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.
|Author||Michael A. Lawrence <email@example.com>|
|Date of publication||2016-11-02 18:17:31|
|Maintainer||Michael A. Lawrence <firstname.lastname@example.org>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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