SRC-Stat Package for Fitting Double Hierarchical Generalized Linear Models

hglm: A Package for Fitting Hierarchical Generalized Linear Models

We present the hglm package for fitting hierarchical generalized linear models. It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the model.

Double hierarchical generalized linear models

We propose a class of double hierarchical generalized linear models in which random effects can be specified for both the mean and dispersion. Heteroscedasticity between clusters can be modelled by introducing random effects in the dispersion model, as is heterogeneity between clusters in the mean model.This class will, among other things, enable models with heavy-tailed distributions to be exp...

KernGPLM – A Package for Kernel-Based Fitting of Generalized Partial Linear and Additive Models

In many cases statisticians are not only required to provide optimal fits or classification results but also to interpret and visualize the fitted curves or discriminant rules. A main issue here is to explain in what way the explanatory variables impact the resulting fit. The R package KernGPLM (currently under development) implements semiparametric extensions to the generalized linear regressi...

Technical note: an R package for fitting generalized linear mixed models in animal breeding.

Mixed models have been used extensively in quantitative genetics to study continuous and discrete traits. A standard quantitative genetic model proposes that the effects of levels of some random factor (e.g., sire) are correlated accordingly with their relationships. For this reason, routines for mixed models available in standard packages cannot be used for genetic analysis. The pedigreemm pac...

Fitting Generalized Linear Mixed Models For Point-Referenced Spatial Data