Exponential Stochastic Block Model for Interaction Lengths | CRAN
R package for fitting stochastic blockmodels to continuous-time dynamic newtorks, with the purpose of modelling the length of interactions and non-interactions over time between a set of nodes. The framework relies on a clustering structure on the nodes, where two nodes belonging to the same latent group tend to create interactions and non-interactions of similar lengths.
Mixtures of Gaussian Graphical Models | CRAN
R package implementing mixtures of Gaussian graphical models for model-based clustering with sparse covariance and concentration matrices. Model fitting is carried out by means of a structural-EM algorithm for parameter estimation and graph structure search.
Latent Space Models for Multivariate Networks | CRAN
R package for latent space models for multivariate networks. The model is defined within a Bayesian hierarchical framework, where it is assumed that the probability of observing an arc between any two nodes is inversely related to their distance in a low-dimensional latent space. Inference is carried out via MCMC algorithm.
Variable Selection for Latent Class Analysis | CRAN
R package implementing variable selection for latent class analysis for model-based clustering of multivariate categorical data. The package provides a general framework for selecting the subset of variables with relevant clustering information and discard those that are redundant and/or not informative.
Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation | CRAN
R package for Gaussian finite mixture modeling for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualization, and resampling-based inference.