# --------------------------------------------
# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# --------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "joinet" in publications use:'
type: software
license: GPL-3.0-only
title: 'joinet: Multivariate Elastic Net Regression'
version: 0.0.10
identifiers:
- type: doi
  value: 10.32614/CRAN.package.joinet
abstract: Implements high-dimensional multivariate regression by stacked generalisation
  (Rauschenberger 2021 <https://doi.org/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. If required, install
  MRCE or remMap from GitHub (<https://github.com/cran/MRCE>, <https://github.com/cran/remMap>).
authors:
- family-names: Rauschenberger
  given-names: Armin
  email: armin.rauschenberger@uni.lu
preferred-citation:
  type: article
  title: Predicting correlated outcomes from molecular data
  authors:
  - family-names: Rauschenberger
    given-names: Armin
    email: armin.rauschenberger@uni.lu
  - family-names: Glaab
    given-names: Enrico
  journal: Bioinformatics
  volume: In press
  year: '2021'
  url: https://doi.org/10.1093/bioinformatics/btab576
repository: https://rauschenberger.r-universe.dev
repository-code: https://github.com/rauschenberger/joinet
commit: c5a9b5f993ed64fd318ef1e256b4bc12dd670ec4
url: https://github.com/rauschenberger/joinet
contact:
- family-names: Rauschenberger
  given-names: Armin
  email: armin.rauschenberger@uni.lu