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MuMIn (version 1.46.0)

Multi-Model Inference

Description

Tools for performing model selection and model averaging. Automated model selection through subsetting the maximum model, with optional constraints for model inclusion. Model parameter and prediction averaging 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

15,184

Version

1.46.0

License

GPL-2

Maintainer

Kamil Barto

Last Published

February 24th, 2022

Functions in MuMIn (1.46.0)

AICc

Second-order Akaike Information Criterion
Information criteria

Various information criteria
coefplot

Plot model coefficients
BGWeights

Bates-Granger minimal variance model weights
bootWeights

Bootstrap model weights
QAIC

Quasi AIC or AICc
MuMIn-package

Multi-model inference
Weights

Akaike weights
QIC

QIC and quasi-Likelihood for GEE
arm.glm

Adaptive Regression by Mixing
loo

Leave-one-out cross-validation
Cement

Cement hardening data
jackknifeWeights

Jackknifed model weights
plot.model.selection

Visualize model selection table
get.models

Retrieve models from selection table
predict.averaging

Predict method for averaged models
Beetle

Flour beetle mortality data
Formula manipulation

Manipulate model formulas
merge.model.selection

Combine model selection tables
Model utilities

Model utility functions
dredge

Automated model selection
model.selection.object

Description of Model Selection Objects
r.squaredLR

Likelihood-ratio based pseudo-R-squared
cos2Weights

Cos-squared model weights
model.sel

model selection table
GPA

Grade Point Average data
stdize

Standardize data
r.squaredGLMM

Pseudo-R-squared for Generalized Mixed-Effect models
nested

Identify nested models
updateable

Make a function return updateable result
model.avg

Model averaging
std.coef

Standardized model coefficients
stackingWeights

Stacking model weights
sw

Per-variable sum of model weights
MuMIn-models

List of supported models
subset.model.selection

Subsetting model selection table
par.avg

Parameter averaging
exprApply

Apply a function to calls inside an expression
pdredge

Automated model selection using parallel computation