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RGS  

Recursive Gradient Scanning Algorithm
View on CRAN: Click here


Download and install RGS package within the R console
Install from CRAN:
install.packages("RGS")

Install from Github:
library("remotes")
install_github("cran/RGS")

Install by package version:
library("remotes")
install_version("RGS", "1.0")



Attach the package and use:
library("RGS")
Maintained by
Shuo Yang
[Scholar Profile | Author Map]
First Published: 2024-12-19
Latest Update: 2024-12-19
Description:
Provides a recursive gradient scanning algorithm for discretizing continuous variables in Logistic and Cox regression models. This algorithm is especially effective in identifying optimal cut-points for variables with U-shaped relationships to 'lnOR' (the natural logarithm of the odds ratio) or 'lnHR' (the natural logarithm of the hazard ratio), thereby enhancing model fit, interpretability, and predictive power. By iteratively scanning and calculating gradient changes, the method accurately pinpoints critical cut-points within nonlinear relationships, transforming continuous variables into categorical ones. This approach improves risk classification and regression analysis performance, increasing interpretability and practical relevance in clinical and risk management settings.
How to cite:
Shuo Yang (2024). RGS: Recursive Gradient Scanning Algorithm. R package version 1.0, https://cran.r-project.org/web/packages/RGS. Accessed 02 May. 2025.
Previous versions and publish date:
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Complete documentation for RGS
Functions, R codes and Examples using the RGS R package
Full RGS package functions and examples
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