Package: ez
Version: 2.1-0
Date: 2010-09-28
Title: Easy analysis and visualization of factorial experiments.
Author: Michael A. Lawrence <Mike.Lawrence@dal.ca>
Maintainer: Michael A. Lawrence <Mike.Lawrence@dal.ca>
Depends: R (>= 2.6-1), car (>= 2.0-0), reshape (>= 0.8-3), plyr (>=
1.1), ggplot2 (>= 0.8-8), stringr (>= 0.4), lme4 (>=
0.999375-35), Matrix (>= 0.999375-31)
Description: This package facilitates easy analysis of factorial
experiments, including purely within-Ss designs (a.k.a.
"repeated measures"), purely between-Ss designs, and mixed
within-and-between-Ss designs. The functions in this package
aim to provide simple, intuitive and consistent specification
of data analysis and visualization. Visualization functions
also include design visualization for pre-analysis data
auditing, and correlation matrix visualization. Finally, this
package includes functions for non-parametric analysis,
including permutation tests and bootstrap resampling. The
bootstrap function obtains predictions either by cell means or
by more advanced/powerful mixed effects models, yielding
predictions and confidence intervals that may be easily
visualized at any level of the experiment's design.
License: GPL (>= 2)
LazyLoad: yes
Packaged: 2010-09-29 01:44:55 UTC; mike
Repository: CRAN
Date/Publication: 2010-09-29 08:00:37

Author: Michael A. Lawrence <mike.lwrnc@gmail.com>
Maintainer: Michael A. Lawrence <mike.lwrnc@gmail.com>

Description: This package facilitates easy analysis of factorial experiments, including purely within-Ss designs (a.k.a. "repeated measures"), purely between-Ss designs, and mixed within-and-between-Ss designs. The functions in this package aim to provide simple, intuitive and consistent specification of data analysis and visualization. Visualization functions also include design visualization for pre-analysis data auditing, and correlation matrix visualization. Finally, this package includes functions for non-parametric analysis, including permutation tests and bootstrap resampling. The bootstrap function obtains predictions either by cell means or by more advanced/powerful mixed effects models, yielding predictions and confidence intervals that may be easily visualized at any level of the experiment's design.