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glmmrBase (version 0.10.5)

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

589

Version

0.10.5

License

GPL (>= 2)

Issues

5

Pull Requests

0

Stars

4

Forks

0

Maintainer

Sam Watson

Last Published

September 7th, 2024

Functions in glmmrBase (0.10.5)

coef.mcml

Extracts fixed effect coefficients from a mcml object
MeanFunction

R6 Class representing a mean function/linear predictor
cycles

Generates all the orderings of a
Model

A GLMM Model
Quantile

Family declaration to support quantile regression
Covariance

R6 Class representing a covariance function and data
Beta

Beta distribution declaration
coef.Model

Extracts coefficients from a Model object
confint.mcml

Fixed effect confidence intervals for a `mcml` object
cross_df

Generate crossed block structure
family.Model

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

Extracts the formula from a `Model` object
fixed.effects

Extracts the fixed effect estimates
family.mcml

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

Fitted values from a `mcml` object
lme4_to_glmmr

Map lme4 formula to glmmrBase formula
formula.mcml

Extracts the formula from a `mcml` object.
hessian_from_formula

Automatic differentiation of formulae
fitted.Model

Extract or generate fitted values from a `Model` object
glmmrBase-package

tools:::Rd_package_title("glmmrBase")
nelder

Generates a block experimental structure using Nelder's formula
mcnr_family

Returns the file name and type for MCNR function
mcml_lmer

lme4 style linear mixed model
predict.Model

Generate predictions at new values from a `Model` object
predict.mcml

Predict from a `mcml` object
random.effects

Extracts the random effect estimates
logLik.mcml

Extracts the log-likelihood from an mcml object
logLik.Model

Extracts the log-likelihood from an mcml object
residuals.Model

Extract residuals from a `Model` object
nest_df

Generate nested block structure
setParallel

Disable or enable parallelised computing
residuals.mcml

Residuals method for a `mcml` object
ytest1

Data for model tests
yexample312c

Data for third example in Section 3.12 of JSS paper
print.mcml

Prints an mcml fit output
progress_bar

Generates a progress bar
summary.Model

Summarizes a `Model` object
vcov.mcml

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

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

Summarises an mcml fit output
match_rows

Generate matrix mapping between data frames
yexample312a

Data for first example in Section 3.12 of JSS paper
mcml_glmer

lme4 style generlized linear mixed model
yexample312b

Data for second example in Section 3.12 of JSS paper