glmmrBase-package {glmmrBase}R Documentation

Generalised Linear Mixed Models in R

Description

Specification, analysis, simulation, and fitting of generalised linear mixed models. Includes Markov Chain Monte Carlo Maximum likelihood and Laplace approximation model fitting for a range of models, non-linear fixed effect specifications, a wide range of flexible covariance functions that can be combined arbitrarily, robust and bias-corrected standard error estimation, power calculation, data simulation, and more. See <https://samuel-watson.github.io/glmmr-web/> for a detailed manual. glmmrBase provides functions for specifying, analysing, fitting, and simulating mixed models including linear, generalised linear, and models non-linear in fixed effects..

Differences between glmmrBase and lme4 and related packages.

glmmrBase is intended to be a broad package to support statistical work with generalised linear mixed models. While there are Laplace Approximation methods in the package, it does not intend to replace or supplant popular mixed model packages like lme4. Rather it provides broader functionality around simulation and analysis methods, and a range of model fitting algorithms not found in other mixed model packages. The key features are:

Package development

The package is still in development and there may still be bugs and errors. While we do not expect the general user interface to change there may be changes to the underlying library as well as new additions and functionality.

Author(s)

Sam Watson [aut, cre]

Maintainer: NA


[Package glmmrBase version 0.9.2 Index]