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SignifReg: Consistent Significance Controlled Variable Selection in Generalized Linear Regression
Provides significance controlled variable selection algorithms with different directions (forward, backward, stepwise) based on diverse criteria (AIC, BIC, adjusted r-square, PRESS, or p-value). The algorithm selects a final model with only significant variables defined as those with significant p-values after multiple testing correction such as Bonferroni, False Discovery Rate, etc. See Zambom and Kim (2018) <doi:10.1002/sta4.210>.
Getting started
Browse package contents
Package details |
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| Author | Jongwook Kim, Adriano Zanin Zambom |
| Maintainer | Adriano Zanin Zambom <adriano.zambom@csun.edu> |
| License | GPL (>= 2) |
| Version | 4.3 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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