Suggests: ascii, xtable, parallel, plyr, optimx, nloptr
Imports: stringr, coin, Matrix, pbkrtest (>= 0.3-6)
using ANOVA or mixed models. ez.glm(), aov.car(), or aov4() allow
convenient 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 more then one observation per
individual and cell of the design. mixed() fits a mixed model using
either Kenward-Roger approximation of degrees of freedom (LMM only),
statistical software). compare.2.vectors() compares two vectors using a
variety of tests (t, wilcoxon, and permutation).

using ANOVA or mixed models. ez.glm(), aov.car(), and aov4() allow
convenient 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), potentially aggregating multiple
observations per individual and cell of the design. mixed() fits a mixed
model using lme4::lmer() and computes p-values for all effects using either
Kenward-Roger 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 statistical
software). compare.2.vectors() compares two vectors using a variety of tests

Suggests: ascii, xtable, parallel, plyr, optimx, nloptr, knitr,
Imports: stringr, coin, Matrix, pbkrtest (>= 0.3-6), car, stats, utils,
Description: Provides 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), potentially 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
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
Authors@R: c(person(given="Henrik", family="Singmann", role=c("aut", "cre"),
family="Højsgaard", role=c("ctb")), person(given="John", family="Fox", role=c("ctb")),
person(given="Michael A.", family="Lawrence", role=c("ctb")), person(given="Ulf",