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glmmrBase (version 1.0.2)

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

640

Version

1.0.2

License

GPL (>= 2)

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Maintainer

Sam Watson

Last Published

July 22nd, 2025

Functions in glmmrBase (1.0.2)

SimGeospat

Simulated data from a geospatial study with continuous outcomes
Quantile

Family declaration to support quantile regression
MeanFunction

For the generalised linear mixed model
Beta

Beta distribution declaration
Covariance

R6 Class representing a covariance function and data
Model

A GLMM Model
SimTrial

Simulated data from a stepped-wedge cluster trial
coef.Model

Extracts coefficients from a Model object
coef.mcml

Extracts fixed effect coefficients from a mcml object
Salamanders

Salamanders data
family.mcml

Extracts the family from a `mcml` object.
confint.mcml

Fixed effect confidence intervals for a `mcml` object
fitted.Model

Extract or generate fitted values from a `Model` object
cross_df

Generate crossed block structure
fixed.effects

Extracts the fixed effect estimates
fitted.mcml

Fitted values from a `mcml` object
logLik.mcml

Extracts the log-likelihood from an mcml object
formula.mcml

Extracts the formula from a `mcml` object.
formula.Model

Extracts the formula from a `Model` object
match_rows

Generate matrix mapping between data frames
mcnr_family

Returns the file name and type for MCNR function
nelder

Generates a block experimental structure using Nelder's formula
predict.mcml

Predict from a `mcml` object
cycles

Generates all the orderings of a
family.Model

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

Automatic differentiation of formulae
glmmrBase-package

tools:::Rd_package_title("glmmrBase")
progress_bar

Generates a progress bar
setParallel

Disable or enable parallelised computing
summary.Model

Summarizes a `Model` object
random.effects

Extracts the random effect estimates
vcov.mcml

Extract the Variance-Covariance matrix for a `mcml` object
predict.Model

Generate predictions at new values from a `Model` object
nest_df

Generate nested block structure
mcml_glmer

lme4 style generlized linear mixed model
mcml_lmer

lme4 style linear mixed model
vcov.Model

Calculate Variance-Covariance matrix for a `Model` object
summary.mcml

Summarises an mcml fit output
print.mcml

Prints an mcml fit output
residuals.Model

Extract residuals from a `Model` object
residuals.mcml

Residuals method for a `mcml` object
logLik.Model

Extracts the log-likelihood from an mcml object
lme4_to_glmmr

Map lme4 formula to glmmrBase formula