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MuMIn (version 1.42.1)

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

20,265

Version

1.42.1

License

GPL-2

Maintainer

Last Published

July 23rd, 2018

Functions in MuMIn (1.42.1)

merge.model.selection

Combine model selection tables

Retrieve models from selection table

Pseudo-R-squared for Generalized Mixed-Effect models

Likelihood-ratio based pseudo-R-squared

model selection table

Automated model selection

Model averaging

Identify nested models

Model utilities

Model utility functions

model.selection.object

Description of Model Selection Objects

stackingWeights

Stacking model weights

Standardized model coefficients

plot.model.selection

Visualize model selection table

Parameter averaging

Automated model selection using parallel computation

Standardize data

subset.model.selection

Subsetting model selection table

predict.averaging

Predict method for averaged models

List of supported models

Make a function return updateable result

Information criteria

Various information criteria

Quasi AIC or AICc

Bates-Granger model weights

Adaptive Regression by Mixing

Second-order Akaike Information Criterion

Multi-model inference

QIC and quasi-Likelihood for GEE

Flour beetle mortality data

Formula manipulation

Manipulate model formulas

jackknifeWeights

Jackknifed model weights

Leave-one-out cross-validation

Grade Point Average data

Cement hardening data

Apply a function to calls inside an expression

Relative variable importance

Cos-squared model weights

Bootstrap model weights