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glmmrBase (version 1.1.0)

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 for a detailed manual.

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Install

install.packages('glmmrBase')

Monthly Downloads

962

Version

1.1.0

License

GPL (>= 2)

Issues

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Repository

https://github.com/samuel-watson/glmmrBase

Maintainer

Sam Watson

Last Published

September 29th, 2025

Functions in glmmrBase (1.1.0)

Extracts fixed effect coefficients from a mcml object

R6 Class representing a covariance function and data

Salamanders data

Simulated data from a geospatial study with continuous outcomes

Beta distribution declaration

Family declaration to support quantile regression

Simulated data from a stepped-wedge cluster trial

Extracts coefficients from a Model object

For the generalised linear mixed model

Extracts the family from a `mcml` object.

Fixed effect confidence intervals for a `mcml` object

Extract or generate fitted values from a `Model` object

Generate crossed block structure

Extracts the fixed effect estimates

Fitted values from a `mcml` object

Generates all the orderings of a

Extracts the formula from a `mcml` object.

Extracts the family from a `Model` object. This information can also be accessed directly from the Model as `Model$family`

Extracts the formula from a `Model` object

Map lme4 formula to glmmrBase formula

Extracts the log-likelihood from an mcml object

residuals.Model

Extract residuals from a `Model` object

hessian_from_formula

Automatic differentiation of formulae

Extracts the random effect estimates

Returns the file name and type for MCNR function

Generate matrix mapping between data frames

Predict from a `mcml` object

Extract the Variance-Covariance matrix for a `mcml` object

Generate predictions at new values from a `Model` object

Calculate Variance-Covariance matrix for a `Model` object

Prints an mcml fit output

Rescales data to [-1,1]

Extracts the log-likelihood from an mcml object

Disable or enable parallelised computing

lme4 style linear mixed model

lme4 style generlized linear mixed model

Residuals method for a `mcml` object

glmmrBase-package

tools:::Rd_package_title("glmmrBase")

Generates a progress bar

Generates a block experimental structure using Nelder's formula

Summarises an mcml fit output

Generate nested block structure

Summarizes a `Model` object