Package: boostmtree 1.5.1

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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10 exports 0.00 score 69 dependencies 8 scripts 431 downloads

Last updated 3 years agofrom:b402ee8976. Checks:OK: 7. Indexed: yes.

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

Dependencies:base64encbitbit64bslibcachemclicliprcolorspacecpp11crayondata.treeDiagrammeRdigestdplyrevaluatefansifarverfastmapfontawesomefsgenericsgluehighrhmshtmltoolshtmlwidgetsigraphjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMatrixmemoisemimemunsellnlmepillarpkgconfigprettyunitsprogresspurrrR6randomForestSRCrappdirsRColorBrewerreadrrlangrmarkdownrstudioapisassscalesstringistringrtibbletidyrtidyselecttinytextzdbutf8vctrsviridisLitevisNetworkvroomwithrxfunyaml