Anthology of Mixture Analysis Tools

Fits finite Bayesian mixture models with a random number of components. The MCMC algorithm implemented is based on point processes as proposed by Argiento and De Iorio (2019) and offers a more computationally efficient alternative to reversible jump. Different mixture kernels can be specified: univariate Gaussian, multivariate Gaussian, univariate Poisson, and multivariate Bernoulli (latent class analysis). For the parameters characterising the mixture kernel, we specify conjugate priors, with possibly user specified hyper-parameters. We allow for different choices for the prior on the number of components: shifted Poisson, negative binomial, and point masses (i.e. mixtures with fixed number of components).


Reference manual

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1.1.0 by Bruno Bodin, 2 months ago

Browse source code at

Authors: Priscilla Ong [aut, edt] , Raffaele Argiento [aut] , Bruno Bodin [aut, cre] , Maria De Iorio [aut]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports stats, graphics, grDevices, Rcpp, salso, mvtnorm, mcclust, GGally, bayesplot, Rdpack

Suggests dendextend, ggdendro, ggplot2, jpeg

Linking to Rcpp, RcppArmadillo

See at CRAN