Open Source Statistics Software
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Statistics Software
View 202 business solutionsBrowse free open source Statistics software and projects below. Use the toggles on the left to filter open source Statistics software by OS, license, language, programming language, and project status.
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gretl
A cross-platform statistical package for econometric analysis
gretl is a cross-platform software package for econometric analysis, written in the C programming language. -
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Practically Random
statistical tests & psuedo- random number generators (RNGs, PRNGs)
Randon number generation & testing. The best suite of statistical tests for fast PRNGs anywhere. Multithreaded for speed, command line tools for automation, no upper limit on data size. Also, a variety of C++ pseudo-random number generators with well designed interfaces aimed at practical uses, not just research. -
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SOFA is a statistics, analysis, and reporting program with an emphasis on ease of use, learn as you go, and beautiful output.
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R portable configures R to work with the PortableApps framework, so that R can be ran from a thumb drive or portable hard drive without leaving artifacts on the computer.
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MAGeCK
Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout
MAGeCK2 is here: https://github.com/davidliwei/mageck2 Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout (MAGeCK) is a computational tool to identify important genes from the recent genome-scale CRISPR-Cas9 knockout screens technology. For instructions and documentations, please refer to the wiki page. MAGeCK is developed by Wei Li and Han Xu from Dr. Xiaole Shirley Liu's lab at Dana-Farber Cancer Institute/Harvard School of Public Health, and is maintained by Wei Li lab at Children's National Medical Center. We thank the support from Claudia Adams Barr Program in Innovative Basic Cancer Research and NIH/NHGRI to develop MAGeCK. -
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seaborn
Statistical data visualization in Python
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Seaborn helps you explore and understand your data. Its plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots. Its dataset-oriented, declarative API lets you focus on what the different elements of your plots mean, rather than on the details of how to draw them. Behind the scenes, seaborn uses matplotlib to draw its plots. For interactive work, it’s recommended to use a Jupyter/IPython interface in matplotlib mode, or else you’ll have to call matplotlib.pyplot.show() when you want to see the plot. -
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LabPlot
Data Visualization and Analysis
LabPlot is a FREE, open source and cross-platform Data Visualization and Analysis software accessible to everyone. -
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This material enables IBM SPSS Statistics users to run code written in the R language inside Statistics. Additional free items for R in Statistics and other materials are available from the SPSS Community at www.ibm.com/developerworks/spssdevcentral
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R packages (maintained by YJLEE)
R packages for PK/PD modeling , BE/BA, drug stability, ivivc, etc.
These R packages are developed for data analysis of PK/PD modeling & simulation, bioequivalence/bioavailability (BE/BA), drug stability, in-vitro and in-vivo correlation (ivivc), as well as therapeutic drug monitoring (TDM). -
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Data Envelopment Analysis using Stata
Unified Data Envelopment Analysis commands for Stata
DEAS provides Data Envelopment Analysis tools for Stata. Version 2.0 is a major update centered on a unified dea command for Stata 16 or later. It consolidates sixteen DEA model families into one interface, keeps backward compatibility with the original inputs = outputs syntax, and adds a fast Mata two-phase revised simplex engine alongside Stata's LinearProgram() IPM engine. The package supports radial technical efficiency (CCR/BCC), SBM, additive model, radial and SBM super-efficiency, multiplier form, directional distance function, cost, revenue, profit, allocative efficiency, FDH, imprecise DEA, undesirable outputs, SBM with undesirable outputs, and congestion analysis. Extension commands provide Malmquist productivity index, window analysis, network DEA, cross-efficiency, bootstrap DEA, fuzzy DEA, and research-oriented game-theoretic DEA diagnostics. The release includes Stata help files, an OECD education application, and forthcoming Stata article and supplement materials. -
