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 kogalur.com>
License:	GPL (≥ 3)
URL:	https://ishwaran.org/ishwaran.html
NeedsCompilation:	no
Citation:	boostmtree citation info
Materials:	NEWS
CRAN checks:	boostmtree results

Documentation:

Reference manual:

boostmtree.pdf

Downloads:

Package source:	boostmtree_1.5.1.tar.gz
Windows binaries:	r-devel: boostmtree_1.5.1.zip, r-release: boostmtree_1.5.1.zip, r-oldrel: boostmtree_1.5.1.zip
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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