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DMRnet
View on CRAN: Click
here
Download and install DMRnet package within the R console
Install from CRAN:
install.packages("DMRnet")
Install from Github:
library("remotes")
install_github("cran/DMRnet")
Install by package version:
library("remotes")
install_version("DMRnet", "0.4.0")
Attach the package and use:
library("DMRnet")
Maintained by
Szymon Nowakowski
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.DMRnet . https://github.com/SzymonNowakowski/DMRnet/issues . https://github.com/SzymonNowakowski/DMRnet . DMRnet citation info . DMRnet results . DMRnet.pdf . Getting Started with DMRnet . DMRnet_0.4.0.tar.gz . DMRnet_0.4.0.zip . DMRnet_0.4.0.zip . DMRnet_0.4.0.zip . DMRnet_0.4.0.tgz . DMRnet_0.4.0.tgz . DMRnet_0.4.0.tgz . DMRnet_0.4.0.tgz . DMRnet_0.4.0.tgz . DMRnet_0.4.0.tgz . DMRnet archive . https://CRAN.R-project.org/package=DMRnet .
First Published: 2017-09-05
Latest Update: 2023-08-07
Description:
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 Soltys, 2023. Improving Group Lasso for High-Dimensional Categorical Data. In: Computational Science
How to cite:
Szymon Nowakowski (2017). DMRnet: Delete or Merge Regressors Algorithms for Linear and Logistic Model Selection and High-Dimensional Data. R package version 0.4.0, https://cran.r-project.org/web/packages/DMRnet. Accessed 29 Mar. 2025.
Previous versions and publish date:
Other packages that cited DMRnet R package
View DMRnet citation profile
Other R packages that DMRnet depends,
imports, suggests or enhances
Complete documentation for DMRnet
Functions, R codes and Examples using
the DMRnet R package
Some associated functions: DMR . DMRnet-package . DMRnet . coef.DMR . coef.cv.DMR . coef.gic.DMR . cv.DMR . cv.DMRnet . gic.DMR . miete . plot.DMR . plot.cv.DMR . plot.gic.DMR . predict.DMR . predict.cv.DMR . predict.gic.DMR . print.DMR . promoter .
Some associated R codes: DMR.R . DMR4glm.R . DMR4glm_help.R . DMR4lm.R . DMR4lm_help.R . DMRnet-package.R . DMRnet.R . DMRnet4glm.R . DMRnet4glm_help.R . DMRnet4lm.R . DMRnet4lm_help.R . SOSnet4glm.R . SOSnet4glm_help.R . SOSnet4lm.R . SOSnet4lm_help.R . clusters_4glm_help.R . clusters_4lm_help.R . coef.DMR.R . coef.cv.DMR.R . coef.gic.DMR.R . constants.R . cv.DMR.R . cv.DMRnet.R . cv_GIC_indexed.R . cv_MD_indexed.R . cv_compute_model.R . cv_indexation.mode_distribute.R . gic.DMR.R . glamer_4glm.R . glamer_4glm_help.R . glamer_4lm.R . glamer_4lm_help.R . glamer_stats.R . miete.R . onAttach.R . part2beta_glm_help.R . part2beta_help.R . plot.DMR.R . plot.cv.DMR.R . plot.gic.DMR.R . postlasso_O_step_preparation.R . postlasso_common.R . postlasso_fac.R . postlasso_glamer.R . predict.DMR.R . predict.cv.DMR.R . predict.gic.DMR.R . prelasso_binomial.R . prelasso_common.R . prelasso_cont_columns.R . print.DMR.R . promoter.R . release_questions.R . t_stats.R . w_stats.R . wrap_up_binomial.R . wrap_up_gaussian.R . Full DMRnet package functions and examples
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