person("Michael", "Moon", role = "ctb", email = "moon@utstat.toronto.edu"),
person("Mark", "Miller", role = "ctb", email = "725mrm@gmail.com"),
formatR, collections, evaluate, rstudioapi, berryFunctions,
Suggests: ggplot2 (>= 3.0.0), testthat (>= 2.1.0), highr, rmarkdown,
covr, broom, boot, gganimate, gifski, forcats, stringr,
cowplot, tidybayes, png, stringi, modelr, styler, future
Packaged: 2022-07-02 20:15:56 UTC; abhraneelsarma

Title: Version Controlled Package Manager
Authors@R: person(c('Siete', 'C.'), 'Frouws', , 'scfrouws@gmail.com', role = c('aut', 'cre'))
Description: Supports installing of multiple versions of a package and loading
of specific versions and their dependencies. The only package that you need
to install is this package providing the support for the complete library
management. Migrate your ordinary library today!!
Suggests: testthat (>= 3.0.0), waldo, devtools, withr, callr
Maintainer: Siete C. Frouws <scfrouws@gmail.com>

Title: Cooperative Learning for Multi-View Analysis
Authors@R: c(person("Daisy Yi Ding", role=c("aut"),
Description: 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) <arXiv:2112.12337>).
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), xfun
Imports: glmnet, Matrix, methods, RColorBrewer, Rcpp, stats, survival,

Description: 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>).