Jmp Version History [better] -
In 2007, JMP 7.0 was launched, bringing advanced analytics capabilities to the software. This version included new features, such as nonlinear regression, generalized linear mixed models, and enhanced simulation capabilities.
The version history of JMP reflects the software's evolution from a simple, interactive statistical analysis tool to a comprehensive analytics platform. With each new version, JMP has built upon its strengths, incorporating emerging technologies and trends to stay ahead of the curve. As data analysis and visualization continue to play an increasingly important role in business and research, JMP remains a leading choice for professionals seeking to extract insights and knowledge from their data.
Focused on connecting JMP with other popular analytics environments. jmp version history
A fully integrated, native Python environment running side-by-side with JSL. Users can install Python packages directly via a built-in package manager, pass data seamlessly between Python and JMP tables, and write scripts using native Python syntax.
Formula Depot for organizing predictive models, and dashboard publishing directly to internal servers. JMP 14 (2018) In 2007, JMP 7
Direct Python integration , projects for organizing multiple files, and functional data analysis. The Predictive Era: JMP 15 to JMP 17 (2019–2023)
Choice modeling for consumer research, Gaussian process modeling, and an interactive, drag-and-drop Graph Builder interface. JMP 9 (2010) Focus Area: Extensibility and programming integration. With each new version, JMP has built upon
(released late 2023) is the most significant update in years:
These iterations embraced modern computing power. JMP 8 introduced the Graph Builder , a drag-and-drop environment that remains the centerpiece of the software’s visual discovery philosophy today. The Era of Big Data and Visualisation (2010–2019)
Introduction of the JMP Scripting Language (JSL).
JMP 18 deeply embedded a native Python environment directly within JMP, enabling analysts to write Python code that seamlessly executes data table manipulation and statistical generation alongside traditional JSL scripts. It also enhanced peak analysis capabilities and clinical trial data tracking. JMP Architecture Variants