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CRAN: Package DMRnet

DMRnet: Delete or Merge Regressors Algorithms for Linear and Logistic Model Selection and High-Dimensional Data

Model selection algorithms for regression and classification, where the predictors can be continuous or categorical and the number of regressors may exceed the number of observations. The selected model consists of a subset of numerical regressors and partitions of levels of factors. Szymon Nowakowski, Piotr Pokarowski, Wojciech Rejchel and Agnieszka Sołtys, 2023. Improving Group Lasso for High-Dimensional Categorical Data. In: Computational Science – ICCS 2023. Lecture Notes in Computer Science, vol 14074, p. 455-470. Springer, Cham. <doi:10.1007/978-3-031-36021-3_47>. Aleksandra Maj-Kańska, Piotr Pokarowski and Agnieszka Prochenka, 2015. Delete or merge regressors for linear model selection. Electronic Journal of Statistics 9(2): 1749-1778. <doi:10.1214/15-EJS1050>. Piotr Pokarowski and Jan Mielniczuk, 2015. Combined l1 and greedy l0 penalized least squares for linear model selection. Journal of Machine Learning Research 16(29): 961-992. <https://www.jmlr.org/papers/volume16/pokarowski15a/pokarowski15a.pdf>. Piotr Pokarowski, Wojciech Rejchel, Agnieszka Sołtys, Michał Frej and Jan Mielniczuk, 2022. Improving Lasso for model selection and prediction. Scandinavian Journal of Statistics, 49(2): 831–863. <doi:10.1111/sjos.12546>.

Version: 0.4.1
Imports: hclust1d, glmnet, grpreg, stats, graphics, utils
Suggests: rmarkdown, knitr
Published: 2025-08-18
DOI: 10.32614/CRAN.package.DMRnet
Author: Agnieszka Prochenka-Sołtys [aut] (previous maintainer for versions <= 0.2.0), Piotr Pokarowski [aut], Szymon Nowakowski ORCID iD [aut, cre]
Maintainer: Szymon Nowakowski <s.nowakowski at mimuw.edu.pl>
BugReports: https://github.com/SzymonNowakowski/DMRnet/issues
License: GPL-2
URL: https://github.com/SzymonNowakowski/DMRnet
NeedsCompilation: no
Citation: DMRnet citation info
Materials: README, NEWS
CRAN checks: DMRnet results

Documentation:

Reference manual: DMRnet.html , DMRnet.pdf
Vignettes: Getting Started with DMRnet (source, R code)

Downloads:

Package source: DMRnet_0.4.1.tar.gz
Windows binaries: r-devel: DMRnet_0.4.1.zip, r-release: DMRnet_0.4.1.zip, r-oldrel: DMRnet_0.4.1.zip
macOS binaries: r-release (arm64): DMRnet_0.4.1.tgz, r-oldrel (arm64): DMRnet_0.4.1.tgz, r-release (x86_64): DMRnet_0.4.1.tgz, r-oldrel (x86_64): DMRnet_0.4.1.tgz
Old sources: DMRnet archive

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