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MuMIn (version 1.48.11)

Multi-Model Inference

Description

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.

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Version

Install

install.packages('MuMIn')

Monthly Downloads

23,138

Version

1.48.11

License

GPL-2

Maintainer

Kamil Barto

Last Published

April 1st, 2025

Functions in MuMIn (1.48.11)

plot.model.selection

Visualize model selection table
bootWeights

Bootstrap model weights
Weights

Akaike weights
arm.glm

Adaptive Regression by Mixing
coefplot

Plot model coefficients
get.models

Retrieve models from selection table
AICc

Second-order Akaike Information Criterion
loo

Leave-one-out cross-validation
cos2Weights

Cos-squared model weights
BGWeights

Bates-Granger minimal variance model weights
jackknifeWeights

Jackknifed model weights
sw

Per-variable sum of model weights
model.avg

Model averaging
Formula manipulation

Manipulate model formulas
Beetle

Flour beetle mortality data
MuMIn-models

List of supported models
model.sel

model selection table
par.avg

Parameter averaging
pdredge

Automated model selection using parallel computation
stdize

Standardize data
QAIC

Quasi AIC or AICc
QIC

QIC and quasi-Likelihood for GEE
model.selection.object

Description of Model Selection Objects
Cement

Cement hardening data
nested

Identify nested models
GPA

Grade Point Average data
r.squaredGLMM

Pseudo-R-squared for Generalized Mixed-Effect models
r.squaredLR

Likelihood-ratio based pseudo-R-squared
subset.model.selection

Subsetting model selection table
Information criteria

Various information criteria
MuMIn-package

Multi-model inference
dredge

Automated model selection
stackingWeights

Stacking model weights
std.coef

Standardized model coefficients
exprApply

Apply a function to calls inside an expression
merge.model.selection

Combine model selection tables
Model utilities

Model utility functions
updateable

Make a function return updateable result
predict.averaging

Predict method for averaged models