Package index • MuMIn

All functions
`AICc()`	Second-order Akaike Information Criterion
`BGWeights()`	Bates-Granger minimal variance model weights
`Cp()` `ICOMP()` `CAICF()` `DIC()`	Various information criteria
`MuMIn-package` `MuMIn`	Multi-model inference
`QAIC()` `QAICc()`	Quasi AIC or AICc
`QIC()` `QICu()` `quasiLik()`	QIC and quasi-Likelihood for GEE
`Weights()` `Weights<-`()	Akaike weights
`arm.glm()` `armWeights()`	Adaptive Regression by Mixing
`bootWeights()`	Bootstrap model weights
`coefplot()` `plot(<averaging>)`	Plot model coefficients
`cos2Weights()`	Cos-squared model weights
`Beetle`	Flour beetle mortality data
`Cement`	Cement hardening data
`GPA`	Grade Point Average data
`dredge()` `print(<model.selection>)`	Automated model selection
`exprApply()`	Apply a function to calls inside an expression
`get.models()`	Retrieve models from selection table
`jackknifeWeights()`	Jackknifed model weights
`loo()`	Leave-one-out cross-validation
`simplify.formula()` `expand.formula()`	Manipulate model formulas
`merge(<model.selection>)` `rbind(<model.selection>)`	Combine model selection tables
`coeffs()` `getAllTerms()` `coefTable()` `get.response()` `model.names()` `.get.extras()`	Model utility functions
`model.avg()`	Model averaging
`model.sel()` `model.sel<-`()	model selection table
`model.selection.object`	Description of Model Selection Objects
`nested()`	Identify nested models
`par.avg()`	Parameter averaging
`pdredge()`	Automated model selection using parallel computation
`plot(<model.selection>)`	Visualize model selection table
`predict(<averaging>)`	Predict method for averaged models
`r.squaredGLMM()`	Pseudo-R-squared for Generalized Mixed-Effect models
`r.squaredLR()` `null.fit()`	Likelihood-ratio based pseudo-R-squared
`stackingWeights()`	Stacking model weights
`std.coef()` `partial.sd()` `beta.weights()`	Standardized model coefficients
`stdize(<default>)` `stdize(<logical>)` `stdize(<data.frame>)` `stdize(<formula>)` `stdizeFit()`	Standardize data
`subset(<model.selection>)` `[`(<model.selection>) `[[`(<model.selection>)	Subsetting model selection table
`sw()` `importance()`	Per-variable sum of model weights
`MuMIn-models`	List of supported models
`updateable()` `get_call()` `uGamm()` `fitdistr2()`	Make a function return updateable result

Reference

All functions