The mosaic package is designed to facilitate the use of R in statistics and calculus instruction by providing a number of functions that (a) make many common tasks fit into a common template, and (b) simplify some tasks that would otherwise be too complicated for beginners.

You install from CRAN using

or from github with

If you want to try out our developmental code (the beta branch), use

Some features of the mosaic package have been split off into auxiliary packages. These include:

- mosaicModel – implements high-level systems for working with statistical models: effect-size calculation, bootstrapped confidence intervals, prediction error, graphics for models with multiple inputs. The package contains an introductory vignette.
- mosaicCalc – provides the calculus components of mosaic, including integration, differentiation, and differential equation solving. See
*Modeling-based calculus with R/mosaic*for an instructor-oriented introduction and*Start R in Calculus*for a student-facing guide.

Install these packages using `install.packages(c("mosaicCalc", "mosaicModel"))`

or from GitHub as described above.

Updates to the master github repository are more frequent than CRAN updates. Our beta branch is where we implement bug fixes most quickly and develop new features. We try to keep it pretty stable, but there may be a few rough edges, missing documentation, etc. while things are in progress.

If you discover a problem with any version of the package, be sure to let us know so that we can address it. Post an issue on github or send email to `Rpkgs@mosaic-web.org`

.

The package includes several vignettes to help you get started. One of these vignettes (*Resources Related to the mosaic package*) includes a list of many resources, both within the package and external to it. That’s a good place to start.

Need help? Try posting a question on Stack Overflow using the tag r-mosaic.

Project MOSAIC is a community of educators working to develop a new way to introduce mathematics, statistics, computation and modeling to students in colleges and universities.

Our goal: Provide a broader approach to quantitative studies that provides better support for work in science and technology. The focus of the project is to tie together better diverse aspects of quantitative work that students in science, technology, and engineering will need in their professional lives, but which are today usually taught in isolation, if at all.

- Modeling. The ability to create, manipulate and investigate useful and informative mathematical representations of a real-world situations.
- Statistics. The analysis of variability that draws on our ability to quantify uncertainty and to draw logical inferences from observations and experiment.
- Computation. The capacity to think algorithmically, to manage data on large scales, to visualize and interact with models, and to automate tasks for efficiency, accuracy, and reproducibility.
- Calculus. The traditional mathematical entry point for college and university students and a subject that still has the potential to provide important insights to today’s students.

The name MOSAIC reflects the first letters — M, S, C, C — of these important components of a quantitative education. Project MOSAIC is motivated by a vision of quantitative education as a mosaic where the basic materials come together to form a complete and compelling picture.

Find out more about Project MOSAIC at [http://mosaic-web.org].