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bioc
glmGamPoi:Fit a Gamma-Poisson Generalized Linear Model
Fit linear models to overdispersed count data. The package can estimate the overdispersion and fit repeated models for matrix input. It is designed to handle large input datasets as they typically occur in single cell RNA-seq experiments.
Maintained by Constantin Ahlmann-Eltze. Last updated 12 days ago.
regressionrnaseqsoftwaresinglecellgamma-poissonglmnegative-binomial-regressionon-diskopenblascpp
111 stars 12.16 score 1.0k scripts 4 dependents
vpnsctl
mixpoissonreg:Mixed Poisson Regression for Overdispersed Count Data
Fits mixed Poisson regression models (Poisson-Inverse Gaussian or Negative-Binomial) on data sets with response variables being count data. The models can have varying precision parameter, where a linear regression structure (through a link function) is assumed to hold on the precision parameter. The Expectation-Maximization algorithm for both these models (Poisson Inverse Gaussian and Negative Binomial) is an important contribution of this package. Another important feature of this package is the set of functions to perform global and local influence analysis. See Barreto-Souza and Simas (2016) <doi:10.1007/s11222-015-9601-6> for further details.
Maintained by Alexandre B. Simas. Last updated 4 years ago.
count-datadiagnosticsinfluence-analysislocal-influencenegative-binomial-regressionpoisson-inverse-gaussian-regression
3 stars 5.44 score 23 scriptsbioc
NBAMSeq:Negative Binomial Additive Model for RNA-Seq Data
High-throughput sequencing experiments followed by differential expression analysis is a widely used approach to detect genomic biomarkers. A fundamental step in differential expression analysis is to model the association between gene counts and covariates of interest. NBAMSeq a flexible statistical model based on the generalized additive model and allows for information sharing across genes in variance estimation.
Maintained by Xu Ren. Last updated 5 months ago.
rnaseqdifferentialexpressiongeneexpressionsequencingcoveragedifferential-expressiongene-expressiongeneralized-additive-modelsgeneralized-linear-modelsnegative-binomial-regressionsplines
2 stars 4.78 score 2 scripts