Boosted Multivariate Trees for Longitudinal Data
Hemant Ishwaran <hemant.ishwaran@gmail.com>, Amol Pande <amoljpande@gmail.com>
randomForestSRC (>= 2.1.0), parallel, splines, nlme
Implements Friedman's gradient descent boosting algorithm for longitudinal data using multivariate tree base learners.  A time-covariate interaction effect is modeled using penalized B-splines (P-splines) with estimated adaptive smoothing parameter.

randomForestSRC (>= 2.5.0), parallel, splines, nlme

randomForestSRC (>= 2.9.0), parallel, splines, nlme
Implements Friedman's gradient descent boosting algorithm for modeling of continuous or binary longitudinal response using multivariate tree base learners.  A time-covariate interaction effect is modeled using penalized B-splines (P-splines) with estimated adaptive smoothing parameter.

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>.

A modular framework to bag or boost any estimation procedure.
boostr provides a modular framework that return the focus of
ensemble learning back to 'learning' (instead of programming).
knitr, xtable, mlbench, nnet, class, e1071, randomForest

S original <http://dmawww.epfl.ch/davison.mosaic/BMA/library.html>
by Angelo Canty.  R port by  Brian Ripley <ripley@stats.ox.ac.uk>.
functions and datasets for bootstrapping from the
book "Bootstrap Methods and Their Applications" by A.C. Davison and
D.V. Hinkley (1997, CUP).

R (>= 0.99), survival5 for some examples

R (>= 1.0.0), survival5 for some examples

S original <http://statwww.epfl.ch/davison/BMA/library.html>
by Angelo Canty <cantya@mcmaster.ca>.
R port by  Brian Ripley <ripley@stats.ox.ac.uk>.