Full-rank Partial Least squares and Partial Least squares regression
Multivariate data analysis - modeling
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Full- and partial-rank Partial Least Squares Regression.
The functions accept X and Y data either as matrices or tables. X and Y are converted to matrix and processed. The optimal number of PLS components is found by Leave-one-out cross-validation but it can be modified by the user. Outputs are organized in user-defined structure variable and a set of figures is generated. MATLAB workspace with three example data is given.
Cite As
Filippo Amato (2026). Full-rank Partial Least squares and Partial Least squares regression (https://github.com/fulepo/PLS), GitHub. Retrieved .
General Information
- Version 1.0.0.0 (5.41 MB)
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View License on GitHub
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
Versions that use the GitHub default branch cannot be downloaded
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.0.0.0 | Screenshot |
To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.
