A new R package for Bayesian estimation of multivariate normal mixtures allowing for selection of the number of components and interval-censored data

An R package mixAK is introduced which implements routines for a semiparametric density estimation through normal mixtures using the Markov chain Monte Carlo (MCMC) methodology. Besides producing the MCMC output, the package computes posterior summary statistics for important characteristics of the fitted distribution or computes and visualizes the posterior predictive density. For the estimated models, penalized expected deviance (PED) and deviance information criterion (DIC) is directly computed which allows for a selection of mixture components. Additionally, multivariate right-, left- and interval-censored observations are allowed. For univariate problems, the reversible jump MCMC algorithm has been implemented and can be used for a joint estimation of the mixture parameters and the number of mixture components. The core MCMC routines have been implemented in C++ and linked to R to ensure a reasonable computational speed. We briefly review the implemented algorithms and illustrate the use of the package on three real examples of different complexity.