Last updated on 2020-11-22 by Paul Northrop

This CRAN task view gives information about packages with features
that are designed to assist with the teaching of Statistics.
It is **not** concerned with the teaching of R itself.
A few of these packages are listed in other task views, but only
the Bayesian task view has a section devoted explicitly
to teaching (Bayesian) Statistics.

The packages are grouped into three broad topics: teaching, examination and packages associated with Statistics books. The latter is for books that are general enough to be of potential interest to a wide audience of teachers of Statistics. They should concern models and methods with wide applicability and not be tied closely to a particular application.

If you think that a package is missing from the list, or have any other comments or suggestions, then please contact the maintainer.

**Teaching**

- Rcmdr provides a GUI for R, based on the tcltk package. A point-and-click interface loads data and calls R functions to perform the kinds of analyses involved in introductory Statistics courses. More advanced and specialized analysis are also available, some of them via plug-ins. The R commands are shown in the console. See the The R Commander homepage for more information.
- swirl uses the R console to provide an interactive learning
environment for students to learn Statistics. Students select courses
to download from the
swirldev/swirl_courses GitHub page and are provided with immediate feedback as they work. A variety of topics are available, under the general headings of Exploratory Data Analysis, Statistical Inference and Regression Models. Teachers can author and share their own swirl courses using theswirldev/swirlify package (currently on Github only). See also the swirl home page. - mosaic contains a wide range of tools to assist in teaching of basic, and more advanced ideas and techniques in mathematics, statistics, computation and modelling. Key aspects are the provision of functions that enable beginners easily to perform tasks that would otherwise be difficult and the use of simulation to illustrate randomization-based inference. See the Project MOSAIC homepage for more information.
- xplain can be used to provide bespoke interactive interpretations of the output from statistics functions. This information needs to be provided by the instructor in XML format and may contain R code, to tailor the explanation to the specific results. See the xplain website for a tutorial and cheatsheet.
- animation provides functions to produce animations relating to a wide range of topics in Statistics, Data Mining and Machine Learning. These animations, or a sequence of images generated by the user, may be exported to a variety of formats.
- gganimate animates plots produced by ggplot2. It can be used to render the plots into an animation, such as a GIF or MP4 video .
- smovie provides movies to illustrate concepts in Statistics. Topics covered are: probability distributions; sampling distributions of the mean (cf. central limit theorem), the maximum (cf. extremal types theorem) and the (Fisher transformation of the) correlation coefficient; simple linear regression; hypothesis testing.
- visualize provides graphs of the pdf/pmf of various continuous and discrete probability distributions, annotated with the mean and variance of the distribution. Shading is used to indicate an interval (lower tail, upper tail, two-tailed or a user-supplied interval) within which the random variable lies with a user-supplied probability.
- bivariate provides graphs of the pdf/pmf and cdf of various bivariate continuous (uniform, normal and normal mixture) and discrete (binomial, Poisson and categorical) distributions and trivariate (normal and Dirichlet) distributions.
- LearnBayes provides functions and to illustrate the essential ideas of Bayesian inference, such as the roles of the prior, likelihood and posterior; posterior predictive checking and predictive inference, and several example datasets.
- TeachingDemos Provides a wide range of static and interactive plots to demonstrate statistical concepts, including: coin tossing and dice rolling; confidence intervals; various aspects of hypothesis testing; the central limit theorem; maximum likelihood estimation; scatterplot smoothing; histograms; correlation and simple linear regression; Box-Cox transformation.
- distrTeach provides plots to illustate the Central Limit Theorem (CLT) and the Law of Large Numbers (LLN). The effects on the CLT plots of changing inputs can be shown using a Tcl/Tk-based widget.
- learnstats uses a console-based menus and shiny apps to provide interactive plots that illustrate key statistical concepts. Topics covered include probability areas on density functions, binomial, normal, t and F distributions, p-values, QQ-plots and simulation of time series with different behaviours.
- BetaBit provides games for students to play in the R console, including one that involves data-cleaning and regression modelling. See the BetaBit home page.
- DALEX provides functions to explore and understand predictive models. The DALEX GitHub page includes two teaching-related showcases.

**Examination**

- exams provides a framework for the automatic random generation of exams and self-study materials from a pool of exercises composed using either Sweave (.Rnw) or R markdown (.Rmd) formats. R code can be used to generate exercise elements dynamically. Questions can be formatted for use in a variety of e-learning platforms or output as documents, for example a PDF file, for which. Scans of PDF answer sheets can be marked automatically. See also the R/exams homepage
- ProfessR creates multiple choice exams from a pool of exercises organised in ASCII test files. Multiple versions of an exam can be created by randomizing the questions and the choices of answers.
- TexExamRandomizer enables the randomization of questions created using LaTeX's document class for preparing exams. Spreadsheets containing students' answers can be marked automatically.

**Packages associated with Statistics books**

The following packages are associated with textbooks that are of potential interest to a general statistical audience, rather than being specific to a particular application area. The general principle for inclusion is that package is likely to be of direct use in the teaching of statistical methods. Official publisher links are provided where possible and, in some cases, a link to further resources.

