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) <arXiv:1904.09733> 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).
|Author||Priscilla Ong [aut, edt], Raffaele Argiento [aut], Bruno Bodin [aut, cre], Maria De Iorio [aut]|
|Maintainer||Bruno Bodin <email@example.com>|
|License||MIT + file LICENSE|
|Package repository||View on CRAN|
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