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FieldTrip
The MATLAB toolbox for MEG, EEG and iEEG analysis
FieldTrip is the MATLAB software toolbox for MEG, EEG and iEEG analysis, which is released free of charge as open source software under the GNU general public license. FieldTrip is developed by members and collaborators of the Donders Institute for Brain, Cognition, and Behaviour at Radboud University, Nijmegen, the Netherlands. The toolbox offers advanced analysis methods of MEG, EEG, and invasive electrophysiological data, such as time-frequency analysis, source reconstruction using dipoles, distributed sources and beamformers and non-parametric statistical testing. It supports the data formats of all major MEG systems (CTF, Neuromag/Elekta/Megin, BTi/4D, Yokogawa/Ricoh, FieldLine) and of most popular EEG systems, and new formats can be added easily. FieldTrip contains high-level functions that you can use to construct your own analysis protocols in MATLAB. Furthermore, it easily allows methods researchers to incorporate new methods for EEG/MEG analysis. -
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PyMC
Bayesian Modeling and Probabilistic Programming in Python
PyMC is a Python library for probabilistic programming focused on Bayesian statistical modeling and machine learning. Built on top of computational tools like Aesara and NumPy, PyMC allows users to define models using intuitive syntax and perform inference using MCMC, variational inference, and other advanced algorithms. It’s widely used in scientific research, data science, and decision modeling. -
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Statcato is a Java software application for elementary statistics. Its features include data and graph generation, probability distributions, descriptive statistics, confidence intervals, hypothesis tests, correlation, regression, and analysis of var
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statsmodels
Statsmodels, statistical modeling and econometrics in Python
statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are available for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open source Modified BSD (3-clause) license. Generalized linear models with support for all of the one-parameter exponential family distributions. Markov switching models (MSAR), also known as Hidden Markov Models (HMM). Vector autoregressive models, VAR and structural VAR. Vector error correction model, VECM. Robust linear models with support for several M-estimators. statsmodels supports specifying models using R-style formulas and pandas DataFrames. -
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AlphaPlot
Interactive scientific graphing and data analysis software.
Alpha Plot can generate different types of 2D and 3D plots (such as line, scatter, bar, pie, and surface plots) from data that is either imported from ASCII files, entered by hand, or calculated using formulas. The data is held in spreadsheets which are referred to as tables with column-based data (typically X and Y values for 2D plots) or matrices (for 3D plots). The spreadsheets as well as graphs and note windows are gathered in a project and can be organized using folders. The built-in analysis operations include column/row statistics, (de)convolution, FFT and FFT-based filters. Scripting Console support in-place evaluation of mathematical expressions and scrtipting interface to ECMAScript like dynamic scripting language(java script). The GUI of the application uses the Qt toolkit. Periodic test builds are available here http://alphaplot.sourceforge.net/test-build.html -
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GIT quick statistics
An efficient way to access various statistics in git repository
git-quick-stats is a simple and efficient way to access various statistics in a git repository. Any git repository may contain tons of information about commits, contributors, and files. Extracting this information is not always trivial, mostly because there are a gadzillion options to a gadzillion git commands. For those who prefer to utilize command-line options, git-quick-stats also has a non-interactive mode supporting both short and long options. You can change to the legacy color scheme by toggling the variable _MENU_THEME between default and legacy. You can set variable _GIT_LIMIT for limited output. It will affect the "changelogs" and "branch tree" options. You can also exclude files from the stats. Note that it works with any alphanumeric, glob, or regex that git respects. -
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broom
Convert statistical analysis objects from R into tidy format
broom is part of the tidymodels ecosystem that converts statistical model outputs (e.g. from lm, glm, t.test, lme4, etc.) into tidy tibbles — standardized data frames — using functions tidy(), glance(), and augment(). These are easier to manipulate, visualize, and report programmatically. -
