IMIFA - Infinite Mixtures of Infinite Factor Analysers and Related Models
Provides flexible Bayesian estimation of Infinite Mixtures of Infinite Factor Analysers and related models, for nonparametrically clustering high-dimensional data, introduced by Murphy et al. (2020) <doi:10.1214/19-BA1179>. The IMIFA model conducts Bayesian nonparametric model-based clustering with factor analytic covariance structures without recourse to model selection criteria to choose the number of clusters or cluster-specific latent factors, mostly via efficient Gibbs updates. Model-specific diagnostic tools are also provided, as well as many options for plotting results, conducting posterior inference on parameters of interest, posterior predictive checking, and quantifying uncertainty.
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bayesian-nonparametricsdimension-reductionfactor-analysisgaussian-mixture-modelmodel-based-clustering
5.92 score 9 stars 1 dependents 62 scripts 395 downloadsMoEClust - Gaussian Parsimonious Clustering Models with Covariates and a Noise Component
Clustering via parsimonious Gaussian Mixtures of Experts using the MoEClust models introduced by Murphy and Murphy (2020) <doi:10.1007/s11634-019-00373-8>. This package fits finite Gaussian mixture models with a formula interface for supplying gating and/or expert network covariates using a range of parsimonious covariance parameterisations from the GPCM family via the EM/CEM algorithm. Visualisation of the results of such models using generalised pairs plots and the inclusion of an additional noise component is also facilitated. A greedy forward stepwise search algorithm is provided for identifying the optimal model in terms of the number of components, the GPCM covariance parameterisation, and the subsets of gating/expert network covariates.
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gaussian-mixture-modelsmixture-of-expertsmodel-based-clustering
5.66 score 7 stars 1 dependents 44 scripts 439 downloadsMEDseq - Mixtures of Exponential-Distance Models with Covariates
Implements a model-based clustering method for categorical life-course sequences relying on mixtures of exponential-distance models introduced by Murphy et al. (2021) <doi:10.1111/rssa.12712>. A range of flexible precision parameter settings corresponding to weighted generalisations of the Hamming distance metric are considered, along with the potential inclusion of a noise component. Gating covariates can be supplied in order to relate sequences to baseline characteristics and sampling weights are also accommodated. The models are fitted using the EM algorithm and tools for visualising the results are also provided.
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distance-based-clusteringmixture-of-expertsmodel-based-clusteringsequence-analysis
4.80 score 5 stars 25 scripts 173 downloads