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) <arXiv:1904.09733> 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 hyperparameters. 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).
Package details 


Author  Raffaele Argiento [aut], Bruno Bodin [aut, cre], Maria De Iorio [aut] 
Maintainer  Bruno Bodin <[email protected]> 
License  MIT + file LICENSE 
Version  1.0 
URL  https://github.com/bbodin/AntMAN 
Package repository  View on CRAN 
Installation 
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