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: 2022-06-28 06:37:55 UTC; Jin
Author: Jin Li [aut, cre]
Maintainer: Jin Li <jinli68@gmail.com>
Repository: CRAN
Date/Publication: 2022-06-29 11:20:02 UTC
Built: R 4.6.0; ; 2026-04-29 08:25:24 UTC; unix
