Statistical Software Resources on the
Web
Collected by Jim Linnemann
Michigan State University Physics
Completeness or Authoritativeness isn't even a goaljust 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 toplevel 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 phystat.org site
is a repository for HEP statistical software, with pointers to the
phystat conference series; see StatPatternRecognition there for
welltuned 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.

 sourceforge.net 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 statisticsspeak to physicsspeak (from one of the phystat conferences).
Astrophysics
 Statcodes Eric Feigelson et.
al., Penn State: big collection, with commentary; see also his Astrostatistics
book. Look heremuch broader than astsrophysics! Includes
link to webbased 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
ominouslynamed 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
 http://lib.stat.cmu.edu/
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
 http://www.stat.ufl.edu/vlib/statistics.html
Use your browser to search for Resources to get to the good stuff
 Journal of Statistical Software;
in many programming languages.
 http://www.mrcbsu.cam.ac.uk/bugs/welcome.shtml
bugs Markov Chain MC package
 http://stat.duke.edu/comp/software/ Duke Statistics (formatting is dodgy on page, alas)
 http://www.mathworks.com/matlabcentral/fileexchange/loadCategory.do
Matlab contributions.
 From national labs:
 http://gams.nist.gov/ see Class L for
a mixture of commercial and academic software
 NIST/SEMATECH
eHandbook 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 webinterfaced 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 SPLUS, by Venables &
Ripley, and others; here's a very good R
tutorial ; for more, search for "tutorial using R"
 http://www.ggobi.org/ 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 Perland sometimes Ruby.
 http://www.omegahat.org/ 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.
 http://statweb.stanford.edu/~tibs/ElemStatLearn/
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 post1960's statistical jargon!

 http://magix.fri.unilj.si/orange/
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.
 http://www.pitt.edu/~csna/software.html
Multivariate Analysis Software (some older items, but useful)
 libsvm, SVMlight, and PRTools are popular Pattern Recognition software (thanks: MSU Computer Science)
 supportvectormachines.org has more svm software and information
 lnknet
MIT Classification Software collectioneasy to compare methods
 http://www.kdnuggets.com/software/classification.html
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
 scikitlearn powerful machine learning Python package, and very good summary of the different methods
 Theano; a very powerful (Linux) library for Python
http://pandas.pydata.org/ brings dataframes to Python
caret most comprehensive predictive modeling package in R; book: http://appliedpredictivemodeling.com/
Rattle, a GUI for data mining in R; book: http://goo.gl/kaUqr2
 Plotly Powerful visualization package
Julia interesting new language for data analysis (getting closer to maturity).
Books:
An Introduction to Statistical Learning: http://wwwbcf.usc.edu/~gareth/ISL/ R based; simple than Hastie et al
Practical Data Science with R
Learning Tools:
 RDataMining data mining examples in R
 http://deeplearning.net/tutorial/ recent fad in machine learning
 https://www.coursera.org/specializations/jhudatascience Data Science Specialization from Johns Hopkins
 http://cs109.org/<http://cs109.org/readings.php> Harvard data science class.
 blogs: yhatq, wesmckiney, hillarymason, rbloggers
 http://www.quandl.com/ datasets
Acknowledgements
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