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Implements the Variable importance Explainable Elastic Shape Analysis pipeline for explainable machine learning with functional data inputs. Converts training and testing data functional inputs to elastic shape analysis principal components that account for vertical and/or horizontal variability. Computes feature importance to identify important principal components and visualizes variability captured by functional principal components. See Goode et al. (2025) <doi:10.48550/arXiv.2501.07602> for technical details about the methodology.
| Version: | 0.1.6 |
| Depends: | R (≥ 3.5.0) |
| Imports: | dplyr, fdasrvf, forcats, ggplot2, purrr, stats, stringr, tidyr |
| Suggests: | randomForest, testthat (≥ 3.0.0) |
| Published: | 2025-01-17 |
| DOI: | 10.32614/CRAN.package.veesa |
| Author: | Katherine Goode [cre, aut], J. Derek Tucker [aut], Sandia National Laboratories [cph, fnd] |
| Maintainer: | Katherine Goode <kjgoode at sandia.gov> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | veesa results |
| Reference manual: | veesa.pdf |
| Package source: | veesa_0.1.6.tar.gz |
| Windows binaries: | r-devel: not available, r-release: not available, r-oldrel: veesa_0.1.6.zip |
| macOS binaries: | r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.