Package: boostmtree
Version: 2.0.0
Date: 2026-04-08
Title: Boosted Multivariate Trees for Longitudinal Data
Authors@R: c(person("Hemant", "Ishwaran", email = "hemant.ishwaran@gmail.com", role = "aut"),
    person("Amol", "Pande", email = "amoljpande@gmail.com", role = c("aut")),
    person("Udaya B.", "Kogalur", email = "ubk@kogalur.com", role = c("aut", "cre")))
Author: Hemant Ishwaran [aut],
  Amol Pande [aut],
  Udaya B. Kogalur [aut, cre]
Maintainer: Udaya B. Kogalur <ubk@kogalur.com>
Depends: R (>= 4.3.0)
Imports: randomForestSRC (>= 3.5.0), parallel, splines, nlme
Description: 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>. 
License: GPL (>= 3)
URL: https://ishwaran.org/
NeedsCompilation: no
Packaged: 2026-04-08 13:59:03 UTC; kogalur
Repository: CRAN
Date/Publication: 2026-04-10 08:00:27 UTC
Built: R 4.6.0; ; 2026-04-25 12:47:04 UTC; unix
