Computational Statistics

By this I do not just mean R, but R is a big part of being a working academic statistician these days...

R, for the record, is a free, open-source interpreted programming language (and interactive environment) for statistical computing. It descends from a language developed at Bell Labs (of blessed memory) called S. There is a commercial descendant of S called S-plus, but I know of no reason to use it, rather than R. For that matter, I know of no reason to use any of the commercial statistical environments (Stata, SPSS, Minitab, ...) rather than R, except for pesonal and organizational inertia. (Which is not to be slighted, of course.) The only real alternative, from my point of view, is hand-written code in something like C/C++ or Fortran --- which can of course be integrated with R. It would be a bit unfair to say that seeing a new method without an R implementation is cause for suspicion, but not wildly unfair.

(And, of course, people who use Excel to do statistics are not to be taken seriously.)

The R Project for Statistical Computing
Journal of Statistical Software
W. John Braun and Duncan J. Murdoch, A First Course in Statistical Programming with R [They're not kidding about being a first course --- experienced programmers may find it irritatingly slow-paced --- but they do a rather good job for total novices.]
Julian J. Faraway, Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models
Tristen Hayfield and Jeffrey S. Racine, "Nonparametric Econometrics: The np Package", Journal of Statistical Software 27:5 (2008): 1--32 [An extremely useful little R package]

Joseph Adler, R in a Nutshell [Glowing review in J. Stat. Soft.]
Adrian W. Bowman and Adelchi Azzalini, Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations
John M. Chambers, Software for Data Analysis: Programming with R
Luc Devroye, Non-Uniform Random Variate Generation [Online]
Ben Fry, Visualizing Data [blurb]
James E. Gentle, Elements of Computational Statistics
J. E. Gentle, W. Härdle, Y. Mori (eds.), Handbook of Computational Statistics [Online]
Ben Klemens, Modeling with Data: Tools and Techniques for Scientific Computing [Blurb, ch. 1; author's book site]
Matthias Kohl and Peter Ruckdeschel, "R Package distrMod: S4 Classes and Methods for Probability Models", Journal of Statistical Software 35 (2010): 10 [Use this for re-writing the power law code?]
John Maidonald, Data Analysis and Graphics Using R
Robert Mariano, Til Schuermann and Melyvn J. Weeks (eds.), Simulation-Based Inference in Econometrics: Methods and Applications
John F. Monahan, Numerical Methods of Statistics
National Research Council
- The Future of Statistical Software
- Massive Data Sets
Thomas J. Santner, Brian J. Williams and William J. Note, Design and Analysis of Computer Experiments
Sonnenberg et al., "The SHOGUN Machine Learning Toolbox", Journal of Machine Learning Research 11 (2010): 1799--1802
James C. Spall, Introduction to Stochastic Search and Optimization [Book website]
Ronald A. Thisted, Elements of Statistical Computing
Mitchell Watnik, "Early Computational Statistics", Journal of Computational and Graphical Statistics 20 (2011): 811--817

Notebooks

Computational Statistics