glmGamPoi: fitting Gamma-Poisson generalized linear models on single cell count data

Bayesian paradigm for analysing count data in longitudina studies using Poisson-generalized log-gamma model

In analyzing longitudinal data with counted responses, normal distribution is usually used for distribution of the random efffects. However, in some applications random effects may not be normally distributed. Misspecification of this distribution may cause reduction of efficiency of estimators. In this paper, a generalized log-gamma distribution is used for the random effects which includes th...

Gamma generalized linear models for pharmacokinetic data.

This article considers the modeling of single-dose pharmacokinetic data. Traditionally, so-called compartmental models have been used to analyze such data. Unfortunately, the mean function of such models are sums of exponentials for which inference and computation may not be straightforward. We present an alternative to these models based on generalized linear models, for which desirable statis...

Fitting Com-Poisson Mixtures to Bimodal Count Data

Bi-modal truncated count distributions are frequently observed in aggregate surveys and ratings when respondents are mixed in their opinion. They also arise in censored count data, where the highest category might create an additional mode. The Poisson distribution is the most common distribution for fitting count data and can be modified to achieve mixtures of truncated Poisson distributions. ...

Fitting Generalized Linear Mixed Models For Point-Referenced Spatial Data

Fitting additive Poisson models

This paper describes how to fit an additive Poisson model using standard software. It is illustrated with SAS code, but can be similarly used for other software packages.