# Generalised Linear Mixed Models in R

## Documentation for package ‘glmmrBase’ version 1.2.1

- DESCRIPTION file.

## Help Pages

glmmrBase-package | Generalised Linear Mixed Models in R
Beta | Beta distribution declaration
coef.mcml | Extracts fixed effect coefficients from a mcml object
coef.Model | Extracts coefficients from a Model object
confint.mcml | Fixed effect confidence intervals for a 'mcml' object
Covariance | R6 Class representing a covariance function and data
cross_df | Generate crossed block structure
cycles | Generates all the orderings of a
exponential | Exponential distribution declaration
family.mcml | Extracts the family from a 'mcml' object.
family.Model | Extracts the family from a 'Model' object. This information can also be accessed directly from the Model as 'Model$family'
fitted.mcml | Fitted values from a 'mcml' object
fitted.Model | Extract or generate fitted values from a 'Model' object
fixed.effects | Extracts the fixed effect estimates
formula.mcml | Extracts the formula from a 'mcml' object.
formula.Model | Extracts the formula from a 'Model' object
glmmrBase | Generalised Linear Mixed Models in R
hessian_from_formula | Automatic differentiation of formulae
hsgp_rescale | Rescales data to [-1,1]
lme4_to_glmmr | Map lme4 formula to glmmrBase formula
logLik.mcml | Extracts the log-likelihood from an mcml object
logLik.Model | Extracts the log-likelihood from an mcml object
match_rows | Generate matrix mapping between data frames
mcml_glmer | lme4 style generlized linear mixed model
mcml_lmer | lme4 style linear mixed model
mcnr_family | Returns the file name and type for MCNR function
MeanFunction | For the generalised linear mixed model
Model | A GLMM Model
nelder | Generates a block experimental structure using Nelder's formula
nest_df | Generate nested block structure
predict.mcml | Predict from a 'mcml' object
predict.Model | Generate predictions at new values from a 'Model' object
print.mcml | Prints an mcml fit output
progress_bar | Generates a progress bar
Quantile | Family declaration to support quantile regression
random.effects | Extracts the random effect estimates
residuals.mcml | Residuals method for a 'mcml' object
residuals.Model | Extract residuals from a 'Model' object
Salamanders | Salamanders data
setParallel | Disable or enable parallelised computing
SimGeospat | Simulated data from a geospatial study with continuous outcomes
SimTrial | Simulated data from a stepped-wedge cluster trial
summary.mcml | Summarises an mcml fit output
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