Multi-Target Automated Tree Engine (MuTATE)

Recursively partitions datasets on binary splits across multiple targets having different dependent variable types, including categorical, continuous, count, and survival outcomes. This overcomes single-target limitations of traditional decision trees while retaining model interpretability. See Ayton and Trevino (2023) and Ayton et al. (2025) for details.

MuTATE

Collection of functions to recursively partition datasets on binary splits across multiple targets.

The goal of MuTATE is to create automated, explainable, and comprehensive models across multiple dependent variables of interest.

It provides a collection of functions to recursively partition data on binary splits across multiple targets having different dependent variable types. This overcomes single-target limitations of traditional decision trees, without losing model interpretability, and while handling continuous, categorical, count, and survival outcome variables. This suite of functions also includes a number of parameters for model customization, dependent variable weights, parameter tuning functions, and visualization tools.

Installation

You can install the development version of MuTATE from GitHub with:

# install.packages("devtools")
devtools::install_github("SarahAyton/MuTATE")

Usage

library(MuTATE)
data(mutate_example)

features     <- c("age", "sex", "biomarker")
outcomes     <- c("response", "tumor_size", "ae_count", "OS_definition_time_status")
outcome_defs <- c("Cat", "Cont", "Count", "Surv")

tree <- MTPart(features, outcomes, outcome_defs, mutate_example,
               depth = 2, nodesize = 30)
PlotTree(tree)

See vignette("mutate-getting-started", package = "MuTATE") for a full walkthrough covering fitting, pruning, prediction on new data, and visualization.