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 .


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("boostmtree")

2.0.0 by Udaya B. Kogalur, 6 months ago


https://ishwaran.org/


Browse source code at https://github.com/cran/boostmtree


Authors: Hemant Ishwaran [aut] , Amol Pande [aut] , Udaya B. Kogalur [aut, cre]


Documentation:   PDF Manual  


GPL (>= 3) license


Imports randomForestSRC, parallel, splines, nlme


See at CRAN