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.

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

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

Bayesian Statistics MOOC by Coursera - Solutions in Python

Tools for Analyzing Finite Mixture Models

Tidy Tools for Visualizing 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 toolbox for inference of mixture models

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).

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

Model-based clustering with vine copulas

Projects & teaching materials.

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.

PySATL module providing tools for working with mixtures of distributions. In particular, it allows you to evaluate their parameters.

No description provided.

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

Functional Latent datA Models for clusterING heterogeneOus curveS

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

Codebase for federated mixture of experts.

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

CRAN Task View: Cluster Analysis & Finite Mixture Models

Differentiable Probabilistic Models

Mixture regression models for NumPyro.

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

A Python package for computing NPMLE of mixture of regression

Economic preference clustering analysis using generative and deep learning models, including Gaussian Mixture Models (GMM), Wishart Mixture Models (WMM), and Variational Deep Embedding (VaDE).

Adaptive quantum networks in practice: superposed graph topologies and operator-space spatialization, with reproducible hardware-relevant demos and figures.

News Article Clustering Using Unsupervised Machine Learning Algorithms

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.

Spring 2021 Machine Learning (CS 181) Homework 5