internal

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

Dbd function

<dl><dt>t</dt>
<dd>current outcome value</dd>
<dt>mu1</dt>
<dd>current predicted linear mean of the first component</dd>
<dt>mu2</dt>
<dd>current predicted linear mean of the second component</dd></dl>

Arguments

Compute utmu — Dbd

<dl>

<dt>t</dt>
<dd>current outcome value</dd>


<dt>mu1</dt>
<dd>current predicted linear mean of the first component</dd>


<dt>mu2</dt>
<dd>current predicted linear mean of the second component</dd>

</dl>

Dbd: Compute utmu

Description

Usage

Arguments