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Software

This is my software page. Most of it relates in some way to my research interests in Bayesian computation. I've decided to split stuff up by language rather than by topic, which may seem a bit strange, but it makes more sense to me.

Please note that much of this code is old, buggy and unmaintained! I continue to make it available, as some of it is still useful, still works, and still serves to illustrate how to go about writing scientific software. However, if you are interested in that topic, you might find following my blog to be easier than trawling through my old code...

Files ending .tgz are gzipped tar files, and can be unpacked on GNU/Linux systems with a command like tar xvfz foo.tgz. Note that some of these should be unpacked into an empty directory - do a tar tvfz foo.tgz first to check!

• ANSI C
• Java
• LISP-STAT
• Perl
• Python
• R
• Sather
• SBML

ANSI C

C isn't a particularly beautiful language, but it is very powerful and efficient, and its ubiquity makes it hard to ignore. When I write C, I generally write pure ANSI C. Most of my code relies on the GNU Scientific Library, which makes scientific computing in C much more practical. Because of its speed, C is my preferred language for developing MCMC-related code. I have a set of C links and a set of links for the GSL.

• GDAGsim is a C library for carrying out conditional simulation for large sparse Gaussian DAG models. It is useful for doing Bayesian inference in large hierarchical linear models. (Last updated: 21/8/2002)
• gillespie.tgz is a simple simulator for stochastic kinetic models. It accepts models described using a subset of SBML Level 1. See the enclosed README.txt for further details. Some example models are packaged separately. Note that Carole Proctor has adapted this code so that it accepts SBML Level 2 models - that code is distributed separately as gillespie2. (Last updated: 21/7/2004)
• gsl-sprng.h is a bit of code which wraps up the SPRNG 2.0 parallel random number generator as a GSL RNG, so that one can use all of the nice GSL random number distribution functions in MPI-based parallel stochastic simulation codes which rely on SPRNG for independent random number streams. You need to have GSL, MPI and SPRNG all up and running on a cluster before attempting to use this code. (Last updated: 28/8/2002)
• meparse.tgz is a mathematical expression parser. It builds a parse tree which may be evaluated repeatedly for different variable settings. Variables are handled by a user-defined callback function. It is not well-documented, but see the README and example code to get the basic idea. It is used by my "gillespie" simulator. Please note that this parser is buggy! I do not recommend using it! (Last updated: 22/1/2004)
• mlmu2.tgz is a simple bit of C code for fitting unbalanced two-level normal models using a 2-block MCMC sampler. (Last updated: 26/6/2000)
• mlmu3.tgz is the corresponding code for a three level nested hierarchical normal linear model. Again, a 2-block MCMC sampler is used. This is the code referred to in "Conditional simulation from highly structured Gaussian systems, with application to blocking-MCMC for the Bayesian analysis of very large linear models". (Last updated: 26/6/2000)
• pbc.tgz is the example code from my chapter on Parallel Bayesian computation in the Handbook of Parallel Computing and Statistics. It includes all the code for the case study on stochastic volatility modelling. (Last updated: 11/2/03)
• stochInf.tgz is a package for carrying out MCMC-based Bayesian inference for stochastic kinetic models using time-course data. See the project web page for further details. This code is now very out of date - see the CaliBayes project for more recent work. (Last updated: 21/7/04)
• sv.tgz is some code for fitting univariate discrete time stochastic volatility models and multivariate factor stochastic volatility models in the "Shephard and Pitt" style. It's essentially just a port of my original Sather classes. (Last updated: 9/6/04)

Java

Java is now a reasonably good object-oriented programming language, with a huge standard library which greatly facilitates application development. It also has some reasonable scientific libraries, most notably COLT, parallel COLT and Apache Commons Math.

• CaliBayes was a Java based project I was involved in the development of. It is a web-services based system for calibrating stochastic and deterministic systems biology models encoded in SBML using time course experimental data.

LISP-STAT

LISP-STAT is/was a wonderful environment for object-oriented interactive statistical computing, but its reliance on LISP means that it will only ever have a cult following. In fact, it seems to be slowly dying a death, due partly to the success of 'R'. I wouldn't now recommend LISP-STAT to people who aren't already familiar with it - 'R' is probably a safer bet, despite its faults. I have some LISP-STAT links which are a useful starting point.

• BAYES-LIN is my main LISP-STAT project. It provides a prototype system for carrying out Bayes linear local computation. (Last updated: 7/4/2000)

Perl

Perl is a revolting but exceedingly useful language which is great for text processing and related activities. I have a few perl links.

