Bayesian Projected Normal Regression Models for Circular Data R package bpnreg version 2.0.3 Fitting Bayesian multiple and mixed-effect regression models for circular data based on the projected normal distribution. Both continuous and categorical predictors can be included. Sampling from the posterior is performed via an MCMC algorithm. Posterior descriptives of all parameters, model fit statistics and Bayes factors for hypothesis tests for inequality constrained hypotheses are provided. See Cremers, Mulder & Klugkist (2018) <doi:10.1111/bmsp.12108> and Nuñez-Antonio & Guttiérez-Peña (2014) <doi:10.1016/j.csda.2012.07.025>. Software Depends: R (>= 3.6.0) Imports: Rcpp (>= 1.0.2), haven (>= 2.1.1), methods (>= 3.6.0) LinkingTo: Rcpp (>= 1.0.2), RcppArmadillo (>= 0.10.1.2.0), BH (>= 1.69.0.1) Suggests: qpdf, knitr, rmarkdown Jolien Cremers <joliencremers@gmail.com> Comprehensive R Archive Network (CRAN) Jolien Cremers [aut, cre] GPL-3 2024-01-15 application/tgz https://CRAN.R-project.org/package=bpnreg doi:10.32614/CRAN.package.bpnreg