28a466ab51
- Bump configure.ac version. - Remove amalgamation to reduce the build time for a single object with the added benefit that we can use parallel build during development. - Fix c function prototype warning. - Remove Windows automake file generation step to make the build script easier to understand.
71 lines
2.5 KiB
Plaintext
71 lines
2.5 KiB
Plaintext
Package: xgboost
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Type: Package
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Title: Extreme Gradient Boosting
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Version: 1.7.0.1
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Date: 2022-10-18
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Authors@R: c(
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person("Tianqi", "Chen", role = c("aut"),
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email = "tianqi.tchen@gmail.com"),
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person("Tong", "He", role = c("aut"),
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email = "hetong007@gmail.com"),
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person("Michael", "Benesty", role = c("aut"),
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email = "michael@benesty.fr"),
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person("Vadim", "Khotilovich", role = c("aut"),
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email = "khotilovich@gmail.com"),
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person("Yuan", "Tang", role = c("aut"),
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email = "terrytangyuan@gmail.com",
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comment = c(ORCID = "0000-0001-5243-233X")),
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person("Hyunsu", "Cho", role = c("aut"),
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email = "chohyu01@cs.washington.edu"),
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person("Kailong", "Chen", role = c("aut")),
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person("Rory", "Mitchell", role = c("aut")),
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person("Ignacio", "Cano", role = c("aut")),
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person("Tianyi", "Zhou", role = c("aut")),
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person("Mu", "Li", role = c("aut")),
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person("Junyuan", "Xie", role = c("aut")),
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person("Min", "Lin", role = c("aut")),
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person("Yifeng", "Geng", role = c("aut")),
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person("Yutian", "Li", role = c("aut")),
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person("Jiaming", "Yuan", role = c("aut", "cre"),
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email = "jm.yuan@outlook.com"),
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person("XGBoost contributors", role = c("cph"),
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comment = "base XGBoost implementation")
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)
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Maintainer: Jiaming Yuan <jm.yuan@outlook.com>
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Description: Extreme Gradient Boosting, which is an efficient implementation
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of the gradient boosting framework from Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>.
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This package is its R interface. The package includes efficient linear
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model solver and tree learning algorithms. The package can automatically
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do parallel computation on a single machine which could be more than 10
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times faster than existing gradient boosting packages. It supports
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various objective functions, including regression, classification and ranking.
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The package is made to be extensible, so that users are also allowed to define
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their own objectives easily.
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License: Apache License (== 2.0) | file LICENSE
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URL: https://github.com/dmlc/xgboost
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BugReports: https://github.com/dmlc/xgboost/issues
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NeedsCompilation: yes
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VignetteBuilder: knitr
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Suggests:
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knitr,
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rmarkdown,
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ggplot2 (>= 1.0.1),
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DiagrammeR (>= 0.9.0),
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Ckmeans.1d.dp (>= 3.3.1),
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vcd (>= 1.3),
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testthat,
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lintr,
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igraph (>= 1.0.1),
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float,
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crayon,
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titanic
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Depends:
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R (>= 3.3.0)
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Imports:
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Matrix (>= 1.1-0),
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methods,
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data.table (>= 1.9.6),
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jsonlite (>= 1.0),
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RoxygenNote: 7.1.1
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SystemRequirements: GNU make, C++14
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