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MuMIn (version 1.47.5)

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

17,792

Version

1.47.5

License

GPL-2

Maintainer

Last Published

March 22nd, 2023

Functions in MuMIn (1.47.5)

Leave-one-out cross-validation

Formula manipulation

Manipulate model formulas

Cement hardening data

Cos-squared model weights

Grade Point Average data

Apply a function to calls inside an expression

jackknifeWeights

Jackknifed model weights

Automated model selection

Flour beetle mortality data

Retrieve models from selection table

Parameter averaging

merge.model.selection

Combine model selection tables

plot.model.selection

Visualize model selection table

Automated model selection using parallel computation

Identify nested models

model selection table

predict.averaging

Predict method for averaged models

Model averaging

model.selection.object

Description of Model Selection Objects

Model utilities

Model utility functions

Make a function return updateable result

subset.model.selection

Subsetting model selection table

Per-variable sum of model weights

Pseudo-R-squared for Generalized Mixed-Effect models

Likelihood-ratio based pseudo-R-squared

List of supported models

Standardize data

stackingWeights

Stacking model weights

Standardized model coefficients

Adaptive Regression by Mixing

Bootstrap model weights

Bates-Granger minimal variance model weights

Quasi AIC or AICc

QIC and quasi-Likelihood for GEE

Plot model coefficients

Second-order Akaike Information Criterion

Information criteria

Various information criteria

Multi-model inference