# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- 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) 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