WinBUGS – A Bayesian modelling framework: Concepts, structure, and extensibility
Statistics and Computing
Multivariate mixtures of normals with unknown number of components
Statistics and Computing
Editorial: Advances in Mixture Models
Computational Statistics & Data Analysis
Univariate Bayesian nonparametric mixture modeling with unimodal kernels
Statistics and Computing
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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.