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) <doi:10.48550/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).
Version: 1.1.0 Imports: stats, graphics, grDevices, Rcpp (≥ 0.12.3), salso, mvtnorm, mcclust, GGally, bayesplot, Rdpack LinkingTo: Rcpp, RcppArmadillo Suggests: dendextend, ggdendro, ggplot2, jpeg Published: 2021-07-23 DOI: 10.32614/CRAN.package.AntMAN Author: Priscilla Ong [aut, edt], Raffaele Argiento [aut], Bruno Bodin [aut, cre], Maria De Iorio [aut] Maintainer: Bruno Bodin <bruno.bodin at yale-nus.edu.sg> License: MIT + file LICENSE URL: https://github.com/bbodin/AntMAN NeedsCompilation: yes CRAN checks: AntMAN results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=AntMAN to link to this page.
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