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# Professional

• ### Analysis Tool: The R Project for Statistical Computing

R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.

• ### A Compendium of Clean Graphs in R

This compendium facilitates the creation of good graphs by presenting a set of concrete examples, ranging from the trivial to the advanced. The graphs can all be reproduced and adjusted by copy-pasting code into the R console. Almost every example in this compendium is driven by the same philosophy: A good graph is a simple graph, in the Einsteinian sense that a graph should be made as simple as possible, but not simpler.  A note for R fans: the majority of our plots have been created in base R, but you will encounter some examples in ggplot.

• ### Analysis Tool: Bayes Factor Robustness [Two sample t-test] (Shiny App)

Check how your Bayes factor conclusion depends on the r-scale parameter.

• ### Analysis Tool: p-Value Analyzer (Shiny App)

This Shiny app implements the p-curve (Simonsohn, Nelson, & Simmons, 2014; see http://www.p-curve.com) in its previous ("app2") and the current version ("app3"), the R-Index and the Test of Insufficient Variance, TIVA (Schimmack, 2014; see http://www.r-index.org/), and tests whether p values are reported correctly.

• ### Analysis Tool: BIC Approximation for ANOVA Designs (Shiny App)

This app allows you to derive an approximation to the difference in Bayesian information criterion and to the probability of the null and the alternative hypothesis from the sum of squares obtained in an ANOVA analysis.

Required input

• Number of participants
• Df ... degrees of freedom of the effect of interest
• Whether the effect is between or within participants
• SSEffect ... sum of squares of the effect of interest
• SSError ... sum of squares of the error, for within-factors the by-subject error, associated with this effect
• SSTotal ... total sum of squares, only required for within-participant designs when using effective sample size (strongly recommended, Nathoo & Masson, 2007)
• ### Analysis Tool: Distribution of Cohen's d, p-values, and power curves for an independent two-tailed t-test (Shiny App)

Plot the theoretical p-value distribution and power curve for an independent t-test based on the effect size, sample size, and alpha.

• ### Analysis Tool: Vovk-Sellke Maximum p-Ratio (Shiny App)

Explore the Vovk-Sellke Maximum p-Ratio, a measure that indicates the maximum diagnosticity of a given p-value. Choose your own p-value to find out how diagnostic it is for your research!

• ### Getting used to R, RStudio, and R Markdown

This resource is designed to provide new users to R, RStudio, and R Markdown with the introductory steps needed to begin their own reproducible research. Many screenshots and screencasts (with no audio) will be included, but if further clarification is needed on these or any other aspect of the book, please create a GitHub issue here or email me with a reference to the error/area where more guidance is necessary.  It is recommended that you have R version 3.3.0 or later, RStudio Desktop version 1.0 or higher, and rmarkdown R package version 1.0 or higher.

• ### Correspondence Analysis

Correspondence analysis is a method allowing you to describe synthetically a contingency table in which homogeneous individuals are classified on two criterias (or categorical variables, continuous ones being usable if discretized).  This resource tells how it can be used, graphical representations of this process, and gives examples of it in action.

• ### The Probability Web

The Probability Web is a collection of probability resources designed to be especially helpful to researchers, teachers, and people in the probability community.  Web page links on this site include probabilty/statistics books and journals, information on mathematics and statistics-based careers, statistical software, teaching resources on probabilty topics, and more.