The ez package was developed to aid those that are new to statistical programming. Over the course of several years of helping colleagues and students learn R, I observed that folks are often initially turned off R because they have difficulty obtaining SPSS-like results quickly (SPSS is the dominant environment in my field, psychology). ez attempts to fill this gap, providing quick and easy analysis and graphics for common experimental designs. By easing the early portions of the R learning curve, ez hopes to promote the spread of R as a means of open source and reproducible analysis. ez now also attempts to pique interest in cutting-edge statistical methods by providing easy specification and visualization of simple* mixed effects models.
*--> mixed effects models are limited to those with a single random effect (eg. Participant) and no numeric predictors. New in version 2.0 - ezDesign(), a function to visualize the balance of data across a specified experimental design (useful for diagnosing missing data) - ezPrecis(), a function to summarize a data set (inspired by summary(), str(), and YaleToolkit:whatis() ) - ezBoot() and ezPlotBoot(), functions to compute and visualize (respectively) bootstrapped confidence intervals for either cell means or predictions from a mixed effects model - ezANOVA() updated with an improved measure of effect size: generalized eta-square. - ezPlot() updated to permit simultaneous plotting of multiple DV's, with each mapped to a row of the plot facets. - see changelog for further changes Enjoy! Mike -- Mike Lawrence Graduate Student Department of Psychology Dalhousie University Looking to arrange a meeting? Check my public calendar: http://tr.im/mikes_public_calendar ~ Certainty is folly... I think. ~ _______________________________________________ R-packages mailing list r-packa...@r-project.org https://stat.ethz.ch/mailman/listinfo/r-packages ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.