bpnreg: Bayesian Projected Normal Regression Models for Circular Data

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>.

Version: 2.0.3
Depends: R (≥ 3.6.0)
Imports: Rcpp (≥ 1.0.2), haven (≥ 2.1.1), methods (≥ 3.6.0)
LinkingTo: Rcpp (≥ 1.0.2), RcppArmadillo (≥, BH (≥
Suggests: qpdf, knitr, rmarkdown
Published: 2024-01-15
DOI: 10.32614/CRAN.package.bpnreg
Author: Jolien Cremers [aut, cre]
Maintainer: Jolien Cremers <joliencremers at gmail.com>
BugReports: https://github.com/joliencremers/bpnreg/issues
License: GPL-3
URL: https://github.com/joliencremers/bpnreg
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: bpnreg results


Reference manual: bpnreg.pdf
Vignettes: FAQ


Package source: bpnreg_2.0.3.tar.gz
Windows binaries: r-devel: bpnreg_2.0.3.zip, r-release: bpnreg_2.0.3.zip, r-oldrel: bpnreg_2.0.3.zip
macOS binaries: r-release (arm64): bpnreg_2.0.3.tgz, r-oldrel (arm64): bpnreg_2.0.3.tgz, r-release (x86_64): bpnreg_2.0.3.tgz, r-oldrel (x86_64): bpnreg_2.0.3.tgz
Old sources: bpnreg archive


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