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cff-version: 1.2.0
message: 'To cite package "veesa" in publications use:'
type: software
license: MIT
title: 'veesa: Pipeline for Explainable Machine Learning with Functional Data'
version: 0.1.6
doi: 10.32614/CRAN.package.veesa
abstract: 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) <https://doi.org/10.48550/arXiv.2501.07602>
  for technical details about the methodology.
authors:
- family-names: Goode
  given-names: Katherine
  email: kjgoode@sandia.gov
- family-names: Tucker
  given-names: J. Derek
  email: jftuck@sandia.gov
repository: https://goodekat.r-universe.dev
commit: bd51335b9128ec050d7386577df9cefcdf265e84
date-released: '2025-01-17'
contact:
- family-names: Goode
  given-names: Katherine
  email: kjgoode@sandia.gov