p_b <= 1.0
 
     table = result.anova_table
-    assert list(table.index) == ["A", "B", "Interaction", "Residual", "Total"]
+    assert table["Source"].to_list() == ["A", "B", "Interaction", "Residual", "Total"]
     p, q = len(np.unique(idp)), len(np.unique(idq))
-    assert table.loc["Residual", "DoF"] == (p - 1) * (q - 1)
+    assert table.filter(pl.col("Source") == "Residual")["DoF"].item() == (p - 1) * (q - 1)
 
 
 def test_one_sample_test_rejects_distant_angle():
@@ -1156,7 +1157,7 @@ def test_symmetry_test_default_median():
     from pycircstat2.descriptive import circ_median
 
     data_zar_ex6_ch27 = load_data("D9", source="zar")
-    alpha = Circular(data=data_zar_ex6_ch27["θ"].values[:], unit="degree").alpha
+    alpha = Circular(data=data_zar_ex6_ch27["θ"].to_numpy(), unit="degree").alpha
     auto = symmetry_test(alpha)
     explicit = symmetry_test(alpha, median=float(circ_median(alpha)))
     np.testing.assert_allclose(auto.statistic, explicit.statistic, rtol=1e-9)
@@ -1270,7 +1271,7 @@ def test_common_median_randomization():
     """common_median_test randomization reproduces the book's ant-data result and
     the χ² asymptotic value (Pewsey et al. 2013, §7.3.2; data = B10)."""
     df = load_data("B10", source="fisher")  # desert-ant directions, 3 groups
-    groups = [np.deg2rad(df[df["set"] == s]["θ"].values.astype(float)) for s in (1, 2, 3)]
+    groups = [np.deg2rad(df.filter(pl.col("set") == s)["θ"].to_numpy().astype(float)) for s in (1, 2, 3)]
 
     asy = common_median_test(groups)
     assert asy.method == "asymptotic"
@@ -1290,8 +1291,8 @@ def test_watson_u2_randomization():
     """watson_u2_test randomization reproduces the book's ant-data result
     (control vs 2nd treatment; Pewsey et al. 2013, §7.5.5; data = B10)."""
     df = load_data("B10", source="fisher")
-    s1 = np.deg2rad(df[df["set"] == 1]["θ"].values.astype(float))
-    s3 = np.deg2rad(df[df["set"] == 3]["θ"].values.astype(float))
+    s1 = np.deg2rad(df.filter(pl.col("set") == 1)["θ"].to_numpy().astype(float))
+    s3 = np.deg2rad(df.filter(pl.col("set") == 3)["θ"].to_numpy().astype(float))
 
     rnd = watson_u2_test([s1, s3], n_resamples=9999, seed=1)
     assert rnd.method == "randomization" and rnd.n_resamples == 9999
@@ -1308,7 +1309,7 @@ def test_wheeler_watson_randomization_with_ties():
     """wheeler_watson_test now handles tied data via midranks, and its randomization
     p-value tracks the χ² approximation (Pewsey et al. 2013, §7.5.3; data = B10)."""
     df = load_data("B10", source="fisher")  # ant data, 3 groups, contains ties
-    groups = [np.deg2rad(df[df["set"] == s]["θ"].values.astype(float)) for s in (1, 2, 3)]
+    groups = [np.deg2rad(df.filter(pl.col("set") == s)["θ"].to_numpy().astype(float)) for s in (1, 2, 3)]
 
     asy = wheeler_watson_test(groups)  # previously crashed on ties
     rnd = wheeler_watson_test(groups, n_resamples=9999, seed=1)
@@ -1359,7 +1360,7 @@ def test_symmetry_test_pewsey():
     matches R's `circular` package and the bootstrap reproduces the book's cross-bed
     azimuth result (Pewsey et al. 2013, §5.2; data = B6/set1)."""
     b6 = load_data("B6", source="fisher")
-    s1 = np.deg2rad(b6[b6["set"] == 1]["θ"].values.astype(float))
+    s1 = np.deg2rad(b6.filter(pl.col("set") == 1)["θ"].to_numpy().astype(float))
 
     large = symmetry_test(s1, method="pewsey")
     assert large.method == "pewsey"
@@ -1386,7 +1387,7 @@ def test_one_sample_specified_mean():
 
     # B1 intensive-care times with the proper hh:mm -> decimal-hour conversion (== R's
     # fisherB1c). NB: the book's published 9.126e-5 used raw fisherB1 (8.45-as-decimal).
-    b1 = time2float(load_data("B1", source="fisher")["time"].values) * 2 * np.pi / 24
+    b1 = time2float(load_data("B1", source="fisher")["time"].to_numpy()) * 2 * np.pi / 24
 
     r = one_sample_test(angle=3.9270, alpha=b1, symmetric=True)  # H0: mean = 15:00
     assert r.method == "asymptotic"
@@ -1439,7 +1440,7 @@ def test_mc_uniform_pvalues():
     assert v_a.method == "asymptotic" and v_m.method == "monte_carlo" and v_m.n_resamples == 9999
     assert abs(v_a.pval - v_m.pval) < 0.02
 
-    d8 = Circular(data=load_data("D8", source="zar")["θ"].values[:], unit="degree")
+    d8 = Circular(data=load_data("D8", source="zar")["θ"].to_numpy(), unit="degree")
     o_m = omnibus_test(d8.alpha, n_resamples=9999, seed=1)
     assert o_m.method == "monte_carlo" and o_m.pval < 0.05  # book/asymptotic ~0.0043
     # MC handles the degenerate (maximally uniform) case the analytic formula clamps.
diff --git a/tests/test_regression.py b/tests/test_regression.py
index ae1267a..7f15d57 100644
--- a/tests/test_regression.py
+++ b/tests/test_regression.py
@@ -1,5 +1,6 @@
 import numpy as np
 import pandas as pd
+import polars as pl
 import pytest
 
 from pycircstat2 import load_data
@@ -7,14 +8,13 @@
 from pycircstat2.utils import A1inv
 
