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MuMIn (version 1.43.6)

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

16,303

Version

1.43.6

License

GPL-2

Maintainer

Last Published

April 9th, 2019

Functions in MuMIn (1.43.6)

Information criteria

Various information criteria

Model utilities

Model utility functions

Quasi AIC or AICc

Standardized model coefficients

Standardize data

Model averaging

QIC and quasi-Likelihood for GEE

Flour beetle mortality data

Cement hardening data

Adaptive Regression by Mixing

Automated model selection using parallel computation

plot.model.selection

Visualize model selection table

predict.averaging

Predict method for averaged models

Pseudo-R-squared for Generalized Mixed-Effect models

subset.model.selection

Subsetting model selection table

Per-variable sum of model weights

jackknifeWeights

Jackknifed model weights

Leave-one-out cross-validation

Identify nested models

Parameter averaging

Likelihood-ratio based pseudo-R-squared

stackingWeights

Stacking model weights

Bootstrap model weights

Second-order Akaike Information Criterion

Bates-Granger model weights

Formula manipulation

Manipulate model formulas

Cos-squared model weights

merge.model.selection

Combine model selection tables

model selection table

model.selection.object

Description of Model Selection Objects

List of supported models

Make a function return updateable result

Grade Point Average data

Automated model selection

Multi-model inference

Apply a function to calls inside an expression

Retrieve models from selection table