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Package: joinet
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Version: 0.0.4
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Title: Multivariate Elastic Net Regression
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Description: 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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Depends: R (>= 3.0.0)
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Imports: glmnet, palasso, cornet
Suggests: knitr, testthat, MASS
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Enhances: mice, earth, spls, MRCE, remMap, MultivariateRandomForest, SiER, MCEN, GPM, RMTL, MTPS
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Authors@R: person("Armin","Rauschenberger",email="armin.rauschenberger@uni.lu",role=c("aut","cre"))
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VignetteBuilder: knitr
License: GPL-3
LazyData: true
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Language: en-GB
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RoxygenNote: 7.1.0
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URL: https://github.com/rauschenberger/joinet
BugReports: https://github.com/rauschenberger/joinet/issues