This package contains Virtual Hybridization tools. Virtual Hybridization uses sets of short probes to generate datasets for comparative genomics: given a DNA sequence and a set of probes, the typical output will give a sequence of oriented probe hits along the DNA sequence. Other tools are supplied to allow simple manipulations such as format conversion and extraction of permutations.
SHOGUN is a machine learning toolbox whose focus is on large scale kernel methods and especially on Support Vector Machines (SVM). It provides a generic SVM object interfacing to several different SVM implementations, all making use of the same underlying, efficient kernel implementations. Apart from SVMs and regression, SHOGUN also features a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons, and algorithms to train hidden Markov models. SHOGUN can be used from within C++, Matlab, R, Octave, and Python.
PrIMETV is a program that can visualize tree-within-tree phenomena such as gene/species tree reconciliations. Output can be given in a range of formats, including PostScript, Fig, and SVG. Thus, it is possible to easily edit the final illustration in many available drawing programs. PrIMETV can also directly manipulate important attributes such as color and layout policy.
Trans (short for Transmuter Programming Language) is an extremely dynamic, biologically-inspired prototyping language providing a framework for experimenting with naturally evolving systems of objects over the net, and for exploring new ideas about recombinant software, code morphing, and evolutionary programming. Trans is also a very capable general-purpose programming language. It's fast, flexible, compact, object-oriented, highly extensible, and easy-to-learn. It can be used for rapid prototyping, or as a scripting language, an embedded language, a network server or client, a system of cooperating network nodes, a real-time control and monitoring system, and more.
PCP (Pattern Classification Program) is a machine learning program for supervised classification of patterns. It runs in interactive and batch modes, and implements the following machine learning algorithms and methods: k-means clustering, Fisher's linear discriminant, dimension reduction using Singular Value Decomposition, Principal Component Analysis, feature subset selection, Bayes error estimation, parametric classifiers (linear and quadratic), pseudo-inverse linear discriminant, k-Nearest Neighbor method, neural networks, Support Vector Machine algorithm (SVM), model selection for SVM, cross-validation, and bagging (committee) classification.
Cactus is a general, modular, parallel environment for solving systems of partial differential equations. The code has been developed over many years by a large international collaboration of numerical relativity and computational science research groups and can be used to provide a portable platform for solving any system of partial differential equations.