A python package for penalized generalized linear models that supports fitting and model selection for structured, adaptive and non-convex penalties.

In this paper we discuss a class of models for time series of low count data based on the Generalized Linear Model (GLM) approach. Unlike the traditional Auto-Regressive Moving-Average (ARMA) models ...

Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie James T. Thorson, Mayumi L. Arimitsu, Taal Levi and Gretchen H. Roffler Ecology , pp. 1-9 (9 ...

Linear regression is commonly used in various literature. However, the linear models have limitations. The generalized linear models (GLMs) expand upon the linear regression to handle data such as ...

The generalized linear models (GLM) expand upon the linear regression to handle data such as binary and count data. In this workshop, participants will learn the fundamentals of GLMs and their ...

To fill this gap, we present a versatile R-workflow template that facilitates (Generalized) Linear (Mixed) Model analyses. The script guides users from data exploration through model formulation, ...

Understanding the General Linear Model is essential for conducting rigorous statistical analyses, making informed inferences about relationships between variables, and developing predictive models.

Sparse generalized linear model is useful in many fields. In the research, the researchers will learn sparse generalized linear model using different algorithms. The paper determines the better ...