Open Source Linux Statistics Software - Page 2

Development of exact and approximate methods (Importance Sampling and MCMC based) for computing likelihoods under the standard population genetic models of mutation,migration & recombination. Project issues are mainatined at https://freecode4susant.atlassian.net/browse/COALESCENT

An R package implementation of a consensus clustering methodology. This package allows users to perform re-sampling statistics based clustering using multiple clustering algorithms to assess the robustness of both clusters and members of clusters.

This project hosts the code and the documents which were created by Francesco Montorsi during his PhD at the University of Modena and Reggio Emilia, Italy, about indoor and outdoor localization and navigation techniques. The code (mostly MATLAB code) is publicly available to enable reproducible research.

ADaMSoft is a free and Open Source Data Mining software developed in Java. It contains data management methods and it can create ready to use reports. It can read data from several sources and it can write the results in different formats.

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.

Statistical models with python using numpy and scipy. Currently covers linear regression (with ordinary, generalized and weighted least squares), robust linear regression, and generalized linear model, discrete models, time series analysis and other statistical methods.

This project adds a ribbon to excel with a number of statistical buttons. This is not intended to be a full statistical analysis package (like R), but only a source for quick visualizations and calculations. For Excel2007 and up

EnvStats is an R package for environmental statistics. It is the open-source successor to the commercial module for S-Plus© called "EnvironmentalStats for S-Plus", which was first released in April, 1997. The EnvStats package, along with the R software environment, provides comprehensive and powerful software for environmental data analysis. EnvStats brings the major environmental statistical methods found in the literature and regulatory guidance documents into one statistical package, along with an extensive hypertext help system that explains what these methods do, how to use these methods, and where to find them in the environmental statistics literature. Also included are numerous built-in data sets from regulatory guidance documents and the environmental statistics literature. EnvStats combined with other R packages (e.g., for spatial analysis) provides the environmental scientist, statistician, researcher, and technician with tools to “get the job done!”

Math Simulator is a java-based application which simulates various mathematical techniques.It is extremely useful for the Engineering students and for professionals. Most importantly, unlike most others Math Simulator is an Application rather than a java - library. So, user is not required to have any computer background. A Unique feature of Math Simulator is that it allows you to save queries for later reference. Hence you can save the problems along with their results.

##### The RKWard project has moved! ##### Find the most recent news and downloads at http://rkward.kde.org . RKWard aims to provide an easily extensible, easy to use IDE/GUI for R. RKWard tries to combine the power of the R-language with the (relative) ease of use of commercial statistics tools.

QPNet (Quick Petri Net) is a fast Petri Nets' emulator, created by students of Moscow State Technical University of Radio Engineering, Electronics and Automation. It brings benefits for the educational process, while being under further development and extension. QPNet это быстрый эмулятор сетей Петри, созданный студентами МИРЭА. Он применяется в учебном процессе, а так же продолжает разрабатываться и эволюционировать.

Main application is twofold: first to convert genotype SNP data into formats of different imputation tools like PLINK MACH, IMPUTE, BEAGLE and BIMBBAM, second to transform imputed data into different file formats like PLINK, HAPLOVIEW, EIGENSOFT and SNPTEST. Readable file formats: plink-pedigree (ped and map), plink-raw, plink-dosage, mach , minimac, impute, snptest, beagle and bimbam. Similarly all kinds of imputation of outputs are also accepted. Formats which can be generated by fcGENE: plink-pedigree, plink-raw, plink-dosage, mach-inputs, minimac-inputs, impute-inputs, beagle-inputs and bimbam-inputs, HAPLOVIEW-inputs, EIGENSOFT-inputs. Further application: -obtaining templates of necessary imputation commands and commands of other imputation tool - Quality control according as MAF,HWE & CALLRATE. key words: genotype transformation, convert genotype format, imputation output, PLINK, IMPUTE, MACH, minimac, HAPLOVIEW, BEAGLE, BIMBAM,EIGENSOFT.

mathSuite is a very powerful Mathematical Suite which deals principally with complex algebric and geometric operations. It is powered by the fabulous ExprEval C Parser. The main purpose of this project is fast math-oriented algorithm virtualization, with an optimized direct text interface. Also it gives you a very powerful and fast Calculus Environment which let you handle easily interchangeable variable lists, matrices, LOGS and Settings Layouts with optimized Items Lists Managing Engine. You'll also be able to execute your own scriptfiles with a basic math-oriented beta script language. Some feature regarding Matrices and Linear Algebra Operations are LU-Factorization, SVD Decomposition, Rank Calculator, Ill Condition Checking and more... There are also a very powerful Linear System Solver and Basic PRELoaded Functions Integrator Engine.

