There are 1 repository under count-data topic.
OUTRIDER: OUTlier in RNA-seq fInDER is an R-based framework to find aberrantly expressed genes in RNA-seq data
R Package With Shiny App 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 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
Text Statistics For Node Streams
Semiparametric and parametric estimation and bootstrapping of integer-valued autoregressive (INAR) models.
Mixed Poisson Regression for Overdispersed Count Data
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 | UPN Veteran Jatim | Website-Bot Register for Robotics community | Final Project
Mortality Rate Prediction based on Age and Cigarette Types in Canada using Generalized Linear Model for Count Data
Analysis of seasonal habitat-use patterns of Aldabra giant tortoises of Aldabra Atoll, Seychelles using temporary emigration models.
R package for model-based causal discovery for zero-inflated count data
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.
Infer network from count data within Snakemake using a sparse Poisson lognormal model (PLNnetwork)
Poisson-Regression und ihre Leiden
Davis, K. L., Silverman, E. D., Sussman, A. L., Wilson, R. R., Zipkin, E. F. (2022). Errors in aerial survey count data: Identifying pitfalls and solutions. Ecology and Evolution 12:e8733.