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MuMIn (version 1.47.1)

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

15,800

Version

1.47.1

License

GPL-2

Maintainer

Kamil Barto

Last Published

September 1st, 2022

Functions in MuMIn (1.47.1)

arm.glm

Adaptive Regression by Mixing
AICc

Second-order Akaike Information Criterion
loo

Leave-one-out cross-validation
Formula manipulation

Manipulate model formulas
exprApply

Apply a function to calls inside an expression
Information criteria

Various information criteria
get.models

Retrieve models from selection table
bootWeights

Bootstrap model weights
Cement

Cement hardening data
jackknifeWeights

Jackknifed model weights
MuMIn-package

Multi-model inference
Beetle

Flour beetle mortality data
GPA

Grade Point Average data
par.avg

Parameter averaging
plot.model.selection

Visualize model selection table
cos2Weights

Cos-squared model weights
coefplot

Plot model coefficients
Model utilities

Model utility functions
model.selection.object

Description of Model Selection Objects
model.sel

model selection table
nested

Identify nested models
dredge

Automated model selection
merge.model.selection

Combine model selection tables
pdredge

Automated model selection using parallel computation
stackingWeights

Stacking model weights
model.avg

Model averaging
updateable

Make a function return updateable result
std.coef

Standardized model coefficients
r.squaredGLMM

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

Per-variable sum of model weights
predict.averaging

Predict method for averaged models
MuMIn-models

List of supported models
r.squaredLR

Likelihood-ratio based pseudo-R-squared
stdize

Standardize data
subset.model.selection

Subsetting model selection table
QIC

QIC and quasi-Likelihood for GEE
BGWeights

Bates-Granger minimal variance model weights
QAIC

Quasi AIC or AICc
Weights

Akaike weights