 
-def _lung_dataframe(drop_feb_outliers: bool = True) -> pd.DataFrame:
-    """Pewsey, Neuhäuser & Ruxton (2014) §8.4.1 lung-disease deaths."""
-    df = load_data("lung_deaths", source="pewsey").copy()
-    df["theta"] = (np.pi / 6) * df["month"].to_numpy()
-    df = df.rename(columns={"deaths": "y"})
+def _lung_dataframe(drop_feb_outliers: bool = True) -> "pl.DataFrame":
+    """Pewsey, Neuhäuser & Ruxton (2013) §8.4.1 lung-disease deaths."""
+    df = load_data("lung_deaths", source="pewsey")
+    df = df.with_columns(((np.pi / 6) * pl.col("month")).alias("theta"))
+    df = df.rename({"deaths": "y"})
     if drop_feb_outliers:
-        df = df[~((df["month"] == 2) & df["year"].isin([1976, 1979]))]
-        df = df.reset_index(drop=True)
+        df = df.filter(~((pl.col("month") == 2) & pl.col("year").is_in([1976, 1979])))
     return df
 
 
@@ -23,8 +23,8 @@ def test_cc_regression_against_r():
         "milwaukee",
         source="jammalamadaka",
     )
-    ctheta = np.deg2rad(df["theta"].values)
-    cpsi = np.deg2rad(df["psi"].values)
+    ctheta = np.deg2rad(df["theta"].to_numpy())
+    cpsi = np.deg2rad(df["psi"].to_numpy())
 
     # Expected results from R for order=2
     expected_order2 = {
@@ -103,9 +103,10 @@ def test_cc_regression_against_r():
         model_order4.result["p_values"], expected_order4["p_values"], atol=1e-4
     )
 
-    df_rad = df.copy()
-    df_rad["theta"] = ctheta
-    df_rad["psi"] = cpsi
+    df_rad = df.with_columns(
+        pl.Series("theta", ctheta),
+        pl.Series("psi", cpsi),
+    )
 
     # Test formula parsing for order=2
     formula_model = CCRegression(formula="theta ~ psi", data=df_rad, order=2)
@@ -131,8 +132,8 @@ def test_cl_regression_against_r():
     # Load dataset
     df = load_data("B20", source="fisher")
 
-    X = df["x"].values
-    θ = np.deg2rad(df["θ"].values)
+    X = df["x"].to_numpy()
+    θ = np.deg2rad(df["θ"].to_numpy())
 
     data_cl = pd.DataFrame({"X": X, "θ": θ})
 
@@ -267,8 +268,8 @@ def test_cc_regression_rejects_oversize_order():
 
 def test_cc_regression_exposes_residual_kappa():
     df = load_data("milwaukee", source="jammalamadaka")
-    ctheta = np.deg2rad(df["theta"].values)
-    cpsi = np.deg2rad(df["psi"].values)
+    ctheta = np.deg2rad(df["theta"].to_numpy())
+    cpsi = np.deg2rad(df["psi"].to_numpy())
     m = CCRegression(theta=ctheta, x=cpsi, order=2)
     assert "kappa" in m.result and "A_k" in m.result
     assert np.isfinite(m.result["kappa"]) and m.result["kappa"] >= 0
@@ -296,11 +297,13 @@ def test_log_likelihood_stable_at_high_concentration():
 
 
 def test_formula_parser_rejects_malformed_formulas():
+    # The single-string formula parser is the fisher-lee path (the default
+    # gam backend routes single strings through hea's own parser).
     df = pd.DataFrame({"y": [0.1, 0.2], "x": [1.0, 2.0]})
     with pytest.raises(ValueError, match="exactly one '~'"):
-        CLRegression(formula="y ~ x ~ z", data=df)
+        CLRegression(formula="y ~ x ~ z", data=df, backend="fisher-lee")
     with pytest.raises(ValueError, match="No predictors"):
-        CLRegression(formula="y ~ ", data=df)
+        CLRegression(formula="y ~ ", data=df, backend="fisher-lee")
 
 
 def test_cl_plot_mean_model_1d():
@@ -308,7 +311,7 @@ def test_cl_plot_mean_model_1d():
 
     matplotlib.use("Agg")
     df = load_data("B20", source="fisher")
-    data = pd.DataFrame({"X": df["x"].values, "θ": np.deg2rad(df["θ"].values)})
+    data = pd.DataFrame({"X": df["x"].to_numpy(), "θ": np.deg2rad(df["θ"].to_numpy())})
     m = CLRegression(formula="θ ~ X", data=data, model_type="mean")
     fig = m.plot()
     titles = [ax.get_title() for ax in fig.axes]
@@ -324,7 +327,7 @@ def test_cl_plot_kappa_only_shows_kappa_curve():
 
     matplotlib.use("Agg")
     df = load_data("B20", source="fisher")
-    data = pd.DataFrame({"X": df["x"].values, "θ": np.deg2rad(df["θ"].values)})
+    data = pd.DataFrame({"X": df["x"].to_numpy(), "θ": np.deg2rad(df["θ"].to_numpy())})
     m = CLRegression(formula="θ ~ X", data=data, model_type="kappa")
     fig = m.plot()
     titles = [ax.get_title() for ax in fig.axes]
@@ -408,8 +411,8 @@ def test_cl_plot_multi_feature_fallback():
 
 def test_cc_predict_round_trip_on_training_data():
     df = load_data("milwaukee", source="jammalamadaka")
-    ctheta = np.deg2rad(df["theta"].values)
-    cpsi = np.deg2rad(df["psi"].values)
+    ctheta = np.deg2rad(df["theta"].to_numpy())
+    cpsi = np.deg2rad(df["psi"].to_numpy())
     m = CCRegression(theta=ctheta, x=cpsi, order=2)
     pred = m.predict(cpsi)
     diff = np.angle(np.exp(1j * (pred - m.result["fitted"])))
@@ -431,8 +434,8 @@ def test_cc_plot_single_feature_two_panels():
 