• tab2bugs takes a tabular MCMC output file and turns it into BUGS-style output and index files ready for reading into CODA. It requires an output file (such as produced by mlmu2) with name of the form myoutput.tab which is converted using tab2bugs myoutput. It makes multiple passes through the file, avoiding the need for large amounts of memory. (Last updated: 13/2/2002)
• thin is useful if you have a whopping great output file which you can't read into CODA. Thin your tabular output with this before converting. (Last updated: 4/4/2000)
• burn is useful for manually chopping some burn-in iterations out of a tabular MCMC file. eg. you may want to do something like myprog 100000 | burn 10000 | thin 10 > myout.tab. (Last updated: 29/5/01)
• chop is for trimming the last line off the end of an file - useful for inspecting the results of a running MCMC code, as the last line of the output file is usually incomplete in this case. eg. chop < myout.tab > snapshot.tab. (Last updated: 19/5/04)

Python

Python is a wonderful object-oriented scripting language. I now use it instead of perl for doing things such as text processing, web/internet programming/CGI scripts, XML processing, database interfacing, GUI development, etc. It has a simple syntax (that many people refer to as "executable pseudo-code"), and is in fact a great language to learn to program with (especially object-oriented programming concepts). It isn't as fast as Java, but it is much quicker and easier to develop code in Python than it is in Java. Everyone should know Python! I have some python links.

• SBML-shorthand and mod2sbml.py - a language for describing systems biology models. (Last updated: 1/7/05)
• isbn.py is a python module for messing about with ISBN-10 and ISBN-13 numbers, including validation, computation of check-digits, and conversion from one form to the other. Do python, import isbn, help(isbn) for full documentation including examples. (Last updated: 11/8/07)
• mcmc.py is a short script for massaging tabular MCMC output files. Supersedes the above perl scripts thin, burn and chop. Do mcmc.py -h for usage info. (Last updated: 26/1/05)
• validateSBML.py is a short script which calls the BASIS SBML validation Web Service. Useful where you have python, but not libSBML and the libSBML python wrappers. Requires the python SOAP library. (Last updated: 22/1/05)

R

R is a very good general purpose environment for statistical computing, with excellent graphical output and a huge range of libraries and packages. It is technically inferior to LISP-STAT in many ways, but its simple intuitive syntax means that it has a great following. R has now become the de facto standard environment for serious interactive statistical computing. I have some links for S, S-PLUS and R.

• Discrete stochastic models test suite - a suite of SBML models and correct output, used for testing the correct behaviour of discrete stochastic simulators. The associated test code is written in R. (Last updated: 12/7/2005)
• rdiric.r is a simple function for simulating Dirichlet random quantities in a reasonably efficient way. eg rdiric(10,c(1,2,3)) simulates a 10x3 matrix whose rows are independent Dirichlet random quantities with parameter vector (1,2,3). (Last updated: 10/12/2000)
• rmn.r is a simple function for simulating multinomial random quantities in a reasonably efficient way. eg rmn(100,1000,c(0.5,0.3,0.2)) simulates a 100x3 matrix whose rows are independent multinomial random quantities based on 1000 trials with proportions (0.5,0.3,0.2). (Last updated: 6/6/2001)
• itosim.r is a function for straightforward simulation of univariate diffusion processes. Examples of use are included. (Last updated: 10/12/2000)
• gammaprior.r is a function for prior belief elicitation of variance components. Given a prior probability interval for the standard deviation, it attempts to find a gamma prior on the precision which is consistent. eg. For a 95% probability interval of [10,20] on the standard deviation scale, gammaprior(10,20,0.95) will find an appropriate gamma prior for the precision. (Last updated: 9/3/2001)
• plottab.r is a function for graphical display of the contents of a tabular MCMC output file. Useful for having a "quick peek" at some MCMC output before loading it into CODA for a more detailed analysis. Requires the "ts" library to be loaded. (Last updated: 1/8/2003)
• fmc.r is a set of functions for messing around with (discrete time) finite state Markov chains. Example of use is included. (Last updated: 18/9/2002)
• These days, any non-trivial R project that I'm involved with I develop on R-Forge as an R package. See my R-Forge page for a list of projects that I am involved with.

Sather

Sather is (in principle) an excellent programming language. It is a safe, efficient object-oriented language, well-suited for scientific computing, but only has a cult following, partly due to the fact that the only available compilers suck. In fact, sather too now seems to be dying a death - why is it that the best languages always get killed off by inferior competition?! My page of sather links provides plenty of information for the new user.

• kalman.tgz is a set of sather classes for Kalman filtering, smoothing and simulation smoothing of dynamic linear state-space models. It is well documented, and has a nice interface, but is not implemented is a very efficient way. See the enclosed README. Note that this package also contains a class RND2, which provides a set of routines for multivariate random number generation, which may be useful independently of the main classes. (Last updated: 27/5/2001)
• fsv.tgz contains classes for Bayesian MCMC analysis of univariate and multivariate factor stochastic volatility models in the "Shephard and Pitt" style. See the enclosed README for further info. (Last updated: 27/5/2001)

SBML

SBML (Systems Biology Markup Language) is not a conventional programming language, but an XML-based markup language for describing the biochemical network models that arise in Systems Biology. It is the closest thing to a standard in that area. I have some links for SBML and XML.

• SBML-shorthand - a language for describing systems biology models. (Last updated: 1/7/05)
• Discrete stochastic models test suite - a suite of SBML models and correct output, used for testing the correct behaviour of discrete stochastic simulators. (Last updated: 12/7/2005)