Package: stepgbm
Title: Stepwise Variable Selection for Generalized Boosted Regression
        Modeling
Version: 1.0.1
Date: 2023-04-03
Authors@R: person("Jin", "Li", email = "jinli68@gmail.com", role = c("aut", "cre"))
Description: An introduction to a couple of novel predictive variable
        selection methods for generalised boosted regression modeling
        (gbm). They are based on various variable influence methods
        (i.e., relative variable influence (RVI) and knowledge informed
        RVI (i.e., KIRVI, and KIRVI2)) that adopted similar ideas as
        AVI, KIAVI and KIAVI2 in the 'steprf' package, and also based
        on predictive accuracy in stepwise algorithms. For details of
        the variable selection methods, please see: Li, J., Siwabessy,
        J., Huang, Z. and Nichol, S. (2019)
        <doi:10.3390/geosciences9040180>. Li, J., Alvarez, B.,
        Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L.,
        Howard, F., Nichol, S. (2017). <DOI:
        10.13140/RG.2.2.27686.22085>.
Depends: R (>= 4.0)
Imports: spm, steprf
License: GPL (>= 2)
RoxygenNote: 7.1.1
Encoding: UTF-8
Suggests: knitr, rmarkdown, reshape2, lattice
NeedsCompilation: no
Packaged: 2026-07-04 08:41:36 UTC; root
Author: Jin Li [aut, cre]
Maintainer: Jin Li <jinli68@gmail.com>
Config/pak/sysreqs: libabsl-dev cmake libgdal-dev gdal-bin libgeos-dev
        libssl-dev libproj-dev libsqlite3-dev libudunits2-dev
Repository: https://jinli22.r-universe.dev
Date/Publication: 2023-04-04 07:20:02 UTC
RemoteUrl: https://github.com/cran/stepgbm
RemoteRef: HEAD
RemoteSha: 916b4a0e041dbdc9a8a18b4b900a9842f956707c