Package: MuMIn 1.48.4

Kamil Bartoń

MuMIn: Multi-Model Inference

Tools for model selection and model averaging with support for a wide range of statistical models. Automated model selection through subsets of the maximum model, with optional constraints for model inclusion. Averaging of model parameters and predictions based on model weights derived from information criteria (AICc and alike) or custom model weighting schemes.

Authors:Kamil Bartoń [aut, cre]

MuMIn_1.48.4.tar.gz
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MuMIn_1.48.4.tgz(r-4.4-emscripten)MuMIn_1.48.4.tgz(r-4.3-emscripten)
MuMIn.pdf |MuMIn.html✨
MuMIn/json (API)
NEWS

# Install 'MuMIn' in R:
install.packages('MuMIn', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • Beetle - Flour beetle mortality data
  • Cement - Cement hardening data
  • GPA - Grade Point Average data

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

8.84 score 8 stars 27 packages 5.6k scripts 19k downloads 1.3k mentions 51 exports 4 dependencies

Last updated 9 months agofrom:1834bb90ba. Checks:2 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 20 2025
R-4.5-linuxOKFeb 20 2025

Exports:AICcarm.glmarmWeightsbeta.weightsBGWeightsbootWeightsCAICFcoeffscoefplotcoefTablecos2WeightsCpDICdredgeexpand.formulaexprApplyget_callget.modelsget.responsegetAllTermsICOMPimportancejackknifeWeightsloomodel.avgmodel.namesmodel.selmodel.sel<-nestednull.fitpar.avgpartial.sdpdredgepget.modelsQAICQAICcQICQICuquasiLikr.squaredGLMMr.squaredLRsimplify.formulastackingWeightsstd.coefstdizestdizeFitswuGammupdateableWeightsWeights<-

Dependencies:insightlatticeMatrixnlme

Readme and manuals

Help Manual

Help pageTopics
Multi-model inferenceMuMIn-package MuMIn
Second-order Akaike Information CriterionAICc
Adaptive Regression by Mixingarm.glm armWeights
Flour beetle mortality dataBeetle
Bates-Granger minimal variance model weightsBGWeights
Bootstrap model weightsbootWeights
Cement hardening dataCement
Plot model coefficientscoefplot plot.averaging
Cos-squared model weightscos2Weights
Automated model selectiondc dredge print.model.selection V
Apply a function to calls inside an expressionexprApply
Manipulate model formulasexpand.formula simplify.formula
Retrieve models from selection tableget.models pget.models
Grade Point Average dataGPA
Various information criteriaCAICF Cp DIC IC ICOMP Mallows' Cp
Jackknifed model weightsjackknifeWeights
Leave-one-out cross-validationloo loo.default loo.lm
Combine model selection tablesappend.model.selection merge.model.selection rbind.model.selection
Model utility functionscoeffs coefTable coefTable.averaging coefTable.default coefTable.gee coefTable.lme get.response getAllTerms getAllTerms.terms model.names MuMIn-model-utils tTable
Model averagingmodel.avg model.avg.default model.avg.model.selection print.averaging
model selection tablemod.sel model.sel model.sel.default model.sel.model.selection model.sel<-
Description of Model Selection Objectsmodel.selection.object
List of supported modelsMuMIn-models
Identify nested modelsnested
Parameter averagingpar.avg
Automated model selection using parallel computationpdredge
Visualize model selection tableplot.model.selection
Predict method for averaged modelspredict.averaging
Quasi AIC or AICcQAIC QAICc
QIC and quasi-Likelihood for GEEQIC QICu quasiLik
Pseudo-R-squared for Generalized Mixed-Effect modelsr.squaredGLMM r.squaredGLMM.merMod
Likelihood-ratio based pseudo-R-squarednull.fit r.squaredLR
Stacking model weightsstackingWeights
Standardized model coefficientsbeta.weights partial.sd std.coef
Standardize datastdize stdize.data.frame stdize.default stdize.formula stdize.logical stdizeFit
Subsetting model selection tablehas subset.model.selection [.model.selection [[.model.selection
Per-variable sum of model weightsimportance sum.of.weights sw
Make a function return updateable resultgamm-wrapper get_call MuMIn-gamm uGamm updateable updateable2
Akaike weightsWeights Weights<-