person(given = "Tilman Leo",
family = "Hohenberger",
role = c("aut", "cre"),
email = "tlh@ust.hk",
comment = c(ORCID = "0000-0002-4372-8750"))
Generates non-circular simple feature geometries e.g. for the use as buffers in model-building.
Tilman Leo Hohenberger [aut, cre]
(<https://orcid.org/0000-0002-4372-8750>)

Stepwise Elimination and Term Reordering for Mixed-Effects
Regression
person("Cesko", "Voeten", email = "c.c.voeten@hum.leidenuniv.nl", role = c("aut", "cre"))
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, Akaike's Information Criterion, or the Bayesian Information Criterion.
JuliaCall, MASS, gamm4, glmmTMB, knitr, lmerTest, nlme, nnet,
parallel, pbkrtest

person(c("Cesko", "C."), "Voeten", email = "cvoeten@gmail.com", role = c("aut", "cre"))
JuliaCall, MASS, gamm4, glmmTMB, knitr, lmerTest, nlme, nnet,
parallel, pbkrtest, rmarkdown

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, or the Bayesian
Information Criterion.
JuliaCall, MASS, gamm4, glmertree, glmmTMB, knitr, lmerTest,
nlme, nnet, parallel, pbkrtest, rmarkdown

person(c("Cesko", "C."), "Voeten", email = "cvoeten@gmail.com", role = c("aut", "cre"), comment = c(ORCID="0000-0003-4687-9973"))
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, or the Bayesian
Information Criterion.
GLMMadaptive, JuliaCall, MASS, gamm4, glmertree, glmmTMB,
knitr, lmerTest, nlme, nnet, parallel, partykit, pbkrtest,
rmarkdown