Package: boostmtree 1.5.1

Udaya B. Kogalur

boostmtree: Boosted Multivariate Trees for Longitudinal Data

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>.

Authors:Hemant Ishwaran <[email protected]>, Amol Pande <[email protected]>

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NEWS

# Install 'boostmtree' in R:
install.packages('boostmtree', repos = c('https://cranhaven.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/cranhaven/cranhaven.r-universe.dev/issues

Datasets:

On CRAN:

archivedpackagesr-universe

1.70 score 5 stars 9 scripts 319 downloads 10 exports 69 dependencies

Last updated 16 hours agofrom:67d41fad8b (on package/boostmtree). Checks:7 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKJan 19 2025
R-4.5-winOKJan 19 2025
R-4.5-linuxOKJan 19 2025
R-4.4-winOKJan 19 2025
R-4.4-macOKJan 19 2025
R-4.3-winOKJan 19 2025
R-4.3-macOKJan 19 2025

Exports:boostmtreeboostmtree.newsmarginalPlotpartialPlotplot.boostmtreepredict.boostmtreeprint.boostmtreesimLongvimp.boostmtreevimpPlot

Dependencies:base64encbitbit64bslibcachemclicliprcolorspacecpp11crayondata.treeDiagrammeRdigestdplyrevaluatefansifarverfastmapfontawesomefsgenericsgluehighrhmshtmltoolshtmlwidgetsigraphjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMatrixmemoisemimemunsellnlmepillarpkgconfigprettyunitsprogresspurrrR6randomForestSRCrappdirsRColorBrewerreadrrlangrmarkdownrstudioapisassscalesstringistringrtibbletidyrtidyselecttinytextzdbutf8vctrsviridisLitevisNetworkvroomwithrxfunyaml