Title: Boosted Multivariate Trees for Longitudinal Data
Author: Hemant Ishwaran <hemant.ishwaran@gmail.com>, Amol Pande <amoljpande@gmail.com>
Maintainer: Udaya B. Kogalur <ubk@kogalur.com>
Imports: randomForestSRC (>= 2.1.0), parallel, splines, nlme
Description: 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.

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

Imports: randomForestSRC (>= 2.9.0), parallel, splines, nlme
Description: 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.

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>.

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

by Angelo Canty.  R port by  Brian Ripley <ripley@stats.ox.ac.uk>.
Description: functions and datasets for bootstrapping from the
book "Bootstrap Methods and Their Applications" by A.C. Davison and

Title: Bootstrap R (S-Plus) Functions (Canty)

Depends: R (>= 0.99), survival5 for some examples