negative-binomial

Companion repository to Lause, Berens & Kobak (2021): "Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data", Genome Biology

Probabilistic outlier identification for bulk RNA sequencing data

NewWave

Various Fortran codes

Code for fitting a negative binomial distribution in Python

Negative binomial distributed pseudorandom numbers.

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

The DOTNB repository is a collection of code files that implement DOTNB across several programming languages. The DOTNB is the distribution for the Difference Of Two Negative Binomial distributions, i.e., Z=X-Y ~ DOTNB (λ_1,λ_2,p_1,p_2), where X ~ NB(λ_1,p_1 ) and Y ~ NB(λ_2,p_2 ).

Research code and reproducibility materials for a mobility-informed SIR model using subway ridership data, particle smoothing, and mobility-reduction scenarios to evaluate influenza transmission and the instantaneous reproduction number.

High-precision ribosome pause detection tool utilizing Negative Binomial modeling to optimize Z-scores and extract ML-ready contextual features from Ribo-seq data.

Create an iterator for generating pseudorandom numbers drawn from a negative binomial distribution.

Create an array containing pseudorandom numbers drawn from a negative binomial distribution.

DEGage is a novel model-based method for gene differential expression analysis between two groups of scRNA-seq count data. It employs a novel family of discrete distributions for describing the difference of two NB distributions (named DOTNB).

RNS-Seq Count Model Explorer

The DEGage2 package works to identify differentially expressed genes (DEGs) on bulk RNA-seq data through utilization of DOTNB

Regional Consistency Probability for Single-Arm Multi-Regional Clinical Trials

Slides sobre modelos de regressão poisson e binomial negativa inflacionadas de zeros

This project analyzes species observations from the National Park Service’s biodiversity database to examine how species category, nativeness, and abundance classification influence richness across U.S. National Parks.

Optional presentation for the "Sistemi Complessi" course.

Applied Multiple Systems Estimation (MSE) econometric framework in R & Python reconstructing hidden populations of modern slavery victims across UK, Netherlands, New Orleans, and Kosovo testbeds via Negative Binomial and Quasi-Poisson GLMs.