Anthology of Mixture Analysis Tools
Return the clustering matrix
Return the co-clustering matrix
Returns an example of AM_mcmc_fit output produced by the multivariat...
Returns an example of AM_mcmc_fit output produced by the multivariat...
Returns an example of AM_mcmc_fit output produced by the univariate ...
Returns an example of AM_mcmc_fit output produced by the univariate ...
compute the hyperparameters of an Normal-Inverse-Gamma distribution us...
Extract values within a AM_mcmc_output object
Given that the prior on M is a dirac delta, find the hyperpar...
Given that the prior on M is a Negative Binomial, find the hy...
Given that the prior on M is a shifted Poisson, find the hype...
S3 class AM_mcmc_configuration
Performs a Gibbs sampling
S3 class AM_mcmc_output
MCMC Parameters
Performs a Gibbs sampling reusing previous configuration
S3 class AM_mix_components_prior
Generate a configuration object that contains a Point mass prior
Generate a configuration object for a Shifted Negative Binomial prior ...
Generate a configuration object for a Poisson prior on the number of m...
S3 class AM_mix_hyperparams
multivariate Bernoulli mixture hyperparameters (Latent Class Analysis)
multivariate Normal mixture hyperparameters
univariate Normal mixture hyperparameters
univariate Poisson mixture hyperparameters
S3 class AM_mix_weights_prior
specify a prior on the hyperparameter for the Dirichlet mixtu...
Plot the Autocorrelation function
Plot the density of variables from AM_mcmc_output object
Visualise the cluster frequency plot for the multivariate bernoulli mo...
Plot AM_mcmc_output scatterplot matrix
Plot the probability mass function of variables from AM_mcmc_output ...
Plot the Similarity Matrix
Plot traces of variables from an AM_mcmc_output object
Plot posterior interval estimates obtained from MCMC draws
S3 class AM_prior
Computes the prior on the number of clusters
computes the prior number of clusters
Computes the prior number of clusters
Sequentially Allocated Latent Structure Optimisation
AM_sample_multibin
AM_sample_multinorm
AM_sample_uninorm
AM_sample_unipois
AntMAN: A package for fitting finite Bayesian Mixture models with a ra...
Compute the logarithm of the absolute value of the generalized Sriling...
Compute stirling ricor log
Internal function used to compute the MCMC Error as a batch mean.
IAM_mcmc_neff MCMC Parameters
Compute the value V(n,k), needed to caclulate the eppf of a Finite Dir...
Compute the value V(n,k), needed to caclulate the eppf of a Finite Dir...
Compute the value V(n,k), needed to caclulate the eppf of a Finite Dir...
Internal function that produces a string from a list of values
plot AM_mcmc_output
plot AM_prior
summary information of the AM_mcmc_configuration object
summary information of the AM_mcmc_output object
summary information of the AM_mix_components_prior object
summary information of the AM_mix_hyperparams object
summary information of the AM_mix_weights_prior object
summary information of the AM_prior object
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).