<div class="csl-bib-body">
<div class="csl-entry">Parzer, R., Vana Gür, L., & Filzmoser, P. (2024). <i>spar: Sparse Projected Averaged Regression in R</i>. arXiv. https://doi.org/10.34726/8080</div>
</div>
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/207913
-
dc.identifier.uri
https://doi.org/10.34726/8080
-
dc.description.abstract
Package spar for R builds ensembles of predictive generalized linear models with high-dimensional predictors. It employs an algorithm utilizing variable screening and random projection tools to efficiently handle the computational challenges associated with large sets of predictors. The package is designed with a strong focus on extensibility. Screening and random projection techniques are implemented as S3 classes with user-friendly constructor functions, enabling users to easily integrate and develop new procedures. This design enhances the package's adaptability and makes it a powerful tool for a variety of high-dimensional applications.
en
dc.description.sponsorship
FWF - Österr. Wissenschaftsfonds
-
dc.language.iso
en
-
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
-
dc.subject
Random Projection
en
dc.subject
Screening
en
dc.subject
Ensemble learning
en
dc.subject
software R
en
dc.title
spar: Sparse Projected Averaged Regression in R
en
dc.type
Preprint
en
dc.type
Preprint
de
dc.rights.license
Creative Commons Namensnennung 4.0 International
de
dc.rights.license
Creative Commons Attribution 4.0 International
en
dc.identifier.doi
10.34726/8080
-
dc.identifier.arxiv
2411.17808
-
dc.relation.grantno
ZK 35-G
-
tuw.project.title
Hochdimensionales statistisches Lernen: Neue Methoden zur Förderung der Wirtschafts- und Nachhaltigkeitspolitik