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: | 1.2.3 |
| Depends: | R (≥ 2.10) |
| Imports: | Rcpp, stats, graphics, shiny, grDevices, ggplot2, reshape2, coda |
| LinkingTo: | Rcpp, BH, RcppArmadillo |
| Published: | 2018-03-09 |
| 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 |
| Reference manual: | circglmbayes.pdf |
| Package source: | circglmbayes_1.2.3.tar.gz |
| Windows binaries: | r-devel: circglmbayes_1.2.3.zip, r-release: circglmbayes_1.2.3.zip, r-oldrel: circglmbayes_1.2.3.zip |
| OS X binaries: | r-release: circglmbayes_1.2.3.tgz, r-oldrel: circglmbayes_1.2.3.tgz |
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