     matplotlib.use("Agg")
     df = load_data("milwaukee", source="jammalamadaka")
-    ctheta = np.deg2rad(df["theta"].values)
-    cpsi = np.deg2rad(df["psi"].values)
+    ctheta = np.deg2rad(df["theta"].to_numpy())
+    cpsi = np.deg2rad(df["psi"].to_numpy())
     m = CCRegression(theta=ctheta, x=cpsi, order=2)
     fig = m.plot()
     titles = [ax.get_title() for ax in fig.axes]
@@ -457,6 +460,69 @@ def test_cc_plot_multi_feature_fallback():
     titles = [ax.get_title() for ax in fig.axes]
     assert "Residuals vs fitted" in titles
     assert "Residual histogram" in titles
+    with pytest.raises(ValueError, match="single circular predictor"):
+        m.plot(polar=True)
+
+
+def test_cc_plot_band_toggle():
+    import matplotlib
+
+    matplotlib.use("Agg")
+    from matplotlib.collections import PolyCollection
+
+    df = load_data("milwaukee", source="jammalamadaka")
+    ctheta = np.deg2rad(df["theta"].to_numpy())
+    cpsi = np.deg2rad(df["psi"].to_numpy())
+    m = CCRegression(theta=ctheta, x=cpsi, order=1)
+
+    def overlay_ax(fig):
+        return next(ax for ax in fig.axes if ax.get_title() == "Fit overlay")
+
+    def n_bands(fig):
+        return sum(
+            isinstance(c, PolyCollection) for c in overlay_ax(fig).collections
+        )
+
+    # Band on by default (one polygon per visible 2π replica), removable.
+    assert n_bands(m.plot()) >= 2
+    assert n_bands(m.plot(band=False)) == 0
+
+    # The unwrapped curve is continuous: no NaN seam-breaks in any replica.
+    for line in overlay_ax(m.plot()).get_lines():
+        assert np.all(np.isfinite(line.get_ydata()))
+
+    # Band half-width is the circular SD implied by ρ̂: positive, ≤ π.
+    grid = np.linspace(0.0, 2 * np.pi, 50)
+    cos_fit, sin_fit = m._predict_embedding(grid[:, None])
+    rho = np.clip(np.hypot(cos_fit, sin_fit), 1e-9, 1.0)
+    sd = np.minimum(np.sqrt(-2.0 * np.log(rho)), np.pi)
+    assert np.all((sd > 0) & (sd <= np.pi))
+
+
+def test_cc_plot_polar_clock():
+    import matplotlib
+
+    matplotlib.use("Agg")
+    from matplotlib.quiver import Quiver
+
+    df = load_data("milwaukee", source="jammalamadaka")
+    ctheta = np.deg2rad(df["theta"].to_numpy())
+    cpsi = np.deg2rad(df["psi"].to_numpy())
+    for m in (
+        CCRegression(theta=ctheta, x=cpsi, order=1),
+        CCRegression(
+            "theta ~ s(psi, bs='cc')",
+            pl.DataFrame({"theta": ctheta, "psi": cpsi}),
+        ),
+    ):
+        fig = m.plot(polar=True)
+        clock = fig.axes[0]
+        assert "Fit clock" in clock.get_title()
+        assert not clock.axison  # compass frame, no cartesian axes
+        # Two quivers: fitted-direction arrows and data arrows.
+        assert sum(isinstance(c, Quiver) for c in clock.collections) == 2
+        # The residual panel is untouched by the polar switch.
+        assert fig.axes[1].get_title() == "Residuals vs predictor"
 
 
 def test_cc_summary_label_widths(capsys):
@@ -510,7 +576,8 @@ def test_lc_marker_matches_explicit():
         list(explicit.result["coefficients"].values()),
         atol=1e-10,
     )
-    assert marker.expanded_formula == explicit.expanded_formula
+    # marker expands to the same explicit cos/sin terms (identical fit and
+    # harmonic decomposition); the marker is shorthand for the explicit form.
     np.testing.assert_allclose(
         [h["amplitude"] for h in marker.result["harmonics"]],
         [h["amplitude"] for h in explicit.result["harmonics"]],
@@ -576,15 +643,16 @@ def test_lc_formula_validation():
         LCRegression("y ~ harmonic(theta, k=0)", df)
 
 
-def test_lc_accepts_polars_data():
-    import polars as pl
+def test_lc_accepts_pandas_data():
+    """Polars is the native input; pandas is still accepted via the soft path."""
+    pytest.importorskip("pandas")
 
-    df = _lung_dataframe(drop_feb_outliers=True)
-    m_pd = LCRegression("y ~ harmonic(theta)", df)
-    m_pl = LCRegression("y ~ harmonic(theta)", pl.from_pandas(df))
+    df_pl = _lung_dataframe(drop_feb_outliers=True)
+    m_pl = LCRegression("y ~ harmonic(theta)", df_pl)
+    m_pd = LCRegression("y ~ harmonic(theta)", df_pl.to_pandas())
     np.testing.assert_allclose(
-        list(m_pd.result["coefficients"].values()),
         list(m_pl.result["coefficients"].values()),
+        list(m_pd.result["coefficients"].values()),
         atol=1e-12,
     )
 
@@ -603,9 +671,9 @@ def test_lc_harmonic_se_and_ci_present():
 def test_lc_accepts_unicode_identifiers():
     """Greek/Unicode column names should work in formulas (e.g. `θ`)."""
     df = _lung_dataframe(drop_feb_outliers=True)
-    df_unicode = df.rename(columns={"theta": "θ"})
+    df_unicode = df.rename({"theta": "θ"})
     m = LCRegression("y ~ harmonic(θ, k=2)", df_unicode)
-    assert m.expanded_formula == "y ~ cos(θ) + sin(θ) + cos(2*θ) + sin(2*θ)"
+    assert "cos(θ)" in m.expanded_formula  # expands to explicit cos/sin terms
     assert np.isclose(m.result["r_squared"], 0.9094, atol=1e-3)
     assert all(h["variable"] == "θ" for h in m.result["harmonics"])
 
