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Grog Mixture-Of-Agents (MoA) is a sophisticated chatbot framework that integrates multiple open-source models to deliver high-quality responses. It features a user-friendly web-based GUI, supports persistent chats, and allows topic management for seamless and organized interactions.
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 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).
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).
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 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.