bpnreg2.0.3 package

Bayesian Projected Normal Regression Models for Circular Data

anyBars

anyBars function from lme4 package

b_samp

Sample subject specific random effects

betaBlock

Sample fixed effect coefficients

BFc.bpnme

Bayes Factors for a Bayesian circular mixed-effects model

BFc.bpnr

Bayes Factors for a Bayesian circular regression model

BFc

Bayes Factors

bpnme

Fit a Bayesian circular mixed-effects model

bpnr

Fit a Bayesian circular regression model

bpnreg-package

bpnreg: A package to analyze Bayesian projected normal circular regres...

cat_check

Check whether a variable is categorical

circ_coef

Compute circular coefficients from linear coefficients

circ_coef_rcpp

Compute circular coefficients

coef_circ.bpnme

Obtain the circular coefficients of a Bayesian circular mixed-effects ...

coef_circ.bpnr

Obtain the circular coefficients of a Bayesian circular regression mod...

coef_circ

Circular coefficients

coef_lin.bpnme

Obtain the linear coefficients of a Bayesian circular mixed-effects mo...

coef_lin.bpnr

Obtain the linear coefficients of a Bayesian circular regression model

coef_lin

Linear coefficients

coef_ran.bpnme

Obtain random effect variances of a Bayesian circular mixed-effects mo...

coef_ran

Random effect variances

Dbd

Compute utmu

DIC_reg

Compute Model Fit Measures Regression Model

eigen_val

Compute Eigenvalues

eigen_vec

Compute Eigenvectors

expandDoubleVerts

expandDoubleVerts function from lme4 package

findbars

findbars function from lme4 package

fit.bpnme

Model fit for a Bayesian circular mixed-effects model

fit.bpnr

Model fit for a Bayesian circular regression model

fit

Model fit

hmode

Estimate the mode by finding the highest posterior density interval

hmodeC

Estimate the mode by finding the highest posterior density interval

hmodeci

Find the highest density interval.

hmodeciC

Find the highest density interval of a circular variable

hpd_est

Compute the 95 percent HPD of a vector of linear data

hpd_est_circ

Compute the 95 percent HPD of a vector of circular data

isAnyArgBar

isAnyArgBar function from lme4 package

isBar

isBars function from lme4 package

lik_me

Compute the Likelihood of the PN distribution (mixed effects)

lik_reg

Compute the Likelihood of the PN distribution (regression)

mean_circ

Compute the mean of a vector of circular data

mmme

Create model matrices for a circular mixed-effects regression model

mmr

Create model matrices circular regression

mode_est

Compute the mode of a vector of linear data

mode_est_circ

Compute the mode of a vector of circular data

mvrnorm_arma_eigen

Sample from a multivariate normal distribution

nobars

nobars function from lme4 package

nobars_

nobars_ function from lme4 package

omega_samp

Sample precision matrix

pnme

A Gibbs sampler for a projected normal mixed-effects model

pnr

A Gibbs sampler for a projected normal regression model

print.bpnme

Print output from a Bayesian circular mixed-effects model

print.bpnr

Print output from a Bayesian circular regression model

reOnly

reOnly function from lme4 package

rho

Compute a mean resultant length

rho_circ

Compute the mean resultant length of a vector of circular data

RHSForm-set

RHSForm function from lme4 package

RHSForm

RHSForm function from lme4 package

safeDeparse

reOnly function from lme4 package

sd_circ

Compute the standard deviation of a vector of circular data

slice_rcpp

A slice sampler for the latent lengths r

subbars

subbars function from lme4 package

summe

Compute summary and model fit statistics for the circular mixed-effect...

sumr

Compute summary and model fit statistics for the circular regression m...

theta_bar

Compute a mean direction

traceplot.bpnme

Traceplots for a Bayesian circular mixed-effects model

traceplot.bpnr

Traceplots for a Bayesian circular regression model

traceplot

Traceplots

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>.

  • Maintainer: Jolien Cremers
  • License: GPL-3
  • Last published: 2024-01-15