Open Source FreeBSD Statistics Software

The truth can't be complicated. Mathematical & engineering components (abbreviation ME . com) for development of the software are the comprehensive collection of original fastest algorithms for scientific computing. Not based on any known software library. Used in AtteStat, StatAnt, Criterial projects and another with rights saved include in educational purposes. Registered 2003-03-21 with the Federal Service for Intellectual Property. Completely free. No donation required. Mathematical & engineering components come with absolutely no warranty. The software is not certified for use in healthcare. Please do not use this software for purposes related to diagnosing diseases or making treatment decisions. The software is not certified in the European Union. Please do not download, distrubute or use the software in countries of the European Economic Area.

mlpy is a Python module for Machine Learning built on top of NumPy/SciPy and of GSL. mlpy provides high-level functions and classes allowing, with few lines of code, the design of rich workflows for classification, regression, clustering and feature selection. mlpy is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License version 3. mlpy is available both for Python >=2.6 and Python 3.X.

This package provides high-level interface utilities for MCMC models via Just Another Gibbs Sampler (JAGS), facilitating the use of parallel (or distributed) processors for multiple chains, automated control of convergence and sample length diagnostics, and evaluation of the performance of a model using drop-k validation or against simulated data. Template model specifications can be generated using a standard lme4-style formula interface to assist users less familiar with the BUGS syntax. A JAGS extension module provides additional distributions including the Pareto family of distributions, the DuMouchel prior and the half-Cauchy prior.