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Negative binomial mixed models for analyzing microbiome count data

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Recent advances in next-generation sequencing (NGS) technology enable researchers to collect a large volume of metagenomic sequencing data. These data provide valuable resources for investigating interactions between the microbiome and host environmental/clinical factors. |

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The negative binomial-weighted Lindley distribution

The Binomial Probability Distribution

Measurement error effect on the power of control chart for zero truncated negative binomial distribution (ZTNBD)

Statistical analysis of variability in TnSeq data across conditions using zero-inflated negative binomial regression

Subject level clustering using a negative binomial model for small transcriptomic studies

QNB: Differential RNA methylation analysis for count-based small-sample sequencing data with a quad-negative binomial model

NBLDA: Negative binomial linear discriminant analysis for RNA-Seq data

NBZIMM: Negative binomial and zero‑infated mixed models, with application to microbiome/metagenomics data analysis

Negative binomial mixed models for analyzing microbiome count data

Probability Examples c-5 Discrete Distributions