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: 2026-07-04 15:41:04 UTC; root
Config/pak/sysreqs: cmake libglpk-dev make libicu-dev libuv1-dev
        libxml2-dev libx11-dev
Repository: https://kogalur.r-universe.dev
Date/Publication: 2026-04-10 09:42:09 UTC
RemoteUrl: https://github.com/cran/boostmtree
RemoteRef: HEAD
RemoteSha: 5d019c4193c44f9ac6c4bc921ab05e4d713f89ad