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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

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.