Learn R Programming

MuMIn (version 1.48.4)

Multi-Model Inference

Description

Tools for model selection and model averaging with support for a wide range of statistical models. Automated model selection through subsets of the maximum model, with optional constraints for model inclusion. Averaging of model parameters and predictions based on model weights derived from information criteria (AICc and alike) or custom model weighting schemes.

Copy Link

Version

Install

install.packages('MuMIn')

Monthly Downloads

20,265

Version

1.48.4

License

GPL-2

Maintainer

Last Published

June 22nd, 2024

Functions in MuMIn (1.48.4)

Cement hardening data

Leave-one-out cross-validation

Retrieve models from selection table

Grade Point Average data

jackknifeWeights

Jackknifed model weights

Flour beetle mortality data

Apply a function to calls inside an expression

Cos-squared model weights

predict.averaging

Predict method for averaged models

Parameter averaging

Identify nested models

Automated model selection

Formula manipulation

Manipulate model formulas

Model utilities

Model utility functions

model selection table

Model averaging

plot.model.selection

Visualize model selection table

model.selection.object

Description of Model Selection Objects

merge.model.selection

Combine model selection tables

Automated model selection using parallel computation

Per-variable sum of model weights

stackingWeights

Stacking model weights

List of supported models

Make a function return updateable result

Standardize data

Pseudo-R-squared for Generalized Mixed-Effect models

Likelihood-ratio based pseudo-R-squared

subset.model.selection

Subsetting model selection table

Standardized model coefficients

QIC and quasi-Likelihood for GEE

Bootstrap model weights

Multi-model inference

Quasi AIC or AICc

Second-order Akaike Information Criterion

Bates-Granger minimal variance model weights

Plot model coefficients

Adaptive Regression by Mixing

Information criteria

Various information criteria