multiview: Cooperative Learning for Multi-View Analysis

Cooperative learning combines the usual squared error loss of predictions with an agreement penalty to encourage the predictions from different data views to agree. By varying the weight of the agreement penalty, we get a continuum of solutions that include the well-known early and late fusion approaches. Cooperative learning chooses the degree of agreement (or fusion) in an adaptive manner, using a validation set or cross-validation to estimate test set prediction error. In the setting of cooperative regularized linear regression, the method combines the lasso penalty with the agreement penalty (Ding, D., Li, S., Narasimhan, B., Tibshirani, R. (2021) <doi:10.1073/pnas.2202113119>).

Version: 0.8
Depends: R (≥ 3.5.0)
Imports: glmnet, Matrix, methods, RColorBrewer, Rcpp, stats, survival, utils
LinkingTo: Rcpp, RcppEigen
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0), xfun
Published: 2023-03-31
Author: Daisy Yi Ding [aut], Robert J. Tibshirani [aut], Balasubramanian Narasimhan [aut, cre], Trevor Hastie [aut], Kenneth Tay [aut], James Yang [aut]
Maintainer: Balasubramanian Narasimhan <naras at stanford.edu>
License: GPL-2
NeedsCompilation: yes
SystemRequirements: C++17
Materials: README NEWS
CRAN checks: multiview results

Documentation:

Reference manual: multiview.pdf
Vignettes: An Introduction to multiview

Downloads:

Package source: multiview_0.8.tar.gz
Windows binaries: r-devel: multiview_0.8.zip, r-release: multiview_0.8.zip, r-oldrel: multiview_0.8.zip
macOS binaries: r-release (arm64): multiview_0.8.tgz, r-oldrel (arm64): multiview_0.8.tgz, r-release (x86_64): multiview_0.8.tgz
Old sources: multiview archive

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