R Package With Shiny App to Perform and Visualize Clustering of Count Data via Mixtures of Multivariate Poisson-log Normal Model
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Updated
Jan 17, 2024 - R
R Package With Shiny App to Perform and Visualize Clustering of Count Data via Mixtures of Multivariate Poisson-log Normal 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.
Unsupervised Learning
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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.
EMMIX fits the data into the specified multivariate mixture models via the EM Algorithm.
A Predictive View of Bayesian Clustering
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.
Processing DNA Copy Number (CN) Data for Detection of CN Events
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