vm_dpm: Bayesian inference for Dirichlet Process Mixture of Von Mises...

In keesmulder/circbayes: Bayesian Analysis of Circular Data

Description

Bayesian inference for Dirichlet Process Mixture of Von Mises distributions.

Usage

vm_dpm(th, g0Priors = c(0, 0, 1), alphaPriors = c(2, 4),
  niter = 1000, ...)

Arguments

th	Circular observations, either numeric in radians, or circular.
g0Priors	Prior for the base distribution, which is the conjugate to the von Mises posterior.
alphaPriors	Prior parameters (a, b) for the gamma prior on the Dirichlet process parameter α. Acts on the number of components.
niter	Number of iterations to perform MCMC for.
...	Further arguments passed to circglmbayes::fitbatmix.

Value

Object of type vm_dpm_mod.

Examples

vm_dpm(rvm(30, 2, 5))

keesmulder/circbayes documentation built on May 30, 2019, 2:04 p.m.