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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>.
Getting started
Browse package contents
Package details |
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| Author | Hemant Ishwaran <hemant.ishwaran@gmail.com>, Amol Pande <amoljpande@gmail.com> |
| Maintainer | Udaya B. Kogalur <ubk@kogalur.com> |
| License | GPL (>= 3) |
| Version | 1.5.1 |
| URL | https://ishwaran.org/ishwaran.html |
| Package repository | View on CRAN |
| Installation |
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