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
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.1
Repository: https://nanxstats.r-universe.dev
Date/Publication: 2024-05-01 02:31:22 UTC
RemoteUrl: https://github.com/nanxstats/stackgbm
RemoteRef: HEAD
RemoteSha: db33de5656579aff009eb5c186378fabe7b5fb1e
NeedsCompilation: no
Packaged: 2026-06-22 08:06:55 UTC; root
Author: Nan Xiao [aut, cre, cph] (ORCID:
    <https://orcid.org/0000-0002-0250-5673>)
Maintainer: Nan Xiao <me@nanx.me>