Stepwise Elimination and Term Reordering for Mixed-Effects Regression

Finds the largest possible regression model that will still converge for various types of regression analyses (including mixed models and generalized additive models) and then optionally performs stepwise elimination similar to the forward and backward effect-selection methods in SAS, based on the change in log-likelihood or its significance, Akaike's Information Criterion, the Bayesian Information Criterion, the explained deviance, or the F-test of the change in R².


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


1.9 by Cesko C. Voeten, 3 months ago

Report a bug at

Browse source code at

Authors: Cesko C. Voeten [aut, cre]

Documentation:   PDF Manual  

FreeBSD license

Imports graphics, lme4, methods, mgcv, nlme, plyr, stats, utils

Suggests GLMMadaptive, MASS, gamm4, glmertree, glmmTMB, knitr, lmerTest, nnet, ordinal, parallel, partykit, pbkrtest, rmarkdown, testthat

Suggested by permutes.

See at CRAN