- AER: Kleiber, C. and Zeileis, A. (2008), Applied Econometrics with R, Springer Verlag, New York. Further resources.
- arm: Gelman, A. and Hill, J. (2007), Data Analysis Using Regression and Multilevel//Hierarchical Models, Cambridge University Press. Further resources.
- ACSWR: Tattar, P.N., Suresh, R., and Manjunath, B.G. (2016), A Course in Statistics With R, John Wiley and Sons, Inc.
- BayesDA: Gelman, A., Carlin, J., Stern, H., Dunson, D., Vehtari, A., Rubin, D. (2013), Bayesian Data Analysis, Third Edition. New York: Chapman and Hall/CRC. Further resources.
- Bolstad: Bolstad, W. M. and Curran, J. M. (2016), Introduction to Bayesian Statistics, Third Edition. John Wiley and Sons, Inc.
- car, carData, effects: Fox, J, and Weisberg, S. (2019), An R Companion to Applied Regression, Springer Verlag, New York. Further resources.
- faraway: Three books by Julian Faraway: Practical Regression and ANOVA in R (CRAN document), Linear Models with R (2014), CRC Press, Extending the Linear Model with R (2016), CRC Press.
- HH: Heiberger, R. M. and Holland B. (2015), Statistical Analysis and Data Display: An Intermediate Course with Examples in R, Second edition. Springer-Verlag, New York.
- HSAUR3: Hothorn, T. and Everitt, B. S. (2014), A Handbook of Statistical Analyses using R, Third Edition. New York: Chapman and Hall/CRC.
- ISwR: Dalgaard, P. (2008), Introductory Statistics with R, Second Edition, Springer Verlag, New York.
- MASS: Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S, Fourth Edition, Springer, New York. Further resources.
- moderndive: Ismay, C. and Kim, A. Y. (2019) ModernDive: Statistical Inference via Data Science. See also infer.
- MPV: Montgomery, D.C., Peck, E. A. and Vining, G. (2012), Introduction to Linear Regression Analysis, John Wiley and Sons, Inc.
- msos: Marden, J. (2015) Multivariate Statistics: Old School, CreateSpace Independent Publishing Platform. Free PDF version.
- openintro: Three open-source books published by OpenIntro: OpenIntro Statistics, Introductory Statistics with Randomization and Simulation, Advanced High School Statistics.
- regtools: Matloff, N. (2017), Statistical Regression and Classification: from Linear Models to Machine Learning, New York: Chapman and Hall/CRC.
- resampledata: Chihara, L. M. and Hesterberg, T. C. (2018), Mathematical Statistics with Resampling in R, Second Edition, John Wiley and Sons, Inc. Further resources.
- Sleuth2 and Sleuth3: Ramsey, F. and Schafer, D. (2013), The Statistical Sleuth: a Course in Methods of Data Analysis, Brooks / Cole Cengage Learning.
- SMPracticals: Davison, A. C. (2003), Statistical Models, Cambridge University Press. Further resources.
- vcd: Friendly, M. and Meyer, D. (2015), Discrete Data Analysis with R, New York: Chapman and Hall/CRC. Further resources.
- wooldridge: Wooldridge, J. M. (2016), Introductory Econometrics: A Modern Approach, Sixth edition, CENGAGE Learning Custom Publishing.

- Mailing list: R Special Interest Group on Teaching Statistics using R
- ABC News' FiveThirtyEight: opinion poll, politics, economics and sports datasets, blogs and some R code.
- The fivethirtyeight package: unofficial collection of datasets and code from FiveThirtyEight.
- R/exams homepage
- OpenIntro text books and R Labs
- Project MOSAIC homepage
- The R Commander homepage
- The swirl home page
swirldev/swirl_courses

10 months ago by Yu-Sung Su

Data Analysis Using Regression and Multilevel/Hierarchical Models

6 years ago by Prabhanjan Tattar

A Companion Package for the Book "A Course in Statistics with R"

9 years ago by Kjetil Halvorsen

Functions and Datasets for the book "Bayesian Data Analysis"

2 months ago by Przemyslaw Biecek

moDel Agnostic Language for Exploration and eXplanation

2 years ago by Peter Ruckdeschel

Extensions of Package 'distr' for Teaching Stochastics/Statistics in Secondary School

5 months ago by Thomas Lin Pedersen

Create Elegant Data Visualisations Using the Grammar of Graphics

6 months ago by Richard M. Heiberger

Statistical Analysis and Data Display: Heiberger and Holland

a month ago by Torsten Hothorn

A Handbook of Statistical Analyses Using R (3rd Edition)

15 days ago by Brian Ripley

Support Functions and Datasets for Venables and Ripley's MASS

7 months ago by James Balamuta

Data Sets and Functions Used in Multivariate Statistics: Old School by John Marden

4 months ago by Randall Pruim

Project MOSAIC Statistics and Mathematics Teaching Utilities

a month ago by Mine Ã‡etinkaya-Rundel

Data Sets and Supplemental Functions from 'OpenIntro' Textbooks and Labs

2 years ago by Albert Y. Kim

Data Sets for Mathematical Statistics with Resampling in R

2 years ago by Berwin A Turlach

Data Sets from Ramsey and Schafer's "Statistical Sleuth (2nd Ed)"

2 years ago by Berwin A Turlach

Data Sets from Ramsey and Schafer's "Statistical Sleuth (3rd Ed)"

2 years ago by Anthony Davison

Practicals for Use with Davison (2003) Statistical Models

3 years ago by Alejandro Gonzalez Recuenco

Personalizes and Randomizes Exams Written in 'LaTeX'

a year ago by James Balamuta

Graph Probability Distributions with User Supplied Parameters and Statistics

3 years ago by Justin M. Shea

111 Data Sets from "Introductory Econometrics: A Modern Approach, 6e" by Jeffrey M. Wooldridge

10 months ago by Joachim Zuckarelli

Providing Interactive Interpretations and Explanations of Statistical Results