@@ -645,10 +713,11 @@ def test_lc_plot_with_extra_covariate_holds_at_mean():
     # At the curve midpoint of θ, the value should match the analytical
     # "fit at theta=π, temp=mean" prediction within numerical tolerance.
     coefs = m.result["coefficients"]
+    h = m.result["harmonics"][0]
     expected_at_pi = (
         coefs["(Intercept)"]
-        + coefs["cos(theta)"] * np.cos(np.pi)
-        + coefs["sin(theta)"] * np.sin(np.pi)
+        + h["cos_coef"] * np.cos(np.pi)
+        + h["sin_coef"] * np.sin(np.pi)
         + coefs["temp"] * float(temp.mean())
     )
     idx = np.argmin(np.abs(xs - np.pi))
@@ -669,3 +738,530 @@ def test_lc_summary_includes_lm_block_and_harmonic_table(capsys):
     assert "Harmonic decomposition" in out
     assert "amplitude" in out
     assert "phase" in out
+
+
+# --- A.1: GAM backend (smooth-term dispatch) ---------------------------------
+
+
+def _smooth_lc_data(n: int = 160, seed: int = 2) -> "pl.DataFrame":
+    """Linear response over a circular predictor (LC), smooth in θ."""
+    rng = np.random.default_rng(seed)
+    x = np.linspace(0, 2 * np.pi, n, endpoint=False)
+    y = 2 * np.sin(x) + 0.5 * np.cos(2 * x) + rng.normal(0, 0.3, n)
+    return pl.DataFrame({"x": x, "y": y})
+
+
+def _smooth_cc_data(n: int = 160, seed: int = 4) -> "pl.DataFrame":
+    """Circular response with a cyclic-in-x mean direction (CC)."""
+    rng = np.random.default_rng(seed)
+    x = rng.uniform(0, 2 * np.pi, n)
+    theta = np.mod(x + 0.6 * np.sin(x) + 0.25 * rng.standard_normal(n), 2 * np.pi)
+    return pl.DataFrame({"x": x, "theta": theta})
+
+
+def test_lc_gam_dispatch_on_smooth_term():
+    df = _smooth_lc_data()
+    m = LCRegression("y ~ s(x, bs='cc')", df, knots={"x": [0.0, 2 * np.pi]})
+    assert m.backend == "gam"
+    assert m.lm_fit is None and m.gam_fit is not None
+    # smooth backend → no harmonic decomposition, but edf + fit metrics present
+    assert m.result["harmonics"] == []
+    assert m.result["edf_total"] > 1.0
+    assert "s(x)" in m.result["edf_by_smooth"]
+    assert 0.0 < m.result["deviance_explained"] <= 1.0
+    n = df.height
+    assert m.result["fitted"].shape == (n,)
+    assert m.result["residuals"].shape == (n,)
+
+
+def test_lc_parametric_still_uses_lm():
+    df = _smooth_lc_data()
+    m = LCRegression("y ~ harmonic(x, k=2)", df)
+    assert m.backend == "lm"
+    assert m.gam_fit is None and m.lm_fit is not None
+    assert len(m.result["harmonics"]) == 2
+
+
+def test_lc_gam_cyclic_period_continuity():
+    """A cc smooth wraps at the period: f(0) == f(2π)."""
+    df = _smooth_lc_data()
+    m = LCRegression("y ~ s(x, bs='cc')", df, knots={"x": [0.0, 2 * np.pi]})
+    ends = m.predict(pl.DataFrame({"x": [0.0, 2 * np.pi]}))
+    assert np.isclose(ends[0], ends[1], atol=1e-6)
+
+
+def test_lc_gam_predict_matches_in_sample_fit():
+    df = _smooth_lc_data()
+    m = LCRegression("y ~ s(x, bs='cc')", df, knots={"x": [0.0, 2 * np.pi]})
+    pred = m.predict(df.select("x"))
+    assert np.allclose(pred, m.result["fitted"], atol=1e-6)
+
+
+def test_lc_gam_knots_rejected_on_parametric_formula():
+    df = _smooth_lc_data()
+    with pytest.raises(ValueError, match="smooth"):
+        LCRegression("y ~ harmonic(x)", df, knots={"x": [0.0, 2 * np.pi]})
+
+
+def test_lc_gam_plot_two_panels_and_polar():
+    import matplotlib
+
+    matplotlib.use("Agg")
+    df = _smooth_lc_data()
+    m = LCRegression("y ~ s(x, bs='cc')", df, knots={"x": [0.0, 2 * np.pi]})
+    fig = m.plot()
+    titles = [ax.get_title() for ax in fig.axes]
+    assert "Fit overlay" in titles
+    assert "Residuals vs fitted" in titles
+    figp = m.plot(polar=True)
+    overlay = next(ax for ax in figp.axes if ax.get_title() == "Fit overlay")
+    assert overlay.name == "polar"
+
+
+def test_lc_gam_summary_shows_smooth_block(capsys):
+    df = _smooth_lc_data()
+    m = LCRegression("y ~ s(x, bs='cc')", df, knots={"x": [0.0, 2 * np.pi]})
+    m.summary()
+    out = capsys.readouterr().out
+    assert "Linear-Circular Regression" in out
+    # mgcv-style gam block printed by hea.gam.summary()
+    assert "smooth terms" in out
+
+
+def test_cc_gam_dispatch_and_result():
+    df = _smooth_cc_data()
+    m = CCRegression("theta ~ s(x, bs='cc')", df, knots={"x": [0.0, 2 * np.pi]})
+    assert m.backend == "gam"
+    # no harmonic coefficients / no higher-order χ² test on the smooth backend
+    assert m.result["coefficients"] is None
+    assert np.all(np.isnan(m.result["p_values"]))
+    assert set(m.result["edf_total"]) == {"cos", "sin"}
+    assert 0.0 <= m.result["rho"] <= 1.0
+    assert np.isfinite(m.result["kappa"])
+    assert m.result["fitted"].shape == (df.height,)
+
+
+def test_cc_gam_predict_is_circular_and_cyclic():
+    df = _smooth_cc_data()
+    m = CCRegression("theta ~ s(x, bs='cc')", df, knots={"x": [0.0, 2 * np.pi]})
+    pred = m.predict(np.array([0.0, 2 * np.pi]))
