Provides convenience functions for analyzing factorial
experiments using ANOVA or mixed-models. ez.glm() and aov.car()
allow convenient calculation of between, within (i.e.,
repeated-measures), or mixed between-within (i.e., split-plot)
ANOVAs for data in the long format (i.e., one observation per
row) wrapping car::Anova() (aggregating more then one
observation per individual and cell of the design), per default
returning a print ready ANOVA table. Function mixed() fits a
mixed model using lme4::lmer() and computes p-values for all
effects in the model using either the Kenward-Rogers
approximation of degrees of freedom (LMM only) or parametric
bootstrap (LMMs and GLMMs). afex uses type 3 sums of squares as
default (imitating commercial statistical software) and sets
the default contrasts to contr.sum. Furthermore,
compare.2.vectors() conveniently compares two vectors using
different statistical tests.

Provides convenience functions for analyzing factorial
experiments using ANOVA or mixed-models. ez.glm() and aov.car()
allow convenient calculation of between, within (i.e.,
repeated-measures), or mixed between-within (i.e., split-plot)
ANOVAs for data in the long format (i.e., one observation per
row) wrapping car::Anova() (aggregating more then one
observation per individual and cell of the design), per default
returning a print ready ANOVA table. Function mixed() fits a
mixed model using lme4::lmer() and computes p-values for all
effects in the model using either the Kenward-Rogers
approximation of degrees of freedom (LMM only), parametric
bootstrap (LMMs and GLMMs) or likelihood ratio tests (LRT).
afex uses type 3 sums of squares as default (imitating
commercial statistical software) and sets the default contrasts
to contr.sum. Furthermore, compare.2.vectors() conveniently
compares two vectors using a variety of tests.

Provides convenience functions for analyzing factorial
experiments using ANOVA or mixed-models. ez.glm() and aov.car()
allow convenient calculation of between, within (i.e.,
repeated-measures), or mixed between-within (i.e., split-plot)
ANOVAs for data in the long format (i.e., one observation per
row) wrapping car::Anova() (aggregating more then one
observation per individual and cell of the design), per default
returning a print ready ANOVA table. Function mixed() fits a
mixed model using lme4::lmer() and computes p-values for all
effects in the model using either the Kenward-Rogers
approximation of 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 statistical software) and sets the default contrasts
to contr.sum. Furthermore, compare.2.vectors() conveniently
compares two vectors using a variety of tests.