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.
- 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 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 the swirlify package. 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.
- 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 .
- TeachBayes provides visualizations to illustrate the basic ideas of Bayesian inference, the roles of prior and posterior distributions in particular. Key teaching examples are used, namely inference for: a mean, a proportion, two proportions and which of several multi-faced dice have been thrown in an experiment.
- 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.
- 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.
- AtelieR uses a GTK GUI to help teach some key statistical concepts. Includes the sampling distributions of the mean (cf. central limit theorem) and variance, probability calculator for common distributions, Bayesian inference for proportions, multinomial counts, means and variances.
- 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.
- RndTexExams creates multiple choice exams from a pool of exercises composed in LaTeX format. R code can be used to generate exercise elements dynamically. Spreadsheets containing students' answers can be marked automatically.
- 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., Zeileis, A. (2008), Applied Econometrics with R, Springer Verlag, New York. Further resources.
- ACSWR: Tattar, P.N., Suresh, R., and Manjunath, B.G. (2016), A Course in Statistics With R, John Wiley and Sons, Inc.
- BaM: Gill, J. (2014), Bayesian Methods: A Social and Behavioral Sciences Approach, New York: Chapman and Hall/CRC.
- 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.
- ElemStatLearn: Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition, Springer, 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.
- 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.
- openintro: Three open-source books published by OpenIntro: OpenIntro Statistics, Introductory Statistics with Randomization and Simulation, Advanced High School Statistics.
- 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.