Welcome to the RandomWalker Wiki! This comprehensive guide will help you master the RandomWalker R package for generating, visualizing, and analyzing random walks.
RandomWalker is a comprehensive R package that provides a unified, tidyverse-compatible interface for generating random walks of various types. Whether you're modeling stock prices, simulating particle movements, or exploring stochastic processes, RandomWalker makes it easy to:
- Generate random walks from 27+ different probability distributions
- Create walks in 1D, 2D, or 3D space
- Visualize walks with beautiful, interactive plots
- Compute comprehensive statistical summaries
- Work seamlessly with tidyverse tools
- Installation - How to install the package
- Quick Start Guide - Get up and running in minutes
- Basic Concepts - Understanding random walks
- Automatic Random Walks - Using
rw30()for instant results - Continuous Distributions - Normal, Brownian, Gamma, Beta, and more
- Discrete Distributions - Binomial, Poisson, Geometric, and more
- Multi-Dimensional Walks - Working in 2D and 3D space
- Visualization Guide - Creating beautiful plots
- Statistical Analysis Guide - Computing summary statistics
- Use Cases and Examples - Real-world applications
- API Reference - Complete function documentation
- FAQ - Frequently Asked Questions
- Troubleshooting - Common issues and solutions
- Contributing Guide - How to contribute to the project
Generate random walks from a wide variety of probability distributions including:
- Continuous: Normal, Brownian Motion, Geometric Brownian Motion, Beta, Cauchy, Chi-Squared, Exponential, F, Gamma, Log-Normal, Logistic, Student's t, Uniform, Weibull
- Discrete: Binomial, Discrete, Geometric, Hypergeometric, Multinomial, Negative Binomial, Poisson
- Custom: Define your own displacement functions
- 1D random walks for time series analysis
- 2D random walks for spatial modeling
- 3D random walks for particle physics simulations
- Static plots with ggplot2
- Interactive visualizations with ggiraph
- Support for multiple walk comparison
- Customizable aesthetics
- Comprehensive summary statistics
- Cumulative functions (sum, product, min, max, mean)
- Confidence intervals
- Running quantiles
- Euclidean distance calculations
- Harmonic and geometric means
- Skewness and kurtosis
Works seamlessly with:
dplyrfor data manipulationtidyrfor data reshapingggplot2for custom visualizations- Pipe operators (
|>and%>%)
- Current Version: 1.0.0.9000 (development)
- CRAN Release: 1.0.0
- License: MIT
- Authors: Steven P. Sanderson II, MPH & Antti Rask
- R Version Required: >= 4.1.0
- Package Website: https://www.spsanderson.com/RandomWalker/
- GitHub Repository: https://github.com/spsanderson/RandomWalker
- Issue Tracker: https://github.com/spsanderson/RandomWalker/issues
- CRAN Page: https://cran.r-project.org/package=RandomWalker
If you're new to RandomWalker, we recommend following this learning path:
- Installation - Install the package
- Quick Start Guide - Learn the basics
- Automatic Random Walks - Use
rw30()for quick results - Continuous Distribution Generators - Explore different distributions
- Visualization Guide - Create beautiful plots
- Statistical Analysis Guide - Analyze your walks
- Use Cases and Examples - See real-world applications
- Finance: Model stock price movements with Geometric Brownian Motion
- Physics: Simulate particle diffusion with Brownian Motion
- Biology: Model organism movement patterns
- Computer Science: Generate test data for algorithms
- Education: Teach probability and stochastic processes
- Research: Explore theoretical properties of random walks
- Documentation: Read the vignettes with
vignette("getting-started") - Issues: Report bugs at the GitHub Issues page
- Discussions: Ask questions in GitHub Discussions
- Email: Contact the maintainer at spsanderson@gmail.com
If you use RandomWalker in your research, please cite it:
citation("RandomWalker")Ready to get started? Head over to the Installation page to begin your journey with RandomWalker!