[Inductive Programming]   IP 1.3 (IP_1.3).
 
IP.hs --for convenience, IP collects up the other modules IP/ :
CTrees.hs --classification- (decision-), model- and regression-trees.
MixtureModels.hs --mixture modelling (clustering).
Models.hs --example models (probability distributions).
T.hs --Student's t Distribution (is a Model).
FnModels.hs --example function models (regressions).
TSModels.hs --example time-series models.
Classes.hs --class definitions for various sorts of statistical-model.
Utilities.hs --like it says.
Sci.hs --some useful scientific/numerical functions.
 
Test/ --directory, contains test programs for the IP modules; Test/Blah.hs is a test for IP/Blah.hs
ghc --make Test/Blah   --compiles a test
Example/   --directory, contains illustrative example programs for the IP modules.
ghc --make Example/Blah   --compiles an example
Data/ ----directory, contains data for use with example programs.
[www.allisons.org/ll/FP/IP/] August 2008

Selected References

L. Allison, Models for machine learning and data mining in functional programming, J. Functional Programming (JFP), 15(1), pp.15-32, [doi:10.1017/S0956796804005301], January 2005.
 
L. Allison, A Programming Paradigm for Machine Learning, with a Case Study of Bayesian Networks, ACSC2006, pp.103-111, January 2006.
 
M. B. Dale, L. Allison, and P. E. R. Dale, Segmentation and Clustering as Complementary Sources of Information, Acta Oecologica, 31(2), pp.193-202, [doi:10.1016/j.actao.2006.09.002] March-April 2007.
 
L. Allison, Added Distributions for use in Clustering (Mixture Modelling), Function Models, Regression Trees, Segmentation, and mixed Bayesian Networks in Inductive Programming 1.2. (IP 1.2) TR 2008/224, Faculty of IT, Monash U., April 2008.