A collection of Poisson lognormal models for multivariate count data analysis
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Updated
Oct 7, 2024 - R
A collection of Poisson lognormal models for multivariate count data analysis
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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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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.
Modeling correlated count data
Semiparametric and parametric estimation and bootstrapping of integer-valued autoregressive (INAR) models.
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R package for flexible univariate count models based on renewal processes
Mixed Poisson Regression for Overdispersed Count Data
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Generation, estimation and testing of INteger Autoregressive models
Mortality Rate Prediction based on Age and Cigarette Types in Canada using Generalized Linear Model for Count Data
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R/C code for Bayesian variable selection for Dirichlet-multinomial regression models. Accompany paper: Wadsworth et al. (2016). An Integrative Bayesian Dirichlet-Multinomial Regression Model for the Analysis of Taxonomic Abundances in Microbiome data. BMC Bioinformatics 18:94.
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