Suggests: xtable, parallel, plyr, optimx, nloptr, knitr, rmarkdown,
lattice, latticeExtra, multcomp, testthat, mlmRev, dplyr,
Imports: stringr, coin, pbkrtest (>= 0.4-1), lmerTest, car, reshape2,
Description: Convenience functions for analyzing factorial experiments using ANOVA or
mixed models. aov_ez(), aov_car(), and aov_4() allow specification of
between, within (i.e., repeated-measures), or mixed between-within (i.e.,
split-plot) ANOVAs for data in long format (i.e., one observation per row),
aggregating multiple observations per individual and cell of the design.
mixed() fits mixed models using lme4::lmer() and computes p-values for all
fixed effects using either Kenward-Roger or Satterthwaite approximation for
likelihood ratio tests (LMMs and GLMMs). afex uses type 3 sums of squares
as default (imitating commercial statistical software).

mixed models. aov_ez(), aov_car(), and aov_4() allow specification of between,
within (i.e., repeated-measures), or mixed between-within (i.e., split-plot)
ANOVAs for data in long format (i.e., one observation per row), aggregating
multiple observations per individual and cell of the design. mixed() fits mixed
models using lme4::lmer() and computes p-values for all fixed effects using
either Kenward-Roger or Satterthwaite approximation for degrees of freedom (LMM
only), parametric bootstrap (LMMs and GLMMs), or likelihood ratio tests (LMMs
and GLMMs). afex uses type 3 sums of squares as default (imitating commercial
role=c("ctb")), person(given="Michael A.", family="Lawrence", role=c("ctb")),

Description: Convenience functions for analyzing factorial
experiments using ANOVA or mixed models. aov_ez(),
aov_car(), and aov_4() allow specification of between,
within (i.e., repeated-measures), or mixed between-within
(i.e., split-plot) ANOVAs for data in long format (i.e.,
one observation per row), aggregating multiple observations
per individual and cell of the design. mixed() fits mixed
models using lme4::lmer() and computes p-values for all
fixed effects using either Kenward-Roger or Satterthwaite
approximation for degrees of freedom (LMM only), parametric
bootstrap (LMMs and GLMMs), or likelihood ratio tests (LMMs
and GLMMs). afex uses type 3 sums of squares as default