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A C library to handle compositional (=closed) data, like proportions.
Inspired by Aitchison 2003 (http://www.amazon.com/The-Statistical-Analysis-Compositional-Data/dp/1930665784/) this library aims to make compositional dataanalysis rigorously.
A collection of compact, powerful programs and functions, written in C, for the analysis and transformation of data. No object or structure dependencies, no fancy interfaces - just good tools.
The final build of this software now is distributed in R, embedded in "RedeR': an R/Bioconductor package for hierarchical and nested network analysis... more about RedeR: http://bioconductor.org/packages/2.9/bioc/html/RedeR.html
A population-based method for DNA copy number analysis: recurrent copy number aberration indentification in multiple samples (with no need of single-sample calling). Developed for a quick analysis of high resolution and large population data.
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This project hosts tools used for analysis of Gaussian Mixture Distributions (GMDs) which are used for statistical signal processing. The tools are libraries for implementing GMD operations and programs used to analyze properties of GMDs.
An application built onto the Open Table Explorer engine for the acquisition and analysis of home energy consumption and solar energy production.Moved to github as Open Table Explorer
An R package for detailed inspection and analysis of LCMS data. An R package developed by Sukhdeep Singh at Department of Surgery and Cancer, Imperial College London,UK.
1. Create an object-oriented python script that can represent mathematical concepts and their properties. 2. Represent all numeric values exactly. 3. Provide a variety of formats to export or embed representations of the mathematical concepts.
This program generates customizable hyper-surfaces (multi-dimensional input and output) and samples data from them to be used further as benchmark for response surface modeling tasks or optimization algorithms.
PGAF provides a framework tuned, user-specific genetic algorithms by handling I/O, UI, and parallelism. It is designed for optimizing functions that take a "very long time" to evaluate.