A collection of Poisson lognormal models for multivariate count data analysis

OUTRIDER: OUTlier in RNA-seq fInDER is an R-based framework to find aberrantly expressed genes in RNA-seq data

rpact: Confirmatory Adaptive Clinical Trial Design and Analysis

📉 JavaScript Text Statistics that counts lines, words, chars, and spaces.

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

Analysing Capture Seq Count Data

Official Implementation of Paper "Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling" (ICML 2023)

R Package with Shiny App: Permit 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.

R package for flexible univariate count models based on renewal processes

Modeling correlated count data

Semiparametric and parametric estimation and bootstrapping of integer-valued autoregressive (INAR) models.

Text Statistics For Node Streams

Generation, estimation and testing of INteger Autoregressive models

ROSeq - A rank based approach to modeling gene expression with filtered and normalized read count matrix. Takes in the complete filtered and normalized read count matrix, the location of the two sub-populations and the number of cores to be used.

Web Programming Class | Final Project | Campus Assignment

R package for model-based causal discovery for zero-inflated count data

R package for statistical modeling with the Skellam distribution, supporting inference, random sampling, and regression for differences of independent Poisson counts.

Mixed Poisson Regression for Overdispersed Count Data

Geographically Weighted Negative Binomial Regression in Python - local spatial modeling for over dispersed count data.