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 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||Raffaele Argiento [aut], Bruno Bodin [aut, cre], Maria De Iorio [aut]|
|Maintainer||Bruno Bodin <[email protected]>|
|License||MIT + file LICENSE|
|Package repository||View on CRAN|
Install the latest version of this package by entering the following in R:
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.