This page has a reference character. It links the student with the sources useful for learning the languages and points to the third-party packages, manuals, resources, and programs.

In this course we will mostly use two software packages: Matlab and Bugs. Matlab is a language of choice for communication among engineers. It is easy to grasp, it is powerful, and it has a wealth of packages and support. Bugs/WinBugs is a free stand-alone package for MCMC inference. It has easy sintax and a superb support. For PhD students in Statistics (quite a few at ISyE) the R package is recommended. R is a language of statistics. R is a free S (SPlus) clone and is supported by an army of statisticians. For a student who had not been exposed to S language before R may be on the difficult side. R keeps objects in its internal code making communication with other packages and outside world cumbersome for a novice. At occasions we will use Wolfram's mathematica when exact analytic solutions are needed in nontrivial situations (use of special functions, nonlinear equations, etc).

Numerous resources for Matlab are available online.

- Matlab Bayesian Networs Kit by Kevin Murphy
- Various Matlab Tutors from Andreas Lahe's links.
- ICA, Bayes Blocks, Nonlin Factor Analysis, Nonlin Dynamical Factor Analysis The group from Finland study Bayesian ensemble learning.
- In-house BayesLAB Suite.

- Bugs Manual 05 by Spiegelhalter, Thomas, Best, and Gilks.
- Addendum addman06.pdf
- Bugs Examples Volume 1
- Bugs Examples Volume 2
- CODA Version 0.30
- CODA Addendum 0.40

- Peter Congdon's worked Bugs examples from his books.
- Andrew Millard and BUGS in Archaeology.
- PDF File: A Short Intro to WinBUGS by Ciprian Crainiceanu.
- Slides WinBUGS by Haitao Chu.
- Slides WinBUGS Demo by Saghir Bashir. Nice DoodleBugs Example.

S is a base for R. It has its origins in the AT&T Bell Labs. Beginning in 1976, the S programming language was developed at Bell Labs by John Chambers and collaborators. Version 1 was running on Honeywell mainframes, Version 2 (1980) added Unix support, Version 3 (1988) added functions and objects, and Version 4 (1998) added full support for object-oriented design.

Beginning in 1993, Bell Labs issued an exclusive license to StatSci (later MathSoft). S-Plus is Mathsoft’s commercial implementation of S, and the only way the language is available outside Lucent (unfortunatelly).

The free clone to S, R, was started by Robert Gentleman and Ross Ihaka of the University of Auckland. It is now an open source project that involves hundreds of volunteers from around the world. The development of R is guided through the Comprehensive R Archive network, and full source code, binaries and documentation are available free at the CRAN web site.

Numerous resources for R, S and S-Plus are available online, including

- John Verzani's excellent SimpleR : Introduction to Statistics via R.
- Emmanuel Paradis R for Beginners.
- Faraway's Course on Practical Regression and Anova via R. You will need faraway.zip archive to "Install packages from local zip file(s)" in R and call library(faraway), in order to reproduce examples from the manuscript.
- Kickstarting R. ZIP archive that expands to HTML type tutorial.
- R Language Short Companion, Version 1.2, by Marc Vandemeulebroecke.
- Using R, by J.H. Maindonald from ANU.
- The Carnegie-Mellon archive of S resources
- ISDS’s online S-Plus tutorial and reference
- William Revelle's A Short Guide to R.
- Barron and Li’s Use of R for psychology experiments and questionnaires. Also R reference card by Jonathan Baron.
- Venables and Ripley,
*Modern Applied Statistics with S-PLUS,*3rd ed., London: Springer-Verlag, 1999. ISBN 0-387-98825-4. Also, R complements and online supplement to Venables and Ripley (1999). - Venables and Ripley,
*S Programming,*3rd ed., London: Springer-Verlag, 2000. ISBN 0-387-98966-8.

- MSBNx Extensive Manual and Basics of Bayesian Inference
- MSBNx Examples from University of Maryland.
- MSBNx *.xbn files. http://www.cs.unb.ca/profs/hzhang/CS4725/labs/Lab1.pdf
- MSBNx Tech Report
- MSBNx Case Study: Block Lifting Example.