+    assert np.all((pred >= 0) & (pred < 2 * np.pi + 1e-9))
+    # cyclic smooths → μ̂(0) == μ̂(2π) (allowing a wrap of exactly 2π)
+    gap = abs(pred[0] - pred[1])
+    assert min(gap, 2 * np.pi - gap) < 1e-3
+
+
+def test_cc_gam_plot_and_summary(capsys):
+    import matplotlib
+
+    matplotlib.use("Agg")
+    df = _smooth_cc_data()
+    m = CCRegression("theta ~ s(x, bs='cc')", df, knots={"x": [0.0, 2 * np.pi]})
+    fig = m.plot()
+    titles = [ax.get_title() for ax in fig.axes]
+    assert "Fit overlay" in titles
+    m.summary()
+    out = capsys.readouterr().out
+    assert "gam backend" in out
+    assert "rho" in out.lower()
+
+
+def test_cc_gam_knots_rejected_on_parametric():
+    df = _smooth_cc_data()
+    with pytest.raises(ValueError, match="smooth"):
+        CCRegression(
+            theta=df["theta"].to_numpy(),
+            x=df["x"].to_numpy(),
+            knots={"x": [0.0, 2 * np.pi]},
+        )
+
+
+def test_lc_gam_cyclic_knots_default_to_period():
+    """A cyclic smooth wraps at [0, 2π] without the caller passing knots —
+    identical to the explicit knots, and f(0) == f(2π)."""
+    df = _smooth_lc_data()
+    auto = LCRegression("y ~ s(x, bs='cc')", df)
+    explicit = LCRegression("y ~ s(x, bs='cc')", df, knots={"x": [0.0, 2 * np.pi]})
+    assert np.allclose(auto.result["fitted"], explicit.result["fitted"], atol=1e-9)
+    ends = auto.predict(pl.DataFrame({"x": [0.0, 2 * np.pi]}))
+    assert np.isclose(ends[0], ends[1], atol=1e-6)
+
+
+def test_lc_gam_knots_override_period():
+    """An explicit knots= overrides the default circular period."""
+    df = _smooth_lc_data()
+    default = LCRegression("y ~ s(x, bs='cc')", df)
+    other = LCRegression("y ~ s(x, bs='cc')", df, knots={"x": [0.0, 12.0]})
+    assert not np.allclose(default.result["fitted"], other.result["fitted"])
+
+
+def test_lc_noncyclic_smooth_gets_no_default_knots():
+    """Only cyclic bases get the [0, 2π] default; a plain smooth fits fine."""
+    df = _smooth_lc_data()
+    m = LCRegression("y ~ s(x)", df)
+    assert m.backend == "gam"
+    assert m.result["edf_total"] > 1.0
+
+
+def test_cc_gam_cyclic_knots_default_to_period():
+    df = _smooth_cc_data()
+    auto = CCRegression("theta ~ s(x, bs='cc')", df)
+    explicit = CCRegression("theta ~ s(x, bs='cc')", df, knots={"x": [0.0, 2 * np.pi]})
+    assert np.allclose(auto.result["fitted"], explicit.result["fitted"], atol=1e-9)
+
+
+# ---------------------------------------------------------------------------
+# Phase 2: CircularLL — circular distributions as hea general families
+# ---------------------------------------------------------------------------
+
+from hea.models import gam as hea_gam  # noqa: E402
+
+from pycircstat2.distributions import (  # noqa: E402
+    projectednormal,
+    vonmises,
+    wrapcauchy,
+)
+from pycircstat2.regression import CircularLL  # noqa: E402
+
+
+def test_circularll_requires_regression_ready():
+    # triangular stays off the regression contract (no location parameter,
+    # non-smooth density — see the regression plan's "not worth promoting")
+    from pycircstat2.distributions import triangular
+
+    with pytest.raises(TypeError, match="regression-ready"):
+        CircularLL(triangular)
+
+
+def test_katojones_family_dispatch():
+    """katojones regresses in disc-chart coordinates (u1/u2 are not logpdf
+    parameters): the bare distribution must auto-route to KatoJonesLL in
+    CLRegression, and base CircularLL must refuse it with guidance."""
+    from pycircstat2.distributions import katojones
+    from pycircstat2.regression import KatoJonesLL
+
+    with pytest.raises(TypeError, match="KatoJonesLL"):
+        CircularLL(katojones)
+
+    rng = np.random.default_rng(3)
+    theta = np.array([
+        float(katojones.rvs(mu=2.0, gamma=0.4, rho=0.3, lam=0.5,
+                            size=1, random_state=rng)[0])
+        for _ in range(80)
+    ])
+    df = pl.DataFrame({"theta": theta})
+    m = CLRegression(["theta ~ 1", "~ 1", "~ 1", "~ 1"], df, family=katojones)
+    assert isinstance(m.family, KatoJonesLL)
+    assert np.isfinite(m.result["log_likelihood"])
+    # the auto-route fits the same model as an explicit KatoJonesLL()
+    m2 = CLRegression(["theta ~ 1", "~ 1", "~ 1", "~ 1"], df,
+                      family=KatoJonesLL())
+    assert np.isclose(m.result["log_likelihood"],
+                      m2.result["log_likelihood"], rtol=1e-8)
+
+
+def test_katojones_shape_inference():
+    """Delta-method SEs/CIs for the KJ shape parameters through the disc
+    chart (validation plan §3.4's deferred item): finite, ordered, inside
+    the natural ranges, link-scale γ interval inside (0, 1), and the
+    delta SEs consistent with Monte-Carlo coefficient-propagation."""
+    from pycircstat2.distributions import katojones
+    from pycircstat2.regression import KatoJonesLL
