joinet

Multivariate Elastic Net Regression

Implements high-dimensional multivariate regression by stacked generalisation (Wolpert 1992 <doi:10.1016/S0893-6080(05)80023-1>). 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.

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Description file content

Package
joinet
Version
0.0.2
Title
Multivariate Elastic Net Regression
Description
Implements high-dimensional multivariate regression by stacked generalisation (Wolpert 1992 ). 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.
Depends
R (>= 3.0.0)
Imports
glmnet, palasso, cornet
Suggests
knitr, testthat, MASS
Enhances
spls, SiER, MRCE
VignetteBuilder
knitr
License
GPL-3
LazyData
true
Language
en-GB
RoxygenNote
6.1.1
URL
BugReports
https://github.com/rauschenberger/joinet/issues
NeedsCompilation
no
Packaged
2019-08-08 15:48:38 UTC; armin.rauschenberger
Author
Armin Rauschenberger [aut, cre]
Maintainer
Armin Rauschenberger
Repository
CRAN
Date/Publication
2019-08-08 16:40:02 UTC

install.packages('joinet')

0.0.2

13 days ago

https://github.com/rauschenberger/joinet

Armin Rauschenberger

GPL-3

Depends on

R (>= 3.0.0)

Imports

glmnet, palasso, cornet

Suggests

knitr, testthat, MASS

Discussions