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MuMIn (version 1.40.0)

Multi-Model Inference

Description

Model selection and model averaging based on information criteria (AICc and alike).

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Version

Install

install.packages('MuMIn')

Monthly Downloads

16,288

Version

1.40.0

License

GPL-2

Maintainer

Kamil Barto

Last Published

October 2nd, 2017

Functions in MuMIn (1.40.0)

bootWeights

Bootstrap model weights

cos2Weights

Cos-squared model weights

AICc

Second-order Akaike Information Criterion

BGWeights

Bates-Granger model weights

Information criteria

Various information criteria

MuMIn-package

Multi-model inference

QAIC

Quasi AIC or AICc

QIC

QIC and quasi-Likelihood for GEE

Weights

Akaike weights

arm.glm

Adaptive Regression by Mixing

Beetle

Flour beetle mortality data

exprApply

Apply a function to calls inside an expression

loo

Leave-one-out cross-validation

GPA

Grade Point Average data

dredge

Automated model selection

model.avg

Model averaging

model.sel

model selection table

stdize

Standardize data

subset.model.selection

Subsetting model selection table

Cement

Cement hardening data

merge.model.selection

Combine model selection tables

Model utilities

Model utility functions

stackingWeights

Stacking model weights

std.coef

Standardized model coefficients

get.models

Retrieve models from selection table

model.selection.object

Description of Model Selection Objects

nested

Identify nested models

importance

Relative variable importance

jackknifeWeights

Jackknifed model weights

plot.model.selection

Visualize model selection table

predict.averaging

Predict method for averaged models

MuMIn-models

List of supported models

updateable

Make a function return updateable result

Formula manipulation

Manipulate model formulas

par.avg

Parameter averaging

pdredge

Automated model selection using parallel computation

r.squaredGLMM

Pseudo-R-squared for Generalized Mixed-Effect models

r.squaredLR

Likelihood-ratio based pseudo-R-squared