The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
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 |
| 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 |
Please use the canonical form https://CRAN.R-project.org/package=boostmtree to link to this page.
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.