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.

Understanding RNA-seq differential expression from Poisson to DESeq2, reproduced by simulation

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.

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 ).

Football prop forecasting engine. Post-competition review found the calibration layer was making forecasts worse, removing it is documented, measured, and enforced by tests.

纯 Python 标准库从零实现 RNA-seq 差异表达分析（归一化/离散度收缩/负二项 GLM/Wald/BH），与官方 DESeq2 对拍：log2FC r=0.9997、显著集 Jaccard=0.90。

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

RNS-Seq Count Model Explorer

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

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

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

Julia hurdle and zero-inflated negative-binomial models for sequencing counts (hurdle_nb, zinb), cross-checked against R pscl. Pre-release: the formal proofs its spec requires do not exist yet.

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).

Final project in Safety Management: analytics and predictive modeling for occupational incidents. Includes EDA, logistic regression, Poisson/Negative Binomial with overdispersion checks, ROC/AUC, and prediction exercises.