Model-based subclonal deconvolution from bulk sequencing.

R Package With Shiny App to Perform and Visualize Clustering of Count Data via Mixtures of Multivariate Poisson-log Normal Model

Model-based time series clustering using variational inference.

**Unsupervised-Learning**(with practice of PCA, ICA and Model-based Clustering)

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.

Gaussian Parsimonious Clustering Models with Gating and Expert Network Covariates

Unsupervised Learning

Infinite Mixtures of Infinite Factor Analysers

Mixtures of Exponential-Distance Models for Clustering Longitudinal Life-Course Sequences with Gating Covariates and Sampling Weights

This code is part of the "Comparison of K-Means and Model-Based Clustering methods for drill core pseudo-log generation based on X-Ray Fluorescence Data" written by researchers of the Directory of Geology and Mineral Resources from the Geological Survey of Brazil – CPRM.

This project is an extension of the Gaussian Mixture Regression (GMR) model to handel censored multivariate responses.

Python code to fit parsimonious Markov models

Model-Based Clustering and Variable Selection for Multivariate Count Data

EMMIX fits the data into the specified multivariate mixture models via the EM Algorithm.

Hierarchical, model-based, and density-based clustering in R and application to unsupervised country classification

A Predictive View of Bayesian Clustering

R & Python | Unsupervised Learning Project

VEV model from Mclust among 5 clustering algorithms has optimal performance and detected 8 distinct groups of users. Data was cleaned, standardized and feature-selected, PCA’s biplot, Ggplot, Radar plots, and parallel coordinate plots were applied for EDA.

Bayesian Specification of model-based clustering

Processing DNA Copy Number (CN) Data for Detection of CN Events