boostmtree: Boosted Multivariate Trees for Longitudinal Data

Implements Friedman's gradient descent boosting algorithm for modeling longitudinal response using multivariate tree base learners. Longitudinal response could be continuous, binary, nominal or ordinal. A time-covariate interaction effect is modeled using penalized B-splines (P-splines) with estimated adaptive smoothing parameter. Although the package is design for longitudinal data, it can handle cross-sectional data as well. Implementation details are provided in Pande et al. (2017), Mach Learn <doi:10.1007/s10994-016-5597-1>.

Version: 1.5.1
Depends: R (≥ 3.5.0)
Imports: randomForestSRC (≥ 2.9.0), parallel, splines, nlme
Published: 2022-03-10
DOI: 10.32614/CRAN.package.boostmtree
Author: Hemant Ishwaran, Amol Pande
Maintainer: Udaya B. Kogalur <ubk at>
License: GPL (≥ 3)
NeedsCompilation: no
Citation: boostmtree citation info
Materials: NEWS
CRAN checks: boostmtree results


Reference manual: boostmtree.pdf


Package source: boostmtree_1.5.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): boostmtree_1.5.1.tgz, r-oldrel (arm64): boostmtree_1.5.1.tgz, r-release (x86_64): boostmtree_1.5.1.tgz, r-oldrel (x86_64): boostmtree_1.5.1.tgz
Old sources: boostmtree archive


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