Package: bpnreg 2.0.3

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>.

Authors:Jolien Cremers [aut, cre]

bpnreg_2.0.3.tar.gz
bpnreg_2.0.3.zip(r-4.7)bpnreg_2.0.3.zip(r-4.6)bpnreg_2.0.3.zip(r-4.5)
bpnreg_2.0.3.tgz(r-4.6-x86_64)bpnreg_2.0.3.tgz(r-4.6-arm64)bpnreg_2.0.3.tgz(r-4.5-x86_64)bpnreg_2.0.3.tgz(r-4.5-arm64)
bpnreg_2.0.3.tar.gz(r-4.7-arm64)bpnreg_2.0.3.tar.gz(r-4.7-x86_64)bpnreg_2.0.3.tar.gz(r-4.6-arm64)bpnreg_2.0.3.tar.gz(r-4.6-x86_64)
bpnreg_2.0.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
bpnreg/json (API)

# Install 'bpnreg' in R:

install.packages('bpnreg', repos = c('https://joliencremers.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/joliencremers/bpnreg/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

Datasets:

Maps - The geometry of human knowledge of navigation space.
Motor - Phase differences in hand flexion-extension movements.

On CRAN:

openblas cpp openmp

6.20 score 15 stars 106 scripts 374 downloads 1 mentions 15 exports 29 dependencies

Last updated from:1d32f5e9ac. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	195
linux-devel-x86_64	OK	168
source / vignettes	OK	235
linux-release-arm64	OK	182
linux-release-x86_64	OK	176
macos-release-arm64	OK	139
macos-release-x86_64	OK	235
macos-oldrel-arm64	OK	106
macos-oldrel-x86_64	OK	290
windows-devel	OK	176
windows-release	OK	159
windows-oldrel	OK	173
wasm-release	OK	181

Exports:BFc bpnme bpnr coef_circ coef_lin coef_ran fit hpd_est hpd_est_circ mean_circ mode_est mode_est_circ rho_circ sd_circ traceplot

Dependencies:BH bit bit64 cli clipr cpp11 crayon forcats glue haven hms lifecycle magrittr pillar pkgconfig prettyunits progress R6 Rcpp RcppArmadillo readr rlang tibble tidyselect tzdb utf8 vctrs vroom withr

FAQ

How to fit a circular regression model: | How to fit a circular mixed effects model: | How to specify interaction effects: | How to format the input data: | Are missing values allowed in the input data and how are missing values dealt with? | How to scale the dependent variable: | Can user-specified priors be used? | Can multiple grouping/nesting factors be used? | How to specify a seed: | How to obtain model summaries: | How to interpret the output of the coef_circ() function: | How to obtain model fit statistics: | How to obtain the raw posterior estimates: | How to obtain random effect variances and individual random effects in circular mixed-effects models: | How to obtain the estimated variance for different groups in a circular regression or circular mixed-effects models: | References

Rendered fromFAQ.Rmdusingknitr::rmarkdown

Last update: 2021-03-09
Started: 2021-03-01

Help page	Topics
Bayes Factors	BFc
Bayes Factors for a Bayesian circular mixed-effects model	BFc.bpnme
Bayes Factors for a Bayesian circular regression model	BFc.bpnr
Fit a Bayesian circular mixed-effects model	bpnme
Fit a Bayesian circular regression model	bpnr
Compute circular coefficients from linear coefficients	circ_coef
Compute circular coefficients	circ_coef_rcpp
Circular coefficients	coef_circ
Obtain the circular coefficients of a Bayesian circular mixed-effects model	coef_circ.bpnme
Obtain the circular coefficients of a Bayesian circular regression model	coef_circ.bpnr
Linear coefficients	coef_lin
Obtain the linear coefficients of a Bayesian circular mixed-effects model	coef_lin.bpnme
Obtain the linear coefficients of a Bayesian circular regression model	coef_lin.bpnr
Random effect variances	coef_ran
Obtain random effect variances of a Bayesian circular mixed-effects model	coef_ran.bpnme
Compute Model Fit Measures Regression Model	DIC_reg
Compute Eigenvalues	eigen_val
Compute Eigenvectors	eigen_vec
Model fit	fit
Model fit for a Bayesian circular mixed-effects model	fit.bpnme
Model fit for a Bayesian circular regression model	fit.bpnr
Estimate the mode by finding the highest posterior density interval	hmode
Estimate the mode by finding the highest posterior density interval	hmodeC
Find the highest density interval.	hmodeci
Find the highest density interval of a circular variable	hmodeciC
Compute the 95 percent HPD of a vector of linear data	hpd_est
Compute the 95 percent HPD of a vector of circular data	hpd_est_circ
Compute the Likelihood of the PN distribution (mixed effects)	lik_me
Compute the Likelihood of the PN distribution (regression)	lik_reg
The geometry of human knowledge of navigation space.	Maps
Compute the mean of a vector of circular data	mean_circ
Create model matrices for a circular mixed-effects regression model	mmme
Create model matrices circular regression	mmr
Compute the mode of a vector of linear data	mode_est
Compute the mode of a vector of circular data	mode_est_circ
Phase differences in hand flexion-extension movements.	Motor
Sample from a multivariate normal distribution	mvrnorm_arma_eigen
A Gibbs sampler for a projected normal mixed-effects model	pnme
A Gibbs sampler for a projected normal regression model	pnr
Print output from a Bayesian circular mixed-effects model	print.bpnme
Print output from a Bayesian circular regression model	print.bpnr
Compute a mean resultant length	rho
Compute the mean resultant length of a vector of circular data	rho_circ
Compute the standard deviation of a vector of circular data	sd_circ
A slice sampler for the latent lengths r	slice_rcpp
Compute a mean direction	theta_bar
Traceplots	traceplot
Traceplots for a Bayesian circular mixed-effects model	traceplot.bpnme
Traceplots for a Bayesian circular regression model	traceplot.bpnr

Package: bpnreg 2.0.3

bpnreg: Bayesian Projected Normal Regression Models for Circular Data

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