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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 |
| 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 |
| Reference manual: | boostmtree.pdf |
| 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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