R Observatory: Metrics for glmnetr version 0.6-4

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

glmnetr

0.6-4Package record

Nested Cross Validation for the Relaxed Lasso and Other Machine Learning Models

0packages depend

8.7Kdownloads / year

–test coverage

13/13flavors OK

Overview

About

Maintained by Walter K KremersCRAN page ↗

Cross validation informed Relaxed LASSO (or more generally elastic net), gradient boosting machine ('xgboost'), Random Forest ('RandomForestSRC'), Oblique Random Forest ('aorsf'), Artificial Neural Network (ANN), Recursive Partitioning ('RPART') or step wise regression models are fit. Cross validation leave out samples (leading to nested cross validation) or bootstrap out-of-bag samples are used to evaluate and compare performances between these models with results presented in tabular or graphical means. Calibration plots can also be generated, again based upon (outer nested) cross validation or bootstrap leave out (out of bag) samples. Note, at the time of this writing, in order to fit gradient boosting machine models one must install the packages 'DiceKriging' and 'rgenoud' using the install.packages() function. For some datasets, for example when the design matrix is not of full rank, 'glmnet' may have very long run times when fitting the relaxed lasso model, from our experience when fitting Cox models on data with many predictors and many patients, making it difficult to get solutions from either glmnet() or cv.glmnet(). This may be remedied by using the 'path=TRUE' option when calling glmnet() and cv.glmnet(). Within the 'glmnetr' package the approach of path=TRUE is taken by default. other packages doing similar include 'nestedcv' https://cran.r-project.org/package=nestedcv, 'glmnetSE' https://cran.r-project.org/package=glmnetSE which may provide different functionality when performing a nested CV. Use of the 'glmnetr' has many similarities to the 'glmnet' package and it could be helpful for the user of 'glmnetr' also become familiar with the 'glmnet' package https://cran.r-project.org/package=glmnet, with the "An Introduction to 'glmnet'" and "The Relaxed Lasso" being especially useful in this regard.

Install

R (CRAN)Ubuntu (r2u)Fedora (COPR)openSUSE (OBS)

install.packages("glmnetr", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-glmnetr

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled. We list this channel from recorded r2u download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-glmnetr

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-glmnetr

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed). We list this channel from recorded autoCRAN download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

Health

CRAN checks

13OK

CRAN check page ↗

Slowest check: 5.8 min · r-oldrel-windows-x86_64

Status changes

OK13 OK2026-08-05
NOTE12 OK, 1 NOTE2026-08-01
OK13 OK2026-06-09
ERROR12 OK, 1 ERROR2026-06-08
OK14 OK2026-03-10

Advisories

none known

How this is measured·How advisories are found

Documentation

In release 0.6-4

READMENoVignettesYes · 12 · dynamicNEWSNoCitation fileNoWebsitenot declared in DESCRIPTION

Help pages: 52
Examples that run: 47%7 run in a plain check · 8 only when donttest examples are enabled (R CMD check --as-cran)
Exported functions with examples: 26%
Documented parameters: 100%
Return-value docs: 97%
Help pages citing references: 0%

Vignettes (12)

An Overview of glmnetr PDF
An Overview of glmnetr PDF
Calibration of Machine Learning Models PDF
Calibration of Machine Learning Models PDF
Elastic net models PDF
Lasso and Ridge Stability PDF
Ridge and Lasso PDF
Using ann_tab_cv PDF
Using ann_tab_cv PDF
Using stepreg PDF

Show 2 moreHide 2 vignettes

Using stepreg PDF
Using stepreg PDF

How these are measured

Downloads

8.7K

CRAN downloads in the past year

Rank #4,794 · ~24/day · ~728/mo

Releases R versions

Daily download trend is not available in this view yet.

79330 days

1.7K90 days

8.7K1 year

Compare downloads with other packages →

Also on110 r2u6 autocran

How downloads are counted

Dependencies

Declared dependencies

12 external dependencies (excludes base and recommended)

Installing glmnetr adds 110 packages (58 with compiled code, 89 MB of source tarballs). Heaviest: stringi 11 MB, data.table 5.7 MB, bslib 5.6 MB.

Depends (1)

R >= 3.4.0

Imports (14)

glmnetsurvivalMatrixxgboost lgr paradox bbotk mlr3mbo DiceKriging pROC randomForestSRCrparttorch aorsf

LinkingTo (0)

none

Suggests (1)

R.rsp

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

How these are counted

Code & Tests

People & History

People (2)

Walter K Kremers

Author, Maintainer since 0.1-1 (2022)

Nicholas B Larson

Contributor

Listed in earlier versions (2)

Walter K

no longer listed · 0.1-1 to 0.6-4

Nicholas B

no longer listed · 0.2-0 to 0.6-4

Releases (20)

Pick two releases to compare their code metrics.

0.6-4Latest
record
2026-09-16 · current release · diff ↗ ·
added 1 export
DK_dev_
R
R 4.6.0 released · 2026-04-24
0.6-3
record
2025-12-16 · diff ↗
0.6-2
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2025-08-19 · diff ↗ ·
removed 2 exports
nested.compare_0_5_1, plot_perf_glmnetr_0_5_5
0.6-1
record
2025-05-10 · diff ↗ ·
removed 1 export
glmnetr

added 1 export
plot_perf_glmnetr_0_5_5
R
R 4.5.0 released · 2025-04-11
0.5-5
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2025-01-17 · diff ↗ ·
added 1 export