Anthology of Mixture Analysis Tools

Fits finite Bayesian mixture models with random number of component. The MCMC algorithm implemented is based on point processes as proposed by Argiento and De Iorio (2019) and offers a more computational efficient alternative to reversible jump. Different mixture kernels can be specified: univariate Gaussian, univariate Poisson, univariate binomial, multivariate Gaussian, 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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1.0 by Bruno Bodin, 3 months ago

Browse source code at

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

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports stats, graphics, grDevices, Rcpp, sdols, mvtnorm, mcclust

Suggests dendextend, ggdendro, ggplot2, jpeg

Linking to Rcpp, RcppArmadillo

See at CRAN