Package: shapr
Version: 0.1.1-1
Title: Explain the Output of Machine Learning Models with more Accurately Estimated 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.
Authors@R: c(
    person("Nikolai", "Sellereite", email = "nikolaisellereite@gmail.com", role = c("cre", "aut"), comment = c(ORCID = "0000-0002-4671-0337")),
    person("Martin", "Jullum", email = "Martin.Jullum@nr.no", role = "aut", comment = c(ORCID = "0000-0003-3908-5155")),  
    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"))
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: 7.1.0
Depends: R (>= 3.5.0)
Imports: 
    stats,
    data.table,
    Rcpp (>= 0.12.15),
    condMVNorm,
    mvnfast,
    Matrix
Suggests: 
    ranger,
    xgboost,
    mgcv,
    testthat, 
    lintr (>= 2.0.0),
    covr,
    maptree,
    corrplot,
    pcaPP,
    knitr,
    rmarkdown,
    pkgdown,
    roxygen2,
    MASS,
    ggplot2,
    caret,
    gbm
LinkingTo: 
    RcppArmadillo,
    Rcpp
VignetteBuilder: knitr