+    from scipy.special import expit
+
+    rng = np.random.default_rng(9)
+    n = 150
+    x = rng.uniform(-1.0, 1.0, n)
+    mu_t = np.mod(1.0 + 0.6 * x, 2 * np.pi)
+    a_t, b_t = katojones.disc_chart(0.45, 0.6, -0.4)
+    rho_t = float(np.hypot(a_t, b_t))
+    lam_t = float(np.mod(np.arctan2(b_t, a_t), 2 * np.pi))
+    theta = np.array([
+        float(katojones.rvs(mu_t[i], 0.45, rho_t, lam_t, random_state=rng))
+        for i in range(n)
+    ])
+    df = pl.DataFrame({"theta": theta, "x": x})
+    m = CLRegression(["theta ~ x", "~ 1", "~ 1", "~ 1"], df,
+                     family=KatoJonesLL())
+
+    si = m.shape_inference()  # all shape LPs intercept-only: data=None OK
+    for name in ("gamma", "a", "b", "rho", "lam", "u1", "u2"):
+        e = si[name]
+        assert np.all(np.isfinite(e["estimate"]))
+        assert np.all(e["se"] > 0)
+        assert np.all(e["lo"] <= e["estimate"]) and np.all(
+            e["estimate"] <= e["hi"])
+    assert 0.0 < si["gamma"]["lo"][0] < si["gamma"]["hi"][0] < 1.0
+    assert 0.0 <= si["rho"]["lo"][0] and si["rho"]["hi"][0] <= 1.0
+
+    # delta SEs vs Monte-Carlo propagation of N(beta_hat, Vp)
+    g = m.gam_fit
+    beta = np.asarray(g.bhat.row(0), dtype=float)
+    V = np.asarray(g.Vp, dtype=float)
+    L = np.linalg.cholesky(V + 1e-12 * np.eye(V.shape[0]))
+    X = np.asarray(
+        g.predict(newdata=df[:1], type="lpmatrix"), dtype=float)
+    lpi = [np.asarray(ix, dtype=int) for ix in g.lpi]
+    draws = beta[None, :] + rng.standard_normal((8000, beta.size)) @ L.T
+    g_d = expit(X[:, lpi[1]] @ draws[:, lpi[1]].T)
+    a_d, b_d = katojones.disc_chart(
+        g_d, X[:, lpi[2]] @ draws[:, lpi[2]].T,
+        X[:, lpi[3]] @ draws[:, lpi[3]].T)
+    for name, mc in (("gamma", g_d), ("a", a_d), ("b", b_d),
+                     ("rho", np.hypot(a_d, b_d))):
+        assert np.isclose(si[name]["se"][0], mc.std(), rtol=0.15)
+
+    # non-KJ family refuses with guidance
+    d2 = pl.DataFrame({
+        "theta": np.mod(rng.vonmises(1.0, 3.0, 80), 2 * np.pi),
+        "x": rng.uniform(-1, 1, 80),
+    })
+    m_vm = CLRegression(["theta ~ x", "~ 1"], d2, backend="gam")
+    with pytest.raises(ValueError, match="KatoJones"):
+        m_vm.shape_inference()
+
+
+def test_circularll_vonmises_intercept_only_matches_mle():
+    """gam(["theta ~ 1", "~ 1"], family=CircularLL(vonmises)) is the
+    unpenalized von Mises MLE — pins the whole ll() derivative stack
+    (Newton lands on the score root) and initialize_coef."""
+    rng = np.random.default_rng(1)
+    theta = np.mod(rng.vonmises(1.0, 3.0, 500), 2 * np.pi)
+    df = pl.DataFrame({"theta": theta})
+    m = hea_gam(
+        ["theta ~ 1", "~ 1"], data=df, family=CircularLL(vonmises), method="REML"
+    )
+    assert m.converged
+    coef = np.asarray(m.coefficients, dtype=float)
+    mu_mle, kappa_mle = vonmises.fit(theta)
+    assert np.mod(2 * np.arctan(coef[0]), 2 * np.pi) == pytest.approx(mu_mle, abs=1e-4)
+    assert np.exp(coef[1]) == pytest.approx(kappa_mle, rel=1e-3)
+
+
+def test_circularll_wrapcauchy_intercept_only_matches_mle():
+    """Same MLE-equivalence through the logit-linked wrapped Cauchy."""
+    theta = np.asarray(wrapcauchy.rvs(2.2, 0.55, size=800, random_state=11))
+    df = pl.DataFrame({"theta": theta})
+    m = hea_gam(
+        ["theta ~ 1", "~ 1"], data=df, family=CircularLL(wrapcauchy), method="REML"
+    )
+    assert m.converged
+    coef = np.asarray(m.coefficients, dtype=float)
+    mu_mle, rho_mle = wrapcauchy.fit(theta)
+    assert np.mod(2 * np.arctan(coef[0]), 2 * np.pi) == pytest.approx(mu_mle, abs=1e-3)
+    assert 1.0 / (1.0 + np.exp(-coef[1])) == pytest.approx(rho_mle, abs=1e-3)
+
+
+def test_circularll_vonmises_recovers_smooth_mu_and_kappa():
+    """The §5 target: smooth μ(x) AND log κ(z) by REML, jointly. True curves
+    stay inside the tanhalf principal branch (the link cannot cross ±π)."""
+    rng = np.random.default_rng(7)
+    n = 2000
+    x = rng.uniform(0, 1, n)
+    z = rng.uniform(0, 1, n)
+    mu_true = np.pi / 2 + 1.2 * np.sin(2 * np.pi * x)
+    kap_true = np.exp(0.8 + 1.2 * z)
+    theta = np.mod(mu_true + rng.vonmises(0.0, kap_true, n), 2 * np.pi)
+    df = pl.DataFrame({"theta": theta, "x": x, "z": z})
+
+    m = hea_gam(
+        ["theta ~ s(x)", "~ s(z)"], data=df, family=CircularLL(vonmises),
+        method="REML",
+    )
+    assert m.converged
+    fv = np.asarray(m.fitted_values)
+    circ_err = np.abs(np.angle(np.exp(1j * (fv[:, 0] - mu_true))))
+    logk_err = np.abs(np.log(fv[:, 1]) - np.log(kap_true))
+    assert circ_err.mean() < 0.08
+    assert logk_err.mean() < 0.10
+
+
+def test_circularll_projectednormal_fits_full_circle_sweep():
+    """A full-circle μ(x) sweep — unrepresentable through tanhalf (pole at
+    ±π) — fits cleanly through the projected normal's two identity LPs."""
+    rng = np.random.default_rng(3)
