Package: joinet 1.0.0

joinet: Penalised Multivariate Regression ('Multi-Target Learning')

Implements penalised multivariate regression (i.e., for multiple outcomes and many features) by stacked generalisation (<doi:10.1093/bioinformatics/btab576>). For positively correlated outcomes, a single multivariate regression is typically more predictive than multiple univariate regressions. Includes functions for model fitting, extracting coefficients, outcome prediction, and performance measurement. For optional comparisons, install 'remMap' from GitHub (<https://github.com/cran/remMap>).

Authors:Armin Rauschenberger [aut, cre]

joinet_1.0.0.tar.gz
joinet_1.0.0.zip(r-4.7-any)joinet_1.0.0.zip(r-4.6-any)joinet_1.0.0.zip(r-4.5-any)
joinet_1.0.0.tgz(r-4.6-any)joinet_1.0.0.tgz(r-4.5-any)
joinet_1.0.0.tar.gz(r-4.7-any)joinet_1.0.0.tar.gz(r-4.6-any)
joinet_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
joinet/json (API)

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

Bug tracker:https://github.com/rauschenberger/joinet/issues

Pkgdown/docs site:https://rauschenberger.github.io

On CRAN:

Conda:

5.30 score 4 stars 1 packages 11 scripts 359 downloads 2 exports 12 dependencies

Last updated from:d5f0b58128. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK137
source / vignettesOK208
linux-releaseOK135
macos-releaseOK225
macos-oldrelOK144
windows-develOK86
windows-releaseOK91
windows-oldrelOK94
wasm-releaseOK103

Exports:cv.joinetjoinet

Dependencies:codetoolscornetforeachglmnetiteratorslatticeMatrixpalassoRcppRcppEigenshapesurvival

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