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
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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:

Conda:

cpp

5.61 score 5 stars 3 packages 4 scripts 832 downloads 17 exports 7 dependencies

Last updated from:02305ad4d6. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK290
linux-devel-x86_64OK330
source / vignettesOK424
linux-release-arm64OK292
linux-release-x86_64OK328
macos-release-arm64OK249
macos-release-x86_64OK382
macos-oldrel-arm64OK197
macos-oldrel-x86_64OK414
windows-develOK322
windows-releaseOK398
windows-oldrelOK302
wasm-releaseOK210

Exports:BetaCovariancecross_dfhessian_from_formulahsgp_rescalelme4_to_glmmrmatch_rowsmcml_glmermcml_lmerMeanFunctionmesh_helperModelneldernest_dfprogress_barQuantilesetParallel

Dependencies:BHlatticeMatrixR6RcppRcppEigenRcppParallel

Readme and manuals

Help Manual

Help pageTopics
Generalised Linear Mixed Models in RglmmrBase-package glmmrBase
Beta distribution declarationBeta
Extracts fixed effect coefficients from a mcml objectcoef.mcml
Extracts coefficients from a Model objectcoef.Model
Fixed effect confidence intervals for a `mcml` objectconfint.mcml
R6 Class representing a covariance function and dataCovariance
Generate crossed block structurecross_df
Generates all the orderings of acycles
Exponential distribution declarationexponential
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` objectfitted.mcml
Extract or generate fitted values from a `Model` objectfitted.Model
Extracts the fixed effect estimatesfixed.effects
Extracts the formula from a `mcml` object.formula.mcml
Extracts the formula from a `Model` objectformula.Model
Automatic differentiation of formulaehessian_from_formula
Rescales data to [-1,1]hsgp_rescale
Map lme4 formula to glmmrBase formulalme4_to_glmmr
Extracts the log-likelihood from an mcml objectlogLik.mcml
Extracts the log-likelihood from an mcml objectlogLik.Model
Generate matrix mapping between data framesmatch_rows
lme4 style generlized linear mixed modelmcml_glmer
lme4 style linear mixed modelmcml_lmer
Returns the file name and type for MCNR functionmcnr_family
For the generalised linear mixed modelMeanFunction
Generates the required mesh data from fmesher for SPDE approximationsmesh_helper
A GLMM ModelModel
Generates a block experimental structure using Nelder's formulanelder
Generate nested block structurenest_df
Predict from a `mcml` objectpredict.mcml
Generate predictions at new values from a `Model` objectpredict.Model
Prints an mcml fit outputprint.mcml
Generates a progress barprogress_bar
Family declaration to support quantile regressionQuantile
Extracts the random effect estimatesrandom.effects
Residuals method for a `mcml` objectresiduals.mcml
Extract residuals from a `Model` objectresiduals.Model
Salamanders dataSalamanders
Disable or enable parallelised computingsetParallel
Simulated data from a geospatial study with continuous outcomesSimGeospat
Simulated data from a stepped-wedge cluster trialSimTrial
Summarises an mcml fit outputsummary.mcml
Summarizes a `Model` objectsummary.Model
Extract the Variance-Covariance matrix for a `mcml` objectvcov.mcml
Calculate Variance-Covariance matrix for a `Model` objectvcov.Model