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SEMMA mainly focuses on the modeling tasks of data mining projects, leaving the business aspects out (unlike, e.g., CRISP-DM and its Business Understanding phase). Additionally, SEMMA is designed to help the users of the SAS Enterprise Miner software. Therefore, applying it outside Enterprise Miner may be ambiguous. [3]
JMP Pro is intended for data scientists, and has an emphasis on advanced predictive modelling and model selection. [41] JMP Genomics, used for analyzing and visualizing genomics data, [49] requires a SAS component to operate and can access SAS/Genetics and SAS/STAT procedures or invoke SAS macros. [48]
SAS macros are pieces of code or variables that are coded once and referenced to perform repetitive tasks. [8] SAS data can be published in HTML, PDF, Excel, RTF and other formats using the Output Delivery System, which was first introduced in 2007. [9] SAS Enterprise Guide is SAS's point-and-click interface.
Group method of data handling (GMDH) is a family of inductive algorithms for computer-based mathematical modeling of multi-parametric datasets that features fully automatic structural and parametric optimization of models.
The SAS macro language is made available within base SAS software to reduce the amount of code, and create code generators for building more versatile and flexible programs. [21] The macro language can be used for functionalities as simple as symbolic substitution and as complex as dynamic programming . [ 8 ]
"A Short Preview of Free Statistical Software Packages for Teaching Statistics to Industrial Technology Majors" (PDF). Journal of Industrial Technology. 21 (2). Archived from the original (PDF) on October 25, 2005.
The general algebraic modeling system (GAMS) is a high-level modeling system for mathematical optimization. GAMS is designed for modeling and solving linear, nonlinear, and mixed-integer optimization problems. The system is tailored for complex, large-scale modeling applications and allows the user to build large maintainable models that can be ...
GAMLSS is especially suited for modelling a leptokurtic or platykurtic and/or positively or negatively skewed response variable. For count type response variable data it deals with over-dispersion by using proper over-dispersed discrete distributions. Heterogeneity also is dealt with by modeling the scale or shape parameters using explanatory ...