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: 2023-04-03 06:02:06 UTC; Jin
Author: Jin Li [aut, cre]
Maintainer: Jin Li <jinli68@gmail.com>
Repository: CRAN
Date/Publication: 2023-04-04 08:20:02 UTC
Built: R 4.6.0; ; 2026-04-25 13:27:02 UTC; unix
