psignifit is a toolbox to fit psychometric functions and test hypotheses on psychometric data. This is version 3 which will now predominantly support python.
A C++ library for principal component analysis
libpca is a C++ library for principal component analysis and related transformations. It comes with example and unit tests. libpca is successfully tested on Linux and MacOSX using g++ (>=4.6), clang++ (>=3.2), and icc (>=14.0). libpca requires Armadillo (>=3.2.4) which can be obtained as a pre-compiled package on most distributions or directly from http://arma.sourceforge.net. libpca is being developed by Christian Blume. Contact Christian at email@example.com for any questions or comments.
Math.NET aims to provide a self contained clean framework for symbolic mathematical (Computer Algebra System) and numerical/scientific computations, including a parser and support for linear algebra, complex differential analysis, system solving and more
A Matlab software routine to perform Principal Component Analysis using Covariance, Correlation or Comedian as the criterion. Though, initially developed for experiments related to fretting wear but can be effectively used to interpret experimental data from any field. The attached files contain source code as well as a sample MATLAB (.mat) data file of 13 variables. It could be replaced to the data file of your choice. The code is open source but you are requested to give credits if used. Additionally, it also has some useful functions for exporting and generating publication quality figures for different kind of figures in MATLAB
A Python package for estimating the statistical impact of features
This package let's you compute the statistical impact of features given a scikit-learn estimator. The computation is based on the mean variation of the difference between quantile and original predictions. The impact is reliable for regressors and binary classifiers. Currently, all features must consist only of pure-numerical, non-categorical values.
Python module to track the overall median of a stream of values "on-line" in reasonably efficient fashion.
Measurement uncertainties with Python.