DESCRIPTION

Package: shapr
Version: 0.2.3.9200
Title: Prediction Explanation with Dependence-Aware Shapley Values
Description: Complex machine learning models are often hard to interpret. However, in 
  many situations it is crucial to understand and explain why a model made a specific 
  prediction. Shapley values is the only method for such prediction explanation framework 
  with a solid theoretical foundation. Previously known methods for estimating the Shapley 
  values do, however, assume feature independence. This package implements the method 
  described in Aas, Jullum and Løland (2019) <arXiv:1903.10464>, which accounts for any feature 
  dependence, and thereby produces more accurate estimates of the true Shapley values.
  An accompanying Python wrapper (shaprpy) is available on GitHub.
Authors@R: c(
    person("Nikolai", "Sellereite", email = "nikolaisellereite@gmail.com", role = "aut", comment = c(ORCID = "0000-0002-4671-0337")),
    person("Martin", "Jullum", email = "Martin.Jullum@nr.no", role = c("cre", "aut"), comment = c(ORCID = "0000-0003-3908-5155")),
    person("Lars Henry Berge", "Olsen", email = "lholsen@math.uio.no", role = "aut", comment = c(ORCID = "0009-0006-9360-6993")),
    person("Annabelle", "Redelmeier", email = "Annabelle.Redelmeier@nr.no", role = "aut"),
    person("Jon", "Lachmann", email = "Jon@lachmann.nu", role = "aut"),
    person("Anders", "Løland", email = "Anders.Loland@nr.no", role = "ctb"), 
    person("Jens Christian", "Wahl", email = "Jens.Christian.Wahl@nr.no", role = "ctb"), 
    person("Camilla", "Lingjærde", role = "ctb"),
    person("Norsk Regnesentral", role =  c("cph", "fnd"))
    )
URL: https://norskregnesentral.github.io/shapr/, https://github.com/NorskRegnesentral/shapr/
BugReports: https://github.com/NorskRegnesentral/shapr/issues
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
ByteCompile: true
Language: en-US
RoxygenNote: 7.3.1
Depends: R (>= 3.5.0)
Imports: 
    stats,
    data.table,
    Rcpp (>= 0.12.15),
    Matrix,
    future.apply,
    methods
Suggests: 
    ranger,
    xgboost,
    mgcv,
    testthat (>= 3.0.0),
    knitr,
    rmarkdown,
    roxygen2,
    ggplot2,
    gbm,
    party,
    partykit,
    waldo,
    progressr,
    future,
    ggbeeswarm,
    vdiffr,
    forecast,
    torch,
    GGally,
    progress,
    coro,
    parsnip,
    recipes,
    workflows,
    tune,
    dials,
    yardstick,
    hardhat,
    rsample,
    rlang
LinkingTo: 
    RcppArmadillo,
    Rcpp
VignetteBuilder: knitr
Config/testthat/edition: 3
Roxygen: list(markdown = TRUE)