Package: veesa
Type: Package
Title: Pipeline for Explainable Machine Learning with Functional Data
Version: 0.1.7
Authors@R: c(
  person("Katherine", "Goode", email = "kjgoode@sandia.gov", role = c("cre", "aut")),
  person("J. Derek", "Tucker", email = "jftuck@sandia.gov", role = c("aut")),
  person("Sandia National Laboratories", role = c("cph", "fnd")))
Description: 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.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2
Depends: R (>= 4.1.0)
Imports: dplyr, fdasrvf, forcats, ggplot2, purrr, stats, stringr, tidyr
Suggests: randomForest, testthat (>= 3.0.0)
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2026-07-02 06:34:12 UTC; root
Author: Katherine Goode [cre, aut], J. Derek Tucker [aut], Sandia
        National Laboratories [cph, fnd]
Maintainer: Katherine Goode <kjgoode@sandia.gov>
Config/pak/sysreqs: cmake make libicu-dev libuv1-dev libssl-dev
Repository: https://goodekat.r-universe.dev
Date/Publication: 2025-12-04 06:50:50 UTC
RemoteUrl: https://github.com/cran/veesa
RemoteRef: HEAD
RemoteSha: 1d99ae72ff205af0d3fcd0e4d5d931439016e593