Package: stacking 0.2.1

stacking: Building Predictive Models with Stacking

Building predictive models with stacking which is a type of ensemble learning. Learners can be specified from those implemented in 'caret'. For more information of the package, see Nukui and Onogi (2023) <doi:10.1101/2023.06.06.543970>.

Authors:Taichi Nukui [aut, cph], Tomohiro Ishibashi [aut, cph], Akio Onogi [aut, cre, cph]

stacking_0.2.1.tar.gz
stacking_0.2.1.zip(r-4.7)stacking_0.2.1.zip(r-4.6)stacking_0.2.1.zip(r-4.5)
stacking_0.2.1.tgz(r-4.6-any)stacking_0.2.1.tgz(r-4.5-any)
stacking_0.2.1.tar.gz(r-4.7-any)stacking_0.2.1.tar.gz(r-4.6-any)
stacking_0.2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
stacking/json (API)

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

Bug tracker:https://github.com/onogi/stacking/issues

On CRAN:

Conda:

3.30 score 4 stars 4 scripts 198 downloads 5 exports 73 dependencies

Last updated from:f4a83161e0. Checks:7 ERROR, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64ERROR168
source / vignettesOK170
linux-release-x86_64ERROR165
macos-release-arm64ERROR161
macos-oldrel-arm64ERROR192
windows-develERROR123
windows-releaseERROR116
windows-oldrelERROR117
wasm-releaseOK112

Exports:stacking_predictstacking_traintrain_basemodeltrain_basemodel_coretrain_metamodel

Dependencies:caretclasscliclockcodetoolscpp11data.tablediagramdigestdplyre1071farverforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixModelMetricsnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcpprecipesreshape2rlangrpartS7scalesshapesparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr