Highlights
HyperStudy 2017 empowers users to explore their design problems more easily. Input variable constraints and a new D-Optimal DOE can streamline the study process, while the Ordination tab and enhanced Fit diagnostics can make learning from the data easier than ever before.
For each approach specification step, the presentation of the methods has changed. A subset of all available methods is shown, but all methods are available from an expanded list. This change makes the interface consistent with HyperStudy’s suggested best practices.
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Define constraints that are functions of only the input variables. These special constraints can be enforced without any solver analysis to avoid running simulations that are known to fail. This can save run time and increase efficiency.
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This is a new space filling DOE. The scheme distributes points in a space for use with a Fit. This is an excellent choice for sampling data for use in least squares regression modeling.
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The Ordination tab is a general post-processing tab. The tab contains Principal Component Analysis bi-plots. These plots are used to identify relationships between variables and responses, especially in multi-dimensional problems.
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The Fit diagnostics table is enhanced with spark lines and color coding. The visual features aid in quickly assessing the Fit’s predictive quality.
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| • | Modified Lattice Sequence sampling is available in a Stochastic approach |
| • | String filtering tools added to the vector source and readsim expression builders |
| • | Added a quick start example with discrete and categorical variables |
| • | The HyperStudy editor supports drag and drop files |
| • | New Fit approach report type: HyperStudy Fit (*pyfit) |
| • | Improved support for 4k resolution including new visual icons |
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| • | Workbench connection upgraded to work with version 17.1 |
| • | Resolved an issue with ARSM and “Ignore failed analysis” |
| • | Miscellaneous font and graphical improvements |
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| • | The DSS post-processing module is removed. |
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