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MuMIn (version 1.43.17)

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

1.43.17

License

GPL-2

Maintainer

Last Published

April 15th, 2020

Functions in MuMIn (1.43.17)

Multi-model inference

Adaptive Regression by Mixing

Bates-Granger model weights

QIC and quasi-Likelihood for GEE

Second-order Akaike Information Criterion

Bootstrap model weights

Cos-squared model weights

jackknifeWeights

Jackknifed model weights

Information criteria

Various information criteria

Quasi AIC or AICc

Apply a function to calls inside an expression

Cement hardening data

Automated model selection

Grade Point Average data

Leave-one-out cross-validation

Model averaging

Formula manipulation

Manipulate model formulas

predict.averaging

Predict method for averaged models

model.selection.object

Description of Model Selection Objects

subset.model.selection

Subsetting model selection table

Identify nested models

Flour beetle mortality data

Parameter averaging

Retrieve models from selection table

model selection table

Model utilities

Model utility functions

Pseudo-R-squared for Generalized Mixed-Effect models

List of supported models

merge.model.selection

Combine model selection tables

Standardize data

Standardized model coefficients

Per-variable sum of model weights

stackingWeights

Stacking model weights

Make a function return updateable result

Likelihood-ratio based pseudo-R-squared

Automated model selection using parallel computation

plot.model.selection

Visualize model selection table