Stepwise Variable Selection for Generalized Boosted Regression Modeling

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) . Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F., Nichol, S. (2017). .


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install.packages("stepgbm")

1.0.0 by Jin Li, a month ago


Browse source code at https://github.com/cran/stepgbm


Authors: Jin Li [aut, cre]


Documentation:   PDF Manual  


GPL (>= 2) license


Imports spm, gbm, steprf

Suggests knitr, rmarkdown, reshape2, lattice


See at CRAN