A Python package following the scikit-learn API for model-based clustering and generalized mixture modeling (latent class/profile analysis) of continuous and categorical data. StepMix handles missing values through Full Information Maximum Likelihood (FIML) and provides multiple stepwise Expectation-Maximization (EM) estimation methods.

A simple but generic implementation of Expectation Maximization algorithms to fit mixture models.

Bayesian Statistics MOOC by Coursera - Solutions in Python

An R package for clustering longitudinal datasets in a standardized way, providing interfaces to various R packages for longitudinal clustering, and facilitating the rapid implementation and evaluation of new methods

Tidy Tools for Visualizing Mixture Models

Tools for Analyzing Finite Mixture Models

A toolbox for inference of mixture models

RMC-BestFit is a state-of-the-art Bayesian estimation and fitting software developed collaboratively by the USACE-RMC and ERDC-CHL.

A Bayesian uncertainty quantification toolbox for discrete and continuum numerical models of granular materials, developed by various projects of the University of Twente (NL), the Netherlands eScience Center (NL), University of Newcastle (AU), and Hiroshima University (JP).

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.

This project contains the code for the paper accepted at NeurIPS 2020 - Robust Meta-learning for Mixed Linear Regression with Small Batches.

Trinomial mixture models in Stan, for fitting to compositional data with 0s

Model-based clustering with vine copulas

Projects & teaching materials.

Functional Latent datA Models for clusterING heterogeneOus curveS

A mixture models package including GMM, Skew GMM, GMN and DGMM

Code supplement for "Unsupervised multimodal modeling of cognitive and brain health trajectories for early dementia prediction"

Clustering and segmentation of heterogeneous functional data (sequential data) with regime changes by mixture of Hidden Markov Model Regressions (MixFHMMR) and the EM algorithm

Differentiable Probabilistic Models