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>.

Version: 1.3.0
Depends: R (≥ 2.10)
Imports: Rcpp, stats, graphics, shiny, grDevices, ggplot2, reshape2, coda
LinkingTo: Rcpp, BH, RcppArmadillo
Published: 2021-01-22
Author: Kees Mulder [aut, cre]
Maintainer: Kees Mulder <keestimmulder at gmail.com>
BugReports: https://github.com/keesmulder/circglmbayes/issues
License: GPL-3
URL: https://github.com/keesmulder/circglmbayes
NeedsCompilation: yes
Citation: circglmbayes citation info
Materials: README
CRAN checks: circglmbayes results

Downloads:

Reference manual: circglmbayes.pdf
Package source: circglmbayes_1.3.0.tar.gz
Windows binaries: r-devel: circglmbayes_1.3.0.zip, r-devel-UCRT: circglmbayes_1.3.0.zip, r-release: circglmbayes_1.3.0.zip, r-oldrel: circglmbayes_1.3.0.zip
macOS binaries: r-release (arm64): circglmbayes_1.3.0.tgz, r-release (x86_64): circglmbayes_1.3.0.tgz, r-oldrel: circglmbayes_1.3.0.tgz
Old sources: circglmbayes archive

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