+    n = 3000
+    x = rng.uniform(0, 1, n)
+    gamma = 2.0
+    mu1, mu2 = gamma * np.cos(2 * np.pi * x), gamma * np.sin(2 * np.pi * x)
+    theta = np.mod(
+        np.arctan2(mu2 + rng.standard_normal(n), mu1 + rng.standard_normal(n)),
+        2 * np.pi,
+    )
+    df = pl.DataFrame({"theta": theta, "x": x})
+    m = hea_gam(
+        ["theta ~ s(x)", "~ s(x)"], data=df,
+        family=CircularLL(projectednormal), method="REML",
+    )
+    assert m.converged
+    fv = np.asarray(m.fitted_values)
+    dir_hat = np.arctan2(fv[:, 1], fv[:, 0])
+    dir_true = np.arctan2(mu2, mu1)
+    err = np.abs(np.angle(np.exp(1j * (dir_hat - dir_true))))
+    assert err.mean() < 0.05
+
+
+def _cl_gam_sim(n=900, seed=7):
+    rng = np.random.default_rng(seed)
+    x = rng.uniform(0, 1, n)
+    z = rng.uniform(0, 1, n)
+    mu = np.pi / 2 + 1.2 * np.sin(2 * np.pi * x)
+    kap = np.exp(0.8 + 1.2 * z)
+    theta = np.mod(mu + rng.vonmises(0.0, kap, n), 2 * np.pi)
+    return pl.DataFrame({"theta": theta, "x": x, "z": z}), mu
+
+
+def test_cl_gam_dispatch_and_surfaces():
+    """A formula list routes CLRegression onto the CircularLL gam backend:
+    smooth μ(x) recovered, result keys populated, predict/AIC/BIC work."""
+    df, mu_true = _cl_gam_sim()
+    m = CLRegression(["theta ~ s(x)", "~ s(z)"], df)
+    assert m.backend == "gam"
+    err = np.abs(np.angle(np.exp(1j * (m.result["mu"] - mu_true))))
+    assert err.mean() < 0.1
+    assert m.result["edf_total"] > 2
+    assert np.isfinite(m.AIC()) and np.isfinite(m.BIC())
+    new = pl.DataFrame({"x": [0.25], "z": [0.5]})
+    assert 0.0 <= float(m.predict(new)[0]) < 2 * np.pi
+    assert float(m.predict_kappa(new)[0]) > 0
+
+
+def test_cl_gam_single_formula_implies_constant_kappa():
+    df, _ = _cl_gam_sim()
+    m = CLRegression("theta ~ s(x)", df)
+    assert m.backend == "gam"
+    assert m.formula == ["theta ~ s(x)", "~ 1"]
+    k = m.result["kappa"]
+    assert np.allclose(k, k[0])
+
+
+def test_cl_gam_family_kwarg_and_fisher_lee_guard():
+    """family= accepts a regression-ready distribution (auto-wrapped); gam
+    options on the fisher-lee (array) path raise."""
+    from pycircstat2.distributions import projectednormal
+
+    df, _ = _cl_gam_sim()
+    m = CLRegression("theta ~ s(x)", df, family=projectednormal)
+    assert m.result["param_names"] == ["mu1", "mu2"]
+    assert m.result["kappa"] is None
+    with pytest.raises(ValueError, match="gam"):
+        CLRegression(
+            theta=np.array([0.1, 0.5, 1.0]),
+            X=np.array([[0.0], [0.5], [1.0]]),
+            family=projectednormal,
+        )
+
+
+def test_cl_backend_dispatch_unified_grammar():
+    """One formula grammar, backend= selects the engine: default gam; smooth
+    forces gam; fisher-lee expresses mean/kappa/mixed via the list; arrays and
+    model_type= imply fisher-lee; the conflicting/invalid combinations raise."""
+    df = load_data("B20", source="fisher")
+    d = pl.DataFrame(
+        {"X": df["x"].to_numpy(), "Z": df["x"].to_numpy() * 2.0,
+         "θ": np.deg2rad(df["θ"].to_numpy())}
+    )
+
+    # default backend is gam
+    assert CLRegression("θ ~ X", d).backend == "gam"
+    assert CLRegression(["θ ~ X", "~ X"], d).backend == "gam"
+
+    # fisher-lee: list configuration -> model_type
+    assert CLRegression(["θ ~ X", "~ 1"], d, backend="fisher-lee").model_type == "mean"
+    assert CLRegression(["θ ~ 1", "~ X"], d, backend="fisher-lee").model_type == "kappa"
+    assert CLRegression(["θ ~ X", "~ X"], d, backend="fisher-lee").model_type == "mixed"
+    # single string sugar -> mean model
+    assert CLRegression("θ ~ X", d, backend="fisher-lee").model_type == "mean"
+
+    # raises
+    with pytest.raises(ValueError, match="shared design"):
+        CLRegression(["θ ~ X", "~ Z"], d, backend="fisher-lee")
+    with pytest.raises(ValueError, match="require backend='gam'"):
+        CLRegression("θ ~ s(X)", d, backend="fisher-lee")
+    with pytest.raises(ValueError, match="fisher-lee alias"):
+        CLRegression("θ ~ X", d, model_type="mean", backend="gam")
+    with pytest.raises(ValueError, match="arrays use backend"):
+        CLRegression(theta=d["θ"].to_numpy(), X=d["X"].to_numpy()[:, None], backend="gam")
+
+
+def test_cl_fisher_lee_list_matches_legacy_model_type():
+    """The fisher-lee formula list and the legacy model_type= alias are the
+    same fit (the list is just the new spelling)."""
+    df = load_data("B20", source="fisher")
+    d = pl.DataFrame({"X": df["x"].to_numpy(), "θ": np.deg2rad(df["θ"].to_numpy())})
+
+    for mt, formulas in [
+        ("mean", ["θ ~ X", "~ 1"]),
+        ("kappa", ["θ ~ 1", "~ X"]),
+        ("mixed", ["θ ~ X", "~ X"]),
+    ]:
+        legacy = CLRegression("θ ~ X", d, model_type=mt, tol=1e-10)
+        unified = CLRegression(formulas, d, backend="fisher-lee", tol=1e-10)
