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Group-Adaptive Elastic Net Penalised Generalised Linear Models
Description
Fit linear and logistic regression models penalised with group-adaptive elastic net penalties.
The group penalties correspond to groups of covariates defined by a co-data group set.
The method accommodates inclusion of unpenalised covariates and overlapping groups.
See Van Nee et al. (2021) <arXiv:2101.03875>.
Details
See squeezy for example code.
Author(s)
Mirrelijn M. van Nee [aut, cre],
Tim van de Brug [aut],
Mark A. van de Wiel [aut]
Maintainer: Mirrelijn M. van Nee <m.vannee@amsterdamumc.nl>
References
Mirrelijn M. van Nee, Tim van de Brug, Mark A. van de Wiel, "Fast marginal likelihood estimation of penalties for group-adaptive elastic net.", 2021
squeezy documentation built on May 13, 2022, 5:08 p.m.
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| squeezy-package | R Documentation |
Group-Adaptive Elastic Net Penalised Generalised Linear Models
Description
Fit linear and logistic regression models penalised with group-adaptive elastic net penalties. The group penalties correspond to groups of covariates defined by a co-data group set. The method accommodates inclusion of unpenalised covariates and overlapping groups. See Van Nee et al. (2021) <arXiv:2101.03875>.
Details
See squeezy for example code.
Author(s)
Mirrelijn M. van Nee [aut, cre], Tim van de Brug [aut], Mark A. van de Wiel [aut]
Maintainer: Mirrelijn M. van Nee <m.vannee@amsterdamumc.nl>
References
Mirrelijn M. van Nee, Tim van de Brug, Mark A. van de Wiel, "Fast marginal likelihood estimation of penalties for group-adaptive elastic net.", 2021
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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