JStats is a small but powerful Java application/applet for conducting statistical tests. The following tests are supported: * Parametric tests: T-test, ANOVA, Repeated Measures ANOVA * Non-parametric tests: Wilcoxon Rank-Sum, Wilcoxon Signed-Ranks, Kruskal-Wallis, Friedman * Check if datasets are normally distributed: Jarque-Bera, Shapiro-Wilk * Check if datasets have equal variances: F-test, Bartlett's test, John, Nagao and Sugiura's test * Correlation: Correlation coefficient, Spearman Rank correlation, linear regression * Confidence intervals test * Outliers: Generalized Extreme Studentized (ESD) test, outliers in ANOVA The latest version is available as applet on http://aiguy.org/Statistics.html

A MATLAB package to simulate sample paths of the solution of a Itô or Stratonovich stochastic differential equation (SDE), compute statistics and estimate the parameters from data. A note of caution: SDE Toolbox is no more developed but it's still downloadable. Its inferential capabilities can be considered surpassed (at best). Actually the parameter estimation methods were already far from the state-of-art when the project began in 2007 (!). The considered implemented parametric and non-parametric Monte Carlo likelihood methods were chosen for their ability to treat both one-dimensional and multivariate SDE systems, although the quality of the inferential results can't match those obtained using more advanced techniques. Nevertheless the toolbox capabilities to simulate numerical solutions of SDE systems are still valid and can serve as a useful starting point to those willing to simulate stochastic dynamical models easily.

The minepy homepage has moved to http://minepy.readthedocs.io. The download page is now at https://github.com/minepy/minepy/releases.

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 project (CvHMM) is an implementation of discrete Hidden Markov Models (HMM) based on OpenCV. It is simple to understand and simple to use. The Zip file contains one header for the implementation and one main.cpp file for a demonstration of how it works. Hope it becomes useful for your projects.

SalStat is a small application for statistical analysis emphasising the sciences and social sciences (particularly Psychology). The project is designed around the user interface which has been designed to be simple to use. Think SPSS, but better!

Metagenomics yields enormous numbers of microbial sequences that can be assigned a metabolic function. Using such data to infer community-level metabolic divergence is hindered by the lack of a suitable statistical framework. Here, we describe a novel hierarchical Bayesian model, called BiomeNet (Bayesian inference of metabolic networks), for inferring differential prevalence of metabolic networks among microbial communities. To infer the structure of community-level metabolic interactions, BiomeNet applies a mixed-membership modelling framework to enzyme abundance information. The basic idea is that the mixture components of the model (metabolic reactions, subnetworks, and networks) are shared across all groups (microbiome samples), but the mixture proportions vary from group to group. Through this framework, the model can capture nested structures within the data. BiomeNet is unique in modeling each metagenome sample as a mixture of complex metabolic systems (metabosystems).

Blueprint XAS is a Matlab-based suite designed for the processing and analysis of near-edge x-ray absorption spectroscopy (XAS) data. The suite is designed primarily to assist users in exploring reasonable fit solutions while minimizing user bias.

DeDAY (Demography Data Analyses) is a tool of analyzing demography data. It supports Gompertz, Weibull and Logistic distributions. DeDay also supports mixed mortality models based on these distribution such as the Gompertz-Makeham distribution. Distributions such as Gompertz describes only age-dependent mortality, which increases over time. Mixed mortality models, such as in Gompertz-Makeham distribution, consider a more general case where mortality is consist of both age-dependent and in-dependent mortality. Mixed models partition mortality into exogenous and endogenous components, so that the intrinsic survivorship can be estimated without the interference from extrinsic noise. DeDAY supports both interval-censored data and exact event-time data. Using MLE (Maximum Likelihood Estimate), DeDAY fits statistic model to the data. DeDAY also calculates the variances and the multi-dimensional confidence limits of model parameters. DeDAY is free for academic users.

IterInt is a numerical integration package that implements iterated numerical integration methods. Iterated methods can be used to compute low dimensional (less than ten, for example) integration problems to get very accurate results.

PeCalc allows making calculations with integer fractions, complex numbers, complex matrices, rational functions and complex polynomials. It also allows making changes of numeric base, prime factor decomposition, solving linear equation systems, statistical and financial computations, rational fraction decomposition and plotting some control engineering graphs. PeCalc includes comprehensive user help in English and Spanish. Currently PeCalc runs on Android and MS Windows. A version for GNU Linux is planned for the near future. It has been developed in Object Pascal using Embarcadero Delphi. A Lazarus/Free Pascal version will be available soon too.

The Robust Correlation Toolbox is a free collection of Matlab functions allowing to visualize data in univariate and bivariate space, check assumptions of normality and homoscedasticity and compute Pearson's and Spearman's, percentage bend, and skipped correlations with bootstrapped confidence intervals - see http://www.frontiersin.org/Quantitative_Psychology_and_Measurement/10.3389/fpsyg.2012.00606/full -- code moved to https://github.com/CPernet/robustcorrtool