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: 1.5.0
Depends: R (≥ 3.5.0)
Imports: randomForestSRC (≥ 2.9.0), parallel, splines, nlme
Published: 2020-11-24
Author: Hemant Ishwaran, Amol Pande
Maintainer: Udaya B. Kogalur <ubk at kogalur.com>
License: GPL (≥ 3)
URL: http://web.ccs.miami.edu/~hishwaran/
NeedsCompilation: no
Citation: boostmtree citation info
Materials: NEWS
CRAN checks: boostmtree results

Downloads:

Reference manual: boostmtree.pdf
Package source: boostmtree_1.5.0.tar.gz
Windows binaries: r-devel: boostmtree_1.5.0.zip, r-release: boostmtree_1.5.0.zip, r-oldrel: boostmtree_1.5.0.zip
macOS binaries: r-release: boostmtree_1.5.0.tgz, r-oldrel: boostmtree_1.5.0.tgz
Old sources: boostmtree archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=boostmtree to link to this page.