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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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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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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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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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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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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SWP Project
SWP is for Weighted Points
SWP is a script language to manipulate sets of isolated weighted point, mainly analysis of fractal and other statistical properties of such sets. Originally started as a tool to explore fractal properties of cosmic fractal like large-scale distribution of galaxies. Could be used for any similar sets either. -
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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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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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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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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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git-stats
Local git statistics including GitHub-like contributions calendars
git-stats is a Node.js command-line tool and library for tracking Git activity locally. It renders commit history as an ANSI contribution calendar similar to the graph displayed on GitHub profiles. Users can select custom date ranges, display global activity, filter commits by author, and compare additions with deletions. Author statistics can also be presented as terminal pie charts. Commit records are stored in a configurable local data file, with a JavaScript configuration file controlling themes and default behavior. A post-commit hook can record future work automatically, while companion importers can add existing repository history. Raw JSON output allows other tools to create HTML pages, images, or custom visualizations. -
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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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NAVAL-SEM
Free offline SEM software with HTMT, bootstrapping & exports
NAVAL-SEM is a free, open-source, fully offline desktop application for Structural Equation Modeling (SEM), supporting both PLS-SEM and CB-SEM workflows. Designed for researchers, PhD scholars, professors, students, and analysts, it enables advanced quantitative research without subscriptions, cloud dependencies, or proprietary software restrictions. Key features include visual drag-and-drop model building, bootstrapping, mediation analysis, HTMT, AVE, Composite Reliability (CR), Cronbach's Alpha, model fit assessment, and Multi-Group Analysis (MGA). NAVAL-SEM also supports export to R, Python, and Lavaan for reproducible research workflows. Built for academic and professional research, NAVAL-SEM helps users conduct scale validation, measurement assessment, and structural modeling across disciplines including marketing, management, psychology, education, healthcare, and social sciences. Available for Windows, macOS, and Linux. -
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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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Chuck Coleman's SAS Codes
SAS Codes
SAS codes for LMG variable importance, Box-Cox transformation including interactions. Documentation and PowerPoint slides. Directories under "Files" with descriptions: NESUG 2014: "A Fast, High-Precision Implementation of the Univariate One-Parameter Box-Cox Transformation Using the Golden Section Search in SAS/IML®" SESUG 2017: "Decomposing the R-squared of a Regression Using the Shapley value in SAS®" SESUG 2019: "Conditionally Executing Data Steps and Statements Based on the Presence of Variables in a SAS® Dataset" GASP 2020: "Online Winsorization of the Survey of Construction’s Price Estimates" My SESUG 2015 materials can also be found in my project "constrainingarrays" at https://sourceforge.net/projects/constrainingarrays/. -
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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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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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Please participate in the SURVEY on rgedit's future: https://www.surveymonkey.com/s/VNMMJMJ your answers are much appreciated! Gedit (Gnome editor, www.gedit.org) plug-in allowing it to become an easy-to-use and yet light-weight IDE for the statistical programming environment, R (www.r-project.org).
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KinetDS is a software for curve fitting particularly designed for kinetic (mechanistic and empirical) description of a substance dissolution from solid state. It was primarily designed for handling pharmaceutical dissolution tests
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AtteStat
The package for statistical data analysis and applied mathematics
Less means - more power. AtteStat is an add-in for desktop versions of the Microsoft Excel. Winner of contest Microsoft Office Extensions (PC Magazine RE) in 2006. Registered 2002-05-23 with the Federal Service for Intellectual Property. Completely free. No donations required. Comes with absolutely no warranty. Rating: Everyone (All ages). MEDICAL DISCLAIMER: This software is not certified for use in healthcare and should not be used for diagnostic or treatment purposes. NOTICE FOR EU RESIDENTS: This software is NOT compliant with EU regulations, including GDPR and the AI Act. It is not intended for use within the European Union. By downloading this software, you acknowledge that you do so at your own risk and waive any rights provided by EU consumer protection laws. NOTICE FOR RF RESIDENTS: This software is not included in the Unified Register of Russian Programs for Electronic Computers and Databases. Support may be limited in the RF region due to local regulatory constraints. -
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bootf2BCA
computing f2 bootstrap CI BCA
Computing similarity factor (f2) bootstrap bias corrected and accelerated confidence interval -
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The new version of MinimPy (MinimPy2) is available for download at: https://osdn.net/dl/minimpy2/MinimPy2.zip Please send your feedback about this new version to mahmood.saghaei@gmail.com (include MinimPy2 in the subject) ============================================================ MinimPy is a desktop application program for sequential allocation of subjects to treatment groups in clinical trials by using the method of minimization. Comprehensive reference help is available at http://minimpy.sourceforge.net MinimPy has been fully described in the following article: Saghaei, M. and Saghaei, S. (2011) Implementation of an open-source customizable minimization program for allocation of patients to parallel groups in clinical trials. Journal of Biomedical Science and Engineering, 4, 734-739. doi: 10.4236/jbise.2011.411090. Available at: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=8518
