• ### 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.

• ### Find-a-fit! (Shiny App)

Find the best linear fit for a given set of data points and residuals (or let this app show you how it is done).

• ### Polynomial Surface Explorer (Shiny App)

Adjust regression parameters to bend and shift a two-dimensional polynomial surface.

• ### 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.

• ### When does a significant p-value indicate a true effect? (Shiny App)

When does a significant p-value indicate a true effect?  This app will help with understanding the Positive Predictive Value (PPV) of a p-value.

This app is based on Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Medicine, 2(8), e124. http://doi.org/10.1371/journal.pmed.0020124

• ### Analysis Tool: 2D Outlier Analysis (Shiny App)

The app allows you to see the trade-offs on various types of outlier/anomaly detection algorithms. Outliers are marked with a star and cluster centers with an X.

• ### Feeling Bayes Factor: Height Difference Between Males and Females (Shiny App)

Can you "see" a group mean difference, just by eyeballing the data? Is your gut feeling aligned to the formal index of evidence, the Bayes factor?

• ### What does a Bayes factor look like? [The urn model] (Shiny App)

Visualizing the Bayes factor (quantification of evidence supporting a null or altermative hypothesis) using the urn model.