Package: stackgbm
Title: Stacked Gradient Boosting Machines
Version: 0.1.0
Authors@R: c(
    person("Nan", "Xiao", email = "me@nanx.me", role = c("aut", "cre", "cph"),
           comment = c(ORCID = "0000-0002-0250-5673")))
Description: A minimalist implementation of model stacking by
    Wolpert (1992) <doi:10.1016/S0893-6080(05)80023-1> for boosted tree models.
    A classic, two-layer stacking model is implemented, where the first layer
    generates features using gradient boosting trees, and the second layer
    employs a logistic regression model that uses these features as inputs.
    Utilities for training the base models and parameters tuning are provided,
    allowing users to experiment with different ensemble configurations easily.
    It aims to provide a simple and efficient way to combine multiple
    gradient boosting models to improve predictive model performance
    and robustness.
License: MIT + file LICENSE
URL: https://nanx.me/stackgbm/, https://github.com/nanxstats/stackgbm
BugReports: https://github.com/nanxstats/stackgbm/issues
Encoding: UTF-8
VignetteBuilder: knitr
Depends: R (>= 3.5.0)
Imports: pROC, progress, rlang
Suggests: knitr, lightgbm, msaenet, rmarkdown, xgboost
RoxygenNote: 7.3.1
NeedsCompilation: no
Packaged: 2024-04-29 04:16:05 UTC; nanx
Author: Nan Xiao [aut, cre, cph] (<https://orcid.org/0000-0002-0250-5673>)
Maintainer: Nan Xiao <me@nanx.me>
Repository: CRAN
Date/Publication: 2024-04-30 11:20:02 UTC
Built: R 4.6.0; ; 2026-04-28 21:42:42 UTC; unix
