Code supplement for "Unsupervised multimodal modeling of cognitive and brain health trajectories for early dementia prediction"
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Updated
May 22, 2024 - Python
Code supplement for "Unsupervised multimodal modeling of cognitive and brain health trajectories for early dementia prediction"
GH pages repository to host all tutorial scripts as websites for sharing (PDF/HTML formats).
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
Mixture regression models for NumPyro.
CRAN Task View: Cluster Analysis & Finite 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).
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.
Trinomial mixture models in Stan, for fitting to compositional data with 0s
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).
Bayesian Statistics MOOC by Coursera - Solutions in Python
Undergraduate honours project exploring learning Gaussian Mixture Models with negative components.
Tools for Analyzing Finite Mixture Models
R Package With Shiny App to Perform and Visualize Clustering of Data via Mixtures of Multivariate Gaussian Model
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.
A simple but generic implementation of Expectation Maximization algorithms to fit mixture models.
Model-based clustering with vine copulas
A mixture models package including GMM, Skew GMM, GMN and DGMM
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.
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.
A Python package for computing NPMLE of mixture of regression
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