hpd_est

Compute the 95 percent HPD of a vector of linear 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>.

Jolien Cremers

bpnreg

Bayesian Projected Normal Regression Models for Circular Data

hpd_est function

<dl><dt>x</dt>
<dd>a vector of linear data</dd></dl>

Arguments

Compute the 95 percent HPD of a vector of linear data — hpd_est

<dl>

<dt>x</dt>
<dd>a vector of linear data</dd>

</dl>

hpd_est: Compute the 95 percent HPD of a vector of linear data

Description

Usage

Arguments

Examples