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DIPM - Depth Importance in Precision Medicine


The Depth Importance in Precision Medicine (DIPM) method is a classification tree designed for the identification of subgroups relevant to the precision medicine setting. In this setting, a relevant subgroup is a subgroup in which subjects perform either especially well or poorly with a particular treatment assignment. The dimp R package ( implements the DIPM method (Chen and Zhang 2020) using R code that calls a program in C. Overall, the DIPM method is built to analyze clinical datasets with either a continuous or right-censored survival outcome variable and two or more treatment groups. The data also contain a number of candidate split variables supplied by the user. Within the method, the candidate split variable with the largest depth variable importance score is identified as the best variable to split the node. An additional simpler tree method that does not fit a random forest at each node is also included in the package. All of the functions in the package are explained, and illustrative examples are provided to help guide anyone aiming to use the DIPM method with the analysis of datasets of their own.

The package may be installed from CRAN ( or locally using the "dipm_1.1.tar.gz" file after unzipping the compressed file that can be downloaded. Documentation about the package is included in the package under the filename "article-dipm-R-package.pdf". Please see CRAN for further updates.