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: 1.0.0
Depends: R (≥ 3.4.0)
Imports: Rcpp (≥ 0.12.13), MASS (≥ 7.3.47), haven (≥ 1.1.0), methods (≥ 3.4.1)
LinkingTo: Rcpp (≥ 0.12.13), RcppArmadillo (≥ 0.7.960.1.2), BH (≥
Published: 2018-02-27
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
Package source: bpnreg_1.0.0.tar.gz
Windows binaries: r-devel: bpnreg_1.0.0.zip, r-release: bpnreg_1.0.0.zip, r-oldrel: not available
OS X El Capitan binaries: r-release: bpnreg_1.0.0.tgz
OS X Mavericks binaries: r-oldrel: not available


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