Statistical Software Resources on the Web

Collected by Jim Linnemann
Michigan State University Physics

Completeness or Authoritativeness isn't even a goal--just useful pointers!
If I'm missing a good link (your collection, for example), or a link died, please email it to me!

A word on selection

I sampled links on most top-level pages, and included pages I thought practicing High Energy Physicsits and Astrophysicists would find useful; that didn't trap me on their web page; and had a reasonable proportion of live links. The web is wonderful, but ephemeral; I'm sure you'll find links that weren't broken when I tried. The opinions are my own; a bit on statistics also slipped in among the software.

High Energy Physics

The site is a repository for HEP statistical software, with pointers to the phystat conference series; see StatPatternRecognition there for well-tuned multivariate algorithms.
Particle Data Group Statistics Summary describes statistical methods (theory) on which there is consensus in HEP
Glen Cowan's statistical resources page (Royal Holloway physics); go up a link for some software associated with his book.
There are some statistical routines in Root (an interactive data analysis framerwork). See also roostats and tmva for more useful software in the Root framework. Older libraries include cernlib, clhep and Fermilab’s Zoom.
FreeHep points to other HEP analysis software (including JAS, Java Analysis Studio), but does not have a specific statistics section
CDF statistics committee a Tevatron experiment's statistics page: mostly methods discussion
A simple version of the D0 experiment's Bayesian limit calculator
Babar statistics working group a SLAC experiment's statistics page: methods and a few applets
Geant statistical packages, Maria Grazia Pia, HEP, INFN Genova, C++ library
Fermilab Advanced Analysis Group
gnu gsl (gnu scientific library) contains random number generators, as well as some histogramming, ntuples, moments for weighted events, and autocorrelation calculations. a broad repository of open source software. Basic browsing or search by name without subscribing. You could troll about in the scientific/engineering section and find, for example, roofit.
The Computer Physics Communications program library contains a few items of interest; it requires a subscription to the journal.
A glossary to help translate from statistics-speak to physics-speak (from one of the phystat conferences).


Statcodes Eric Feigelson et. al., Penn State: big collection, with commentary; see also his Astrostatistics book. Look here--much broader than astsrophysics! Includes link to web-based VOSTAT (Virtual Observatory Statistics) project, largely implemented in R (see below).
StatPy: Python interfaces to statistical software, Tom Loredo, Cornell; see also his
Bayesian Inference in the Physical Sciences (Software Section) see especially the ominously-named BUGS (heavily used by statisticians), and BAYESPACK
Astrostatistics, Barry Madore, Cal Tech
Mutual translation glossaries for astronomers and statisticians, and software, and other goodies from statistician David van Dyk.

Statistics Carnegie Mellon's StatLib: a key resource
Free software and interactive pages from John Pezzullo (retired, Georgetown Statistics)
Statistics on the Web from Clay Hellberg of SPSS Use your browser to search for Resources to get to the good stuff
Journal of Statistical Software; in many programming languages. bugs Markov Chain MC package   Duke Statistics (formatting is dodgy on page, alas) Matlab contributions.
From national labs: see Class L for a mixture of commercial and academic software
NIST/SEMATECH e-Handbook of Statistical Methods (Engineering Statistics reference, but not much on multidimensional data, and little software under Tools and Aids)

There is a wiki list of statistical software
And finally, a handy statistics glossary.

Statistical Computations in Java on the Web

Some nice things, some trivia, and many broken links. Gives a feel for the strengths and limitations of web-interfaced statistics. Pezzulo's page above is the best starting place. Many java links are powered by sisa and graphpad

Multivariate Analysis and Statistical Learning

Useful buzzwords to search on in bold; "statistics" will get you more data than methods. Try wiki as well as search engines.

R The R project for Statistical Computing: gnu implementation of the S language
Graphics, statistical algorithms, and a huge repository (CRAN) of R packages. Extensive online documentation. Published books include Introductory Statistics with R by Dalgaard; and Programming with Data: A Guide to the S Language by Chambers; Modern Applied Statistics with S-PLUS, by Venables & Ripley, and others; here's a very good R tutorial ; for more, search for "tutorial using R" GGobi visualization package for multidimensional data.
Includes dynamic graphics such as arrays of scatterplots, brushing techniques (highlighting groups of objectes in one dimension and having their coordinates highlighted in other coordinates); parallel coordinate plots, and grand tours. Interfaces exist to R and Python front ends, and database back ends. I've skimped on Perl here and elsewhere, but often where you find Python interfaces, you'll also find Perl-and sometimes Ruby. The Omega project for Statistical Computing.
Interfaces between R, Python, XML, Java, databases, and other goodies. At this point, aimed more at developers than users.
Jerry Friedman (High Energy Physicist turned Statistican) has software for a number of multivariate techniques on the web; don't miss his book below. Elements of Statistical Learning Theory, by Hastie, Tibshirani, and Friedman. Site includes R/S+ Code
The best multivariate analysis and Statistical Learning textbook I know of; web site includes software. From a modern and sophisticated computational statistics viewpoint, but quite readable. Compares methods from trees to neural nets, kernel methods, and support vector machines, though nothing on genetic algorithms. You can even learn the meaning of useful things like bootstrapping and boosting and other post-1960's statistical jargon! Orange
a massive toolkit, including visualization, feature selection, many evaluation tools, including calibration curves and ROC (Receiver Operating Characteristics = efficiency for signal vs fraction of background: true positives vs. false positives). Practically all major algorithms from machine learning. Python is a popular interface to this library. Multivariate Analysis Software (some older items, but useful)
libsvm, SVMlight, and PRTools are popular Pattern Recognition software (thanks: MSU Computer Science) has more svm software and information
lnknet MIT Classification Software collection--easy to compare methods mixture of commercial and academic software links
Machine Learning Resources online
Neural Network Software list, see also SNNS popular in Babar
ROC curves :
Note: external criteria define the best efficiency point to select, and that often no single algorithm dominates at all efficiencies. There is a considerable gap between the machine learning and statistics communities, which Elements of Statistical Learning by Hastie et al tries to bridge.

Data Mining 

weka is one Java toolkit
mloss is a sortable machine learning repository of free resources; also try wiki machine learning
Most common languages are Python and R
A Python data analysis distribution should include  SciPy NumPy and MatplotLib 
scikit-learn powerful machine learning Python package, and very good summary of the different methods
Theano; a very powerful (Linux) library for Python brings dataframes to Python
caret  most comprehensive predictive modeling package in R;    book:
Rattle, a GUI for data mining in R;      book:
Plotly Powerful visualization package
Julia  interesting new language for data analysis (getting closer to maturity).

An Introduction to Statistical Learning: R based; simple than Hastie et al
Practical Data Science with R

Learning Tools:
RDataMining data mining examples in R  recent fad in machine learning Data Science Specialization from Johns Hopkins<>   Harvard data science class.
blogs: yhatq, wesmckiney, hillarymason, r-bloggers datasets


Google rankings, and Glen Cowan's and Eric Feiglson's pages got me started. The following people (and some others I've forgotten) have provided me with several useful links as well as some excellent suggestions which I have unaccountably ignored.

Tom Loredo (Cornell Astronomy); Rene Brun (CERN); Paul Padley (Rice); Jim Kowalkowski (Fermilab) John Rice (Berkeley Statistics); Louis Lyons (Oxford/UCL); Ilya Narsky (Matlab), Deb Davis (statistics teacher); Yannis Katsnos, Aous Abdo, Sarah G. Williams (data miners)

Last updated November 2015