Package: boostmtree 2.0.7
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 designed 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>. This is a Cleveland Clinic Foundation patched build correcting terminal-node coefficient divergence when fitting with cross-validation enabled.
Authors:
boostmtree_2.0.7.tar.gz
boostmtree_2.0.7.zip(r-4.7-any)boostmtree_2.0.7.zip(r-4.6-any)boostmtree_2.0.7.zip(r-4.5-any)
boostmtree_2.0.7.tgz(r-4.6-any)boostmtree_2.0.7.tgz(r-4.5-any)
boostmtree_2.0.7.tar.gz(r-4.7-any)boostmtree_2.0.7.tar.gz(r-4.6-any)
boostmtree_2.0.7.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
boostmtree/json (API)
| # Install 'boostmtree' in R: |
| install.packages('boostmtree', repos = c('https://ehrlinger.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/ehrlinger/boostmtree_src/issues
- AF - Atrial Fibrillation Data
- spirometry - Spirometry Data
Last updated from:b90180e4aa (on v2.0.7-ccf). Checks:9 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 206 | ||
| source / vignettes | OK | 360 | ||
| linux-release | OK | 216 | ||
| macos-release | OK | 171 | ||
| macos-oldrel | OK | 190 | ||
| windows-devel | OK | 165 | ||
| windows-release | OK | 195 | ||
| windows-oldrel | OK | 170 | ||
| wasm-release | OK | 131 |
Exports:boostmtreeboostmtree.controlboostmtree.newsmarginal.plotpartial.plotplot.boostmtreeplot.vimp.boostmtreepredict.boostmtreeprint.boostmtreesimLongvimp.boostmtree
Dependencies:base64encbitbit64bslibcachemclicliprcpp11crayondata.treeDiagrammeRdigestdplyrevaluatefarverfastmapfontawesomefsgenericsgluehighrhmshtmltoolshtmlwidgetsigraphjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMatrixmemoisemimenlmepillarpkgconfigprettyunitsprogresspurrrR6randomForestSRCrappdirsRColorBrewerreadrRhpcBLASctlrlangrmarkdownrstudioapisassscalesstringistringrtibbletidyrtidyselecttinytextzdbutf8vctrsviridisLitevisNetworkvroomwithrxfunyaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Boosted multivariate trees for longitudinal data. | boostmtree-package |
| Atrial Fibrillation Data | AF |
| Boosted multivariate trees for longitudinal data | boostmtree |
| Create a control object for 'boostmtree' | boostmtree.control |
| Show the NEWS file | boostmtree.news |
| Marginal fitted-response summaries for boostmtree models | marginal.plot marginal.plot.boostmtree plot.marginal.plot.boostmtree |
| Partial-dependence summaries for fitted boostmtree models | partial.plot partial.plot.boostmtree plot.partial.plot.boostmtree |
| Plot fitted trajectories and diagnostic summaries for a boostmtree object | plot.boostmtree |
| Plot Method for Variable Importance Objects | plot.vimp.boostmtree |
| Predict longitudinal trajectories from a fitted boostmtree model | predict.boostmtree |
| Print a summary of a boostmtree fit or prediction object | print.boostmtree |
| Simulate longitudinal data for boostmtree examples | simLong |
| Spirometry Data | spirometry |
| Permutation Variable Importance for Boosted Tree Models | vimp.boostmtree |