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Merge pull request #110 from DistanceDevelopment/mrds_issue_15
Updated documentation of flnl.grad.R
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DESCRIPTION

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fitting is performed via maximum likelihood. Also included are diagnostics
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and plotting for fitted detection functions. Abundance estimation is via a
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Horvitz-Thompson-like estimator.
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Version: 3.0.0.9000
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Version: 3.0.0.9001
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URL: https://github.com/DistanceDevelopment/mrds/
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BugReports: https://github.com/DistanceDevelopment/mrds/issues
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Depends:

NEWS.md

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# mrds 3.0.1
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Bug Fixes
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* Fixed formatting issue in flnl.grad help
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# mrds 3.0.0
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New features

R/flnl.grad.R

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#' Gradients of negative log likelihood function
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#'
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#' Gradient of the negative log likelihood function
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#'
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#' This function derives the gradients of the negative log likelihood function,
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#' with respect to all parameters. It is based on the theory presented in
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#' Introduction to Distance Sampling (2001) and Distance Sampling: Methods and
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#' Applications (2015). It is not meant to be called by users of the \code{mrds}
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#' and \code{Distance} packages directly but rather by the gradient-based
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#' solver. This solver is use when our distance sampling model is for
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#' solver. This solver is used when our distance sampling model is for
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#' single-observer data coming from either line or point transect and only when
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#' the detection function contains an adjustment series but no covariates. It is
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#' implement for the following key + adjustment series combinations for the

man/flnl.grad.Rd

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