Package: steprf
Title: Stepwise Predictive Variable Selection for Random Forest
Version: 1.0.2
Date: 2022-6-28
Authors@R: person("Jin", "Li", email = "jinli68@gmail.com", role = c("aut", "cre"))
Description: An introduction to several novel predictive variable
        selection methods for random forest. They are based on various
        variable importance methods (i.e., averaged variable importance
        (AVI), and knowledge informed AVI (i.e., KIAVI, and KIAVI2))
        and 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, randomForest, spm2, psy
License: GPL (>= 2)
RoxygenNote: 7.1.1
Encoding: UTF-8
Suggests: knitr, rmarkdown, lattice, reshape2
NeedsCompilation: no
Packaged: 2026-07-03 07:31:38 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: 2022-06-29 10:20:02 UTC
RemoteUrl: https://github.com/cran/steprf
RemoteRef: HEAD
RemoteSha: dbe93cced33a0c0304c7e3a8494ecba6e5de57ae