For a recent overview of the computing resources, see the slides from the 2014 computing seminar.
Our primary point of contact for desktop IT support is the Faculty of Science IT Support Team. You can submit a request for support here. Requests for support for the Unix network, including Fedora desktops, may be submitted by email to firstname.lastname@example.org. See the Gripe Helpdesk page for more information on submitting a gripe.
The departmental Unix computers are part of FASnet, a computer network maintained by the IT Services Research Computing Group. Our access to FASnet is controlled by membership to the departmental mailing lists
stat-visitors-sessionals. If you are a member of one of these lists, you have access to the following login and compute servers. Users new to Unix might consult a locally-hosted Unix tutorial.
Software on the Unix network is maintained through what are called modules. For a list of available modules, type
module avail from the Unix command line. To use software, such as R, you must first load the relevant module; e.g., typing
module load LANG/R/3.3.1
from the Unix command line loads the module containing R version 3.3.1. You can now type
R from the Unix command line to start R.
FASnet users have access to the Colony Cluster for high-performance computing. For the most up-to-date information on the Colony Cluster, please visit the SFU HPC Wiki. There is also a getting started with the cluster page with some examples of potential interest for statistics.
SFU students, staff and faculty may download a copy of SAS, JMP, Splus, SPSS PASW, and more for free from the SFU IT Services Software Download Page.
- Local SAS help and information
- JMP (A more user-friendly product from the SAS Institute)
- Local Splus help and information
- R (an open-source alternative to Splus)
- R project homepage
- Download a copy from the SFU mirror of the Comprehensive R Archive Network (CRAN)
- R introductions: R-intro.pdf from CRAN | www.personal.psu.edu/ljk20/RIntro.pdf
- Online manuals
- Online FAQs
- Dirk Eddelbuettel's tutorial on high-performance computing with R
- Matt Pratola's presentation on optimizing and packaging R code: PDF | PostScript
- Sigal Blay's presentation on calling C-code from R: PowerPoint | Plain Text
- Stat Lab information and Minitab handbooks.
- Miscellaneous Statistical Computing: BUGS | BioConductor | Omega project