Package: grf
Title: Generalized Random Forests
Version: 1.2.0
Authors@R: c(
    person("Julie", "Tibshirani", role = c("aut", "cre"), email = "jtibs@cs.stanford.edu"),
    person("Susan", "Athey", role = "aut"),
    person("Rina", "Friedberg", role = "ctb"),
    person("Vitor", "Hadad", role = "ctb"),
    person("David", "Hirshberg", role = "ctb"),
    person("Luke", "Miner", role = "ctb"),
    person("Erik", "Sverdrup", role = "ctb"),
    person("Stefan", "Wager", role = "aut"),
    person("Marvin", "Wright", role = "ctb")
    )
BugReports: https://github.com/grf-labs/grf/issues
Description: A pluggable package for forest-based statistical estimation and inference.
    GRF currently provides methods for non-parametric least-squares regression,
    quantile regression, survival regression and treatment effect estimation (optionally using instrumental
    variables), with support for missing values.
Depends: R (>= 3.5.0)
License: GPL-3
LinkingTo: Rcpp, RcppEigen
Imports: DiceKriging, lmtest, Matrix, methods, Rcpp (>= 0.12.15),
        sandwich (>= 2.4-0)
RoxygenNote: 7.0.2
Suggests: DiagrammeR, testthat
SystemRequirements: GNU make
URL: https://github.com/grf-labs/grf
NeedsCompilation: yes
Packaged: 2020-05-29 04:02:03 UTC; erik
Author: Julie Tibshirani [aut, cre],
  Susan Athey [aut],
  Rina Friedberg [ctb],
  Vitor Hadad [ctb],
  David Hirshberg [ctb],
  Luke Miner [ctb],
  Erik Sverdrup [ctb],
  Stefan Wager [aut],
  Marvin Wright [ctb]
Maintainer: Julie Tibshirani <jtibs@cs.stanford.edu>
Repository: CRAN
Date/Publication: 2020-06-04 07:40:11 UTC
Built: R 4.1.0; x86_64-w64-mingw32; 2020-08-03 08:42:21 UTC; windows
Archs: i386, x64
