Stepwise Elimination and Term Reordering for Mixed-Effects Regression
Add terms to a formula
Convert a buildmer term list into a proper model formula
Use buildmer to fit big generalized additive models using bam from...
Use buildmer to fit cumulative link mixed models using clmm from p...
Use buildmer to perform stepwise elimination using a custom fitting ...
Use buildmer to fit generalized additive models using gam from pac...
Use buildmer to fit big generalized additive models using gamm fro...
Use buildmer to fit generalized additive models using package `gamm4...
Use buildmer to fit generalized linear mixed models using `mixed_mod...
Use buildmer to perform stepwise elimination on glmmTMB models
Use buildmer to fit generalized-least-squares models using gls fro...
Use buildmer to perform stepwise elimination of mixed-effects models...
The buildmer class
Construct and fit as complete a model as possible and perform stepwise...
Use buildmer to fit negative-binomial models using glm.nb and `glm...
Use buildmer to fit mixed-effects models using lmer/glmer from `...
Set control options for buildmer
Use buildmer to perform stepwise elimination for lmertree and `glm...
Use buildmer to perform stepwise elimination for multinom models f...
Test a model for convergence
Diagonalize the random-effect covariance structure, possibly assisting...
Generate an LRT elimination function with custom alpha level
Convert lme4 random-effect terms to mgcv 're' smooths
Remove terms from a formula
Parse a formula into a buildmer terms list
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.