JMP is a powerful visual analytics tool from SAS, widely used for design of experiments, quality control, and interactive data exploration. Below is a curated list of 20 alternative software packages—both open‑source and commercial—that provide comparable statistical analysis, modeling, and visualization capabilities.
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Open‑source programming language and environment for statistical computing and graphics; extensive CRAN package ecosystem for everything from basic tests to advanced machine learning.
Python libraries such as pandas, NumPy, SciPy, statsmodels, and scikit‑learn deliver a full suite for data manipulation, statistical testing, and predictive modeling.
Enterprise‑grade analytics platform offering a broad range of statistical procedures, data management, and reporting tools; the original home of JMP.
IBM’s user‑friendly statistical package with point‑and‑click interface, strong survey analysis features, and extensive reporting options.
Powerful statistical software for data analysis, data management, and graphics; popular in economics, epidemiology, and social sciences.
Statistical software focused on quality improvement and Six Sigma; intuitive UI for DOE, regression, and control charts.
Comprehensive analytics suite offering data mining, predictive modeling, and interactive visualizations for scientific and business users.
High‑performance language for technical computing with Statistics and Machine Learning Toolbox for advanced modeling and simulation.
Modern high‑performance language with packages like StatsBase, GLM, and Turing for statistical modeling and Bayesian inference.
Free, open‑source graphical software for Bayesian and classical statistics; point‑and‑click interface similar to SPSS.
Open‑source statistical platform built on R; offers a spreadsheet‑like UI with real‑time results and extensible modules.
Open‑source workflow engine for data blending, analytics, and reporting; integrates R, Python, and Spark nodes.
Visual data science platform for machine learning, predictive analytics, and statistical modeling without coding.
Self‑service analytics tool that combines data preparation, spatial analytics, and statistical modeling in a drag‑and‑drop workflow.
Automated machine‑learning platform that includes built‑in statistical tests, model diagnostics, and explainability features.
Open‑source data mining and visualization suite with widgets for statistical tests, clustering, and predictive modeling.
Cloud‑based environment for data science that integrates R, Python, and SPSS Modeler for statistical analysis and AI.
Specialized SAS product for data mining and predictive modeling, offering a visual workflow and extensive statistical procedures.
Legacy statistical package still used in some biomedical research labs; provides classic tests, ANOVA, and survival analysis.
Statistical analysis system with strong graphical capabilities, DOE tools, and a user‑friendly interface for non‑programmers.