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R Package That Can Simultaneously Perform Factor Analysis And Cluster Analysis Of Count Data Via Parsimonious Finite Mixtures of Multivariate Poisson-Log Normal Factor Analyzers. This Model Permits For Parsimonious Covariance Structures And Dimension Reduction, Thus Reducing The Number Of Free Parameters To Be Calculated.
This repository contains the code to reproduce all the results reported in the paper Unsupervised EM Initialization for Mixture Models: A Complex Network Driven Approach for Modeling Financial Time Series.
Repository where I keep all the assignments and the project developed in the scope of the Machine Learning discipline, lectured by Professor Diego Mesquita (FGV EMAp).
Clustering and segmentation of heteregeneous functional data (sequential data) by mixture of gaussian Hidden Markov Models (MixFHMMs) and the EM algorithm
This `R` tutorial automates the BCH two-step axiliary variable procedure (Bolk, Croon, Hagenaars, 2004) using the `MplusAutomation` package (Hallquist & Wiley, 2018) to estimate models and extract relevant parameters.
R Package to Perform Clustering of Three-way Count Data Using Mixtures of Matrix Variate Poisson-log Normal Model With Parameter Estimation via MCMC-EM, Variational Gaussian Approximations, or a Hybrid Approach Combining Both.