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MuMIn (version 1.43.15)

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

23,305

Version

1.43.15

License

GPL-2

Maintainer

Kamil Barto

Last Published

December 20th, 2019

Functions in MuMIn (1.43.15)

Cos-squared model weights

Grade Point Average data

Automated model selection

Flour beetle mortality data

Cement hardening data

Retrieve models from selection table

Apply a function to calls inside an expression

jackknifeWeights

Jackknifed model weights

Leave-one-out cross-validation

predict.averaging

Predict method for averaged models

Pseudo-R-squared for Generalized Mixed-Effect models

model.selection.object

Description of Model Selection Objects

model selection table

Automated model selection using parallel computation

plot.model.selection

Visualize model selection table

merge.model.selection

Combine model selection tables

Formula manipulation

Manipulate model formulas

Model utilities

Model utility functions

Model averaging

Per-variable sum of model weights

subset.model.selection

Subsetting model selection table

Parameter averaging

Identify nested models

Likelihood-ratio based pseudo-R-squared

stackingWeights

Stacking model weights

Standardize data

Standardized model coefficients

List of supported models

Make a function return updateable result

Bates-Granger model weights

Second-order Akaike Information Criterion

Quasi AIC or AICc

Information criteria

Various information criteria

Bootstrap model weights

Multi-model inference

QIC and quasi-Likelihood for GEE

Adaptive Regression by Mixing