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boostmtree (version 1.5.1)

Boosted Multivariate Trees for Longitudinal Data

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 .

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Version

Install

install.packages('boostmtree')

Monthly Downloads

347

Version

1.5.1

License

GPL (>= 3)

Maintainer

Last Published

March 10th, 2022

Functions in boostmtree (1.5.1)

plot.boostmtree

Plot Summary Analysis
spirometry

Spirometry Data
vimp.boostmtree

Variable Importance
simLong

Simulate longitudinal data
print.boostmtree

Print Summary Output
vimpPlot

Variable Importance (VIMP) plot
boostmtree-package

Boosted multivariate trees for longitudinal data.
boostmtree.news

Show the NEWS file
marginalPlot

Marginal plot analysis
AF

Atrial Fibrillation Data
boostmtree

Boosted multivariate trees for longitudinal data
predict.boostmtree

Prediction for Boosted multivariate trees for longitudinal data.
partialPlot

Partial plot analysis