veesa: Pipeline for Explainable Machine Learning with Functional Data
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 |
Documentation:
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