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rethinking
Statistical Rethinking course and book package
This R package accompanies Richard McElreath’s Statistical Rethinking (2nd edition), offering utilities to fit and compare Bayesian models using both MAP estimation (quap) and Hamiltonian Monte Carlo via RStan (ulam). It supports specifying models via explicit distributional assumptions, providing flexibility for advanced statistical workflows. -
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JDistlib
Java library of statistical distribution
This project moved to Github: https://robbyjo.github.io/JDistlib/ A Java package that provides routines for various statistical distributions. Based on R version 2.14.1 (continuously updated; current as of R v4.6.1). The major difference is that JDistlib is thread safe. The library contains the density (pdf), cumulative (cdf), quantile, and random number generator (RNG) routines of the following distributions: Ansari, Beta, Binomial, Cauchy, Chi square, Exponential, Fisher's F, Gamma, Geometric, Hypergeometric, Kendall, Logistic, Log normal, Negative binomial, Noncentral beta, Noncentral chi square, Noncentral f, Noncentral t, Normal, Poisson, Sign Rank, Spearman, Student's T, Tukey, Uniform, Weibull, Wilcoxon, and many more. Normality tests, such as: Kolmogorov-Smirnov, Anderson-Darling, Cramer-Von Mises, D'Agostino-Pearson, Jarque Bera, Kolmogorov-Lilliefors, Shapiro-Francia, Shapiro-Wilk. And many others. -
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NAVAL-SEM
Award-winning open-source offline SEM platform for PLS-SEM & CB-SEM
NAVAL-SEM is an award-winning, free and open-source offline Structural Equation Modeling (SEM) platform supporting PLS-SEM and CB-SEM workflows. Designed for researchers, PhD scholars, professors, and analysts, it provides visual model building, measurement assessment, structural model analysis, and reproducible research workflows without subscriptions or cloud dependency. Features include: • PLS-SEM & CB-SEM • Bootstrapping, mediation, moderation & conditional process analysis • HTMT, AVE, Composite Reliability, Cronbach's Alpha, Fornell-Larcker • VIF, f² effect size, indirect effects, Q² blindfolding, PLSpredict, CVPAT • Multi-Group Analysis (MGA), HOC, MICOM • IPMA, NCA, nonlinear effects, FIMIX-PLS, PLS-POS, endogeneity checks • EFA, CVI, measurement validation, fsQCA • APA report generation and R/Python/Lavaan export Fully offline and available for Windows, macOS, and Linux. Recognized with the SourceForge Rising Star Award. -
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PhEq_bootstrap
FDA's f2 computation with bootstrap technique
This program was developed as a help in establishing pharmaceutical equivalence by use of FDA f2 coefficient. It was designed to help with f2 computation in cases when intra- and inter-batch variability is large, namely RSD>10%. The use of statistical bootstrap technique allows to implement confidence interval (CI) into the f2 coefficients resulting in overcoming of their major drawback in the original metrics. The algorithm provides possible “worst case scenario” of f2 values, thus supporting claim about pharmaceutical equivalence. The target users are researchers from industry and academia dealing with pharmaceutical equivalence problem. The software is Open Source. It was developed in Lazarus environment, therefore source code is available in ObjectPascal. -
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##### 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.
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UnBBayes
Framework & GUI for Bayes Nets and other probabilistic models.
UnBBayes is a probabilistic network framework written in Java. It has both a GUI and an API with inference, sampling, learning and evaluation. It supports Bayesian networks, influence diagrams, MSBN, OOBN, HBN, MEBN/PR-OWL, PRM, structure, parameter and incremental learning. Please, visit our wiki (https://sourceforge.net/p/unbbayes/wiki/Home/) for more information. Check out the license section (https://sourceforge.net/p/unbbayes/wiki/License/) for our licensing policy. -
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The Simplest Manual Counter
Manual counter with the keyboard or the mouse on images
The only open source counter to count any items the simplest and easiest way with the keyboard, or the mouse specifically on images. After associating a key to each item, or a predefined graphical symbol for images, pressing the key or clicking on the image increments its associated counter, and displays (for the images) the symbol at the mouse's pointer location. Such a project is so simple a child could use it! -
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mlpy
Machine Learning Python
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.