steprf
Stepwise Predictive Variable Selection for Random Forest
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.
- Version1.0.2
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Last release06/29/2022
Team
Jin Li
Insights
Last 30 days
This package has been downloaded 139 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 2 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 1,897 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Jul 21, 2024 with 71 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Imports4 packages
- Suggests4 packages
- Reverse Imports1 package