The writers had a presentation of the techniques found here: http://www.interfacesymposia.org/I04/I2004Proceedings/ChenChao/ChenChao.presentation.pdf

According to the authors, there’s an add-on package to R that implements their original Fortran:

Here are the working links to the R package:

• https://cran.r-project.org/web/packages/randomForest/index.html
• https://CRAN.R-project.org/package=randomForest

This can save you from having to implement this manually.

The writers had a presentation of the techniques found here: http://www.interfacesymposia.org/I04/I2004Proceedings/ChenChao/ChenChao.presentation.pdf

According to the authors, there’s an add-on package to R that implements their original Fortran:

Here are the working links to the R package:

• https://cran.r-project.org/web/packages/randomForest/index.html
• https://CRAN.R-project.org/package=randomForest

Unfortunately if you search the documentation for that package here, there is no mention of "balanced" or "brf." This paper, provides a clue: "we estimate balanced RF models using the sampsize argument from the randomForest package"

This can save you from having to implement this manually.