mvrnorm_arma_eigen

Sample from a multivariate normal distribution

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>.

Jolien Cremers

bpnreg

Bayesian Projected Normal Regression Models for Circular Data

mvrnorm_arma_eigen function

<dl><dt>n</dt>
<dd>An integer indicating the number of samples to take.</dd>
<dt>mu</dt>
<dd>A mean vector.</dd>
<dt>sigma</dt>
<dd>A variance-covariance matrix.</dd></dl>

Arguments

Sample from a multivariate normal distribution — mvrnorm_arma_eigen

<dl>

<dt>n</dt>
<dd>An integer indicating the number of samples to take.</dd>


<dt>mu</dt>
<dd>A mean vector.</dd>


<dt>sigma</dt>
<dd>A variance-covariance matrix.</dd>

</dl>

mvrnorm_arma_eigen: Sample from a multivariate normal distribution

Description

Usage

Arguments