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

Code for fitting a negative binomial distribution in Python

Various Fortran codes

Negative binomial distributed pseudorandom numbers.

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

Statistical Analysis about cancer incidence in Modena hospital.

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

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

Methods to estimate Negative Binomial Distribution

Conducting a predictive data analysis to predict the future rental bike demands.

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 ).

We will extend the biclustering model to use Zero-Inflated Poisson (ZIP) and Zero-Inflated Negative Binomial (ZINB) distributions.

Optional presentation for the "Sistemi Complessi" course.

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).

Plots of how negative binomial distribution converges to Poisson distribution