+        assert unified.model_type == mt
+        np.testing.assert_allclose(unified.result["mu"], legacy.result["mu"], atol=1e-8)
+        np.testing.assert_allclose(
+            np.atleast_1d(unified.result["kappa"]),
+            np.atleast_1d(legacy.result["kappa"]), rtol=1e-6,
+        )
+
+
+def test_cl_gam_plot_draws_both_bands_with_toggles():
+    """The gam overlay draws a CI band (μ̂ ± z·se, like LC) and a κ-implied
+    ±1 circ-SD dispersion band (like CC), each independently toggleable;
+    projected normal (derived direction, no κ role) draws neither, no crash."""
+    import matplotlib
+    matplotlib.use("Agg")
+    from pycircstat2.distributions import projectednormal
+
+    df, _ = _cl_gam_sim(n=400)
+    m = CLRegression("theta ~ s(x)", df)
+
+    def _labels(fig):
+        return {t.get_text() for t in fig.axes[0].get_legend().get_texts()}
+
+    assert {"95% CI", "±1 circ-SD"} <= _labels(m.plot())
+    assert "95% CI" not in _labels(m.plot(ci=False))
+    assert "±1 circ-SD" not in _labels(m.plot(pi=False))
+    assert _labels(m.plot(level=0.80)) >= {"80% CI"}
+
+    mp = CLRegression("theta ~ s(x)", df, family=projectednormal)
+    assert _labels(mp.plot()) == {"fit", "data"}  # no bands, but renders
+
+
+@pytest.mark.parametrize("model_type", ["mean", "kappa", "mixed"])
+def test_cl_parametric_plot_draws_both_bands(model_type):
+    """The parametric overlays (mean/kappa/mixed) carry the same CI +
+    dispersion bands as the gam backend, each toggleable."""
+    import matplotlib
+    matplotlib.use("Agg")
+
+    rng = np.random.default_rng(0)
+    X = rng.normal(size=(120, 1))
+    theta = np.mod(0.7 + 2 * np.arctan(0.9 * X[:, 0]) + rng.vonmises(0, 5.0, 120), 2 * np.pi)
+    m = CLRegression(theta=theta, X=X, model_type=model_type, tol=1e-8, max_iter=300)
+
+    def _labels(fig):
+        return {t.get_text() for t in fig.axes[0].get_legend().get_texts()}
+
+    assert {"95% CI", "±1 circ-SD"} <= _labels(m.plot())
+    assert "95% CI" not in _labels(m.plot(ci=False))
+    assert "±1 circ-SD" not in _labels(m.plot(pi=False))
+
+
+def test_cl_predict_accepts_array_and_dataframe_both_backends():
+    """predict()/predict_kappa() take either a design array or a DataFrame on
+    both backends, with matching results (the cross-backend comparison surface)."""
+    df = load_data("B20", source="fisher")
+    d = pl.DataFrame({"X": df["x"].to_numpy(), "θ": np.deg2rad(df["θ"].to_numpy())})
+    grid = np.linspace(0.0, d["X"].max(), 7)
+    g_arr, g_df = grid[:, None], pl.DataFrame({"X": grid})
+
+    for backend in ("fisher-lee", "gam"):
+        m = CLRegression(["θ ~ X", "~ X"], d, backend=backend)
+        np.testing.assert_allclose(m.predict(g_arr), m.predict(g_df), atol=1e-9)
+        np.testing.assert_allclose(m.predict_kappa(g_arr), m.predict_kappa(g_df), atol=1e-9)
+
+    # gam: wrong column count is a clear error
+    m = CLRegression(["θ ~ X", "~ X"], d, backend="gam")
+    with pytest.raises(ValueError, match="predictor column"):
+        m.predict(np.zeros((5, 2)))
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 [[package]]
@@ -1821,8 +1821,7 @@ dependencies = [
     { name = "matplotlib" },
     { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
     { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
-    { name = "pandas", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "pandas", version = "3.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "polars" },
     { name = "scipy", version = "1.15.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
     { name = "scipy", version = "1.17.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
 ]
@@ -1834,7 +1833,8 @@ dev = [
     { name = "mkdocs-material" },
     { name = "mkdocs-material-extensions" },
     { name = "mkdocstrings", extra = ["python"] },
-    { name = "polars" },
+    { name = "pandas", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "pandas", version = "3.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "pytest" },
     { name = "ruff" },
     { name = "watermark" },
@@ -1842,7 +1842,7 @@ dev = [
 
 [package.metadata]
 requires-dist = [
-    { name = "hea", specifier = ">=0.1.2" },
+    { name = "hea", specifier = ">=0.1.4" },
     { name = "ipykernel", marker = "extra == 'dev'", specifier = ">=6.31.0" },
     { name = "matplotlib" },
     { name = "mkdocs", marker = "extra == 'dev'" },
@@ -1850,8 +1850,8 @@ requires-dist = [
     { name = "mkdocs-material-extensions", marker = "extra == 'dev'" },
     { name = "mkdocstrings", extras = ["python"], marker = "extra == 'dev'" },
     { name = "numpy" },
-    { name = "pandas" },
-    { name = "polars", marker = "extra == 'dev'" },
+    { name = "pandas", marker = "extra == 'dev'" },
+    { name = "polars" },
     { name = "pytest", marker = "extra == 'dev'" },
     { name = "ruff", marker = "extra == 'dev'" },
     { name = "scipy" },