keesmulder/circglmbayes: Bayesian Analysis of a Circular GLM
Perform a Bayesian analysis of a circular outcome General Linear Model (GLM), which allows regressing a circular outcome on linear and categorical predictors. Posterior samples are obtained by means of an MCMC algorithm written in 'C++' through 'Rcpp'. Estimation and credible intervals are provided, as well as hypothesis testing through Bayes Factors. See Mulder and Klugkist (2017) <doi:10.1016/j.jmp.2017.07.001>.
Getting started
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Package details |
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| Maintainer | Kees Mulder <keestimmulder@gmail.com> |
| License | GPL-3 |
| Version | 1.3.1 |
| URL | https://github.com/keesmulder/circglmbayes |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
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