JMP will suspend normal business operations for our Winter Holiday beginning on Wednesday, Dec. 24, 2025, at 5:00 p.m. ET (2:00 p.m. ET for JMP Accounts Receivable).
Regular business hours will resume at 9:00 a.m. EST on Friday, Jan. 2, 2026.
We’re retiring the File Exchange at the end of this year. The JMP Marketplace is now your destination for add-ins and extensions.
When generating features of a signal over time, some statistics—such as mean, median, and SD—can be easily calculated using New Formula Columns, grouped by a time bin column. However, important signal features such as kurtosis, skewness, and variance are missing from the available list.
The new scoping framework provides on-the-fly grouped statistics for GraphBuilder - based on the power of Col ...() aggregations.
Even better: For Grouped Statistics, JMP19 has a another great improvement: JMP19 comes with a Fast Way to calculate "nested" Col ... aggregations - like MAD . So, if you know the function behind the aggregation, you can set it up manually by combining existing Col... ( ) aggregations. And even if nesting is required - like it is for MAD, Skewness, Kurtosis , it will be orders of magnitude faster than before : )
Amidst all this excitement, I have to agree. This approach is just for experts who are willing to set up the aggregation manually. However, for the standard JMP user who wants to calculate Col Skewness() — just as they would with Col Mean() — it doesn't help. There's still a lot to do to synchronize the different statistics platforms in JMP!