Easy analysis and visualization of factorial experiments.
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
provide easy access to descriptive statistics, ANOVA, and
visualization of results.

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
provide easy access to descriptive statistics, ANOVA,
permutation tests, and visualization of results.

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.

http://groups.google.com/group/ez4r (discussion list),
https://github.com/mike-lawrence/ez/issues (bug reports and
feature requests)

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.