Package: glmmrBase 1.4.1

Sam Watson

glmmrBase: Generalised Linear Mixed Models in R

Specification, analysis, simulation, and fitting of generalised linear mixed models. Includes Markov Chain Monte Carlo Maximum likelihood 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.

Authors:Sam Watson [aut, cre]

glmmrBase_1.4.1.tar.gz
glmmrBase_1.4.1.zip(r-4.7-x86_64)glmmrBase_1.4.1.zip(r-4.7-arm64)glmmrBase_1.4.1.zip(r-4.6-x86_64)glmmrBase_1.4.1.zip(r-4.6-arm64)glmmrBase_1.4.1.zip(r-4.5-x86_64)
glmmrBase_1.4.1.tgz(r-4.6-x86_64)glmmrBase_1.4.1.tgz(r-4.6-arm64)glmmrBase_1.4.1.tgz(r-4.5-x86_64)glmmrBase_1.4.1.tgz(r-4.5-arm64)
glmmrBase_1.4.1.tar.gz(r-4.7-arm64)glmmrBase_1.4.1.tar.gz(r-4.7-x86_64)glmmrBase_1.4.1.tar.gz(r-4.6-arm64)glmmrBase_1.4.1.tar.gz(r-4.6-x86_64)
glmmrBase_1.4.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
glmmrBase/json (API)

# Install 'glmmrBase' in R:

install.packages('glmmrBase', repos = c('https://samuel-watson.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/samuel-watson/glmmrbase/issues

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

Salamanders - Salamanders data
SimGeospat - Simulated data from a geospatial study with continuous outcomes
SimTrial - Simulated data from a stepped-wedge cluster trial

On CRAN:

cpp

5.50 score 5 stars 3 packages 5 scripts 628 downloads 17 exports 7 dependencies

Last updated from:02305ad4d6. Checks:15 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	296
linux-devel-x86_64	OK	275
source / vignettes	OK	481
linux-release-arm64	OK	296
linux-release-x86_64	OK	336
macos-release-arm64	OK	235
macos-release-x86_64	OK	613
macos-oldrel-arm64	OK	269
macos-oldrel-x86_64	OK	521
windows-devel-arm64	OK	489
windows-devel-x86_64	OK	332
windows-release-arm64	OK	485
windows-release-x86_64	OK	314
windows-oldrel-x86_64	OK	285
wasm-release	OK	178

Exports:Beta Covariance cross_df hessian_from_formula hsgp_rescale lme4_to_glmmr match_rows mcml_glmer mcml_lmer MeanFunction mesh_helper Model nelder nest_df progress_bar Quantile setParallel

Dependencies:BH lattice Matrix R6 Rcpp RcppEigen RcppParallel

Citation

Development and contributors

Readme and manuals

Help Manual

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