boostmtree_package: Boosted multivariate trees for longitudinal data.

In boostmtree: Boosted Multivariate Trees for Longitudinal Data

Boosted multivariate trees for longitudinal data.

Description

Multivariate boosted tree models for repeated-measures outcomes. The method combines gradient boosting, subject-level tree partitioning, and smooth time effects to estimate subject-specific mean trajectories or class probabilities over time. Supports continuous, binary, nominal, and ordinal longitudinal data, as well as univariate responses.

Package Overview

This package contains many useful functions and users should read the help file in its entirety for details. However, we briefly mention several key functions that may make it easier to navigate and understand the layout of the package.

1. boostmtree

   This is the main entry point to the package. It grows a multivariate tree using user supplied training data. Trees are grown using the randomForestSRC R-package.

2. predict.boostmtree (predict)

   Used for prediction. Predicted values are obtained by dropping the user supplied test data down the grow forest. The resulting object has class (rfsrc, predict).

Author(s)

Amol Pande, Udaya B. Kogalur and Hemant Ishwaran

References

Friedman J.H. (2001). Greedy function approximation: a gradient boosting machine. The Annals of Statistics, 29(5):1189–1232.

Friedman J.H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4):367–378.

Pande A., Li L., Rajeswaran J., Ehrlinger J., Kogalur U.B., Blackstone E.H., Ishwaran H. (2017). Boosted multivariate trees for longitudinal data. Machine Learning, 106(2):277–305.

Pande A., Ishwaran H., Blackstone E.H., Rajeswaran J., and Gillanov M. (2022). Application of gradient boosting in evaluating surgical ablation for atrial fibrillation. SN Computer Science, 3:466.

Pande A., Ishwaran H., and Blackstone E.H. (2022). Boosting for multivariate longitudinal responses. SN Computer Science, 3:186.

Pande A. (2017). Boosting for longitudinal data. Ph.D. dissertation, Miller School of Medicine, University of Miami.

See Also

boostmtree, boostmtree.control, plot.boostmtree, predict.boostmtree, print.boostmtree, simLong, vimp.boostmtree,

boostmtree documentation built on April 10, 2026, 9:10 a.m.