The Package
R is a free software environment for statistical computing and graphics plotting. It's allows very quick statistical analysis of data with minimum script coding when compared to any other programming environment. It's a open-source tool that replaces the classic "S" package. R is a very well documented package and several tutorials can be easily found in the Internet. In this page we should focus on the ones related to the several machine learning and pattern recognition techniques related to the course. Please, follow the links below to more information:
- `The official R website <http://www.r-project.org/>`_
- `R download mirror at CMU <http://lib.stat.cmu.edu/R/CRAN/>`_
There are available versions for several platforms such as Linux, MacOS, and Windows binaries. You may try to download the source code and compile the package yourself in case you're using a platform for which R is not yet available.
Some very useful general tutorials
- `Statistics with R <http://zoonek2.free.fr/UNIX/48_R/all.html>`_
- `A short course in R from Washington University <http://faculty.washington.edu/tlumley/Rcourse/>`_
- `Power Point slides on R programming <http://www.math.ntu.edu.tw/~hchen/Prediction/notes/R-programming.ppt>`_
- `SVM using R tutorial <http://www.potschi.de/svmtut/svmtut.html>`_
- `Cluster analysis package <http://cran.r-project.org/web/packages/cluster/index.html>`_
- `Learning Bayesian Networks with R <http://www.ci.tuwien.ac.at/Conferences/DSC-2003/Proceedings/BottcherDethlefsen.pdf>`_
