Package: xgboost
Type: Package
Title: Extreme Gradient Boosting
Version: 1.0.0.2
Date: 2020-03-25
Authors@R: c(
  person("Tianqi", "Chen", role = c("aut"),
         email = "tianqi.tchen@gmail.com"),
  person("Tong", "He", role = c("aut", "cre"),
         email = "hetong007@gmail.com"),
  person("Michael", "Benesty", role = c("aut"),
         email = "michael@benesty.fr"),
  person("Vadim", "Khotilovich", role = c("aut"),
         email = "khotilovich@gmail.com"),
  person("Yuan", "Tang", role = c("aut"),
         email = "terrytangyuan@gmail.com",
         comment = c(ORCID = "0000-0001-5243-233X")),
  person("Hyunsu", "Cho", role = c("aut"),
         email = "chohyu01@cs.washington.edu"),
  person("Kailong", "Chen", role = c("aut")),
  person("Rory", "Mitchell", role = c("aut")),
  person("Ignacio", "Cano", role = c("aut")),
  person("Tianyi", "Zhou", role = c("aut")),
  person("Mu", "Li", role = c("aut")),
  person("Junyuan", "Xie", role = c("aut")),
  person("Min", "Lin", role = c("aut")),
  person("Yifeng", "Geng", role = c("aut")),
  person("Yutian", "Li", role = c("aut")),
  person("XGBoost contributors", role = c("cph"),
         comment = "base XGBoost implementation")
  )
Description: Extreme Gradient Boosting, which is an efficient implementation
    of the gradient boosting framework from Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>.
    This package is its R interface. The package includes efficient linear 
    model solver and tree learning algorithms. The package can automatically 
    do parallel computation on a single machine which could be more than 10 
    times faster than existing gradient boosting packages. It supports
    various objective functions, including regression, classification and ranking.
    The package is made to be extensible, so that users are also allowed to define
    their own objectives easily.
License: Apache License (== 2.0) | file LICENSE
URL: https://github.com/dmlc/xgboost
BugReports: https://github.com/dmlc/xgboost/issues
NeedsCompilation: yes
VignetteBuilder: knitr
Suggests: knitr, rmarkdown, ggplot2 (>= 1.0.1), DiagrammeR (>= 0.9.0),
        Ckmeans.1d.dp (>= 3.3.1), vcd (>= 1.3), testthat, lintr, igraph
        (>= 1.0.1), jsonlite, float
Depends: R (>= 3.3.0)
Imports: Matrix (>= 1.1-0), methods, data.table (>= 1.9.6), magrittr
        (>= 1.5), stringi (>= 0.5.2)
RoxygenNote: 7.0.2
SystemRequirements: GNU make, C++11
Packaged: 2020-03-25 01:19:19 UTC; ubuntu
Author: Tianqi Chen [aut],
  Tong He [aut, cre],
  Michael Benesty [aut],
  Vadim Khotilovich [aut],
  Yuan Tang [aut] (<https://orcid.org/0000-0001-5243-233X>),
  Hyunsu Cho [aut],
  Kailong Chen [aut],
  Rory Mitchell [aut],
  Ignacio Cano [aut],
  Tianyi Zhou [aut],
  Mu Li [aut],
  Junyuan Xie [aut],
  Min Lin [aut],
  Yifeng Geng [aut],
  Yutian Li [aut],
  XGBoost contributors [cph] (base XGBoost implementation)
Maintainer: Tong He <hetong007@gmail.com>
Repository: CRAN
Date/Publication: 2020-03-25 14:10:02 UTC
Built: R 3.5.3; x86_64-w64-mingw32; 2020-04-22 05:59:46 UTC; windows
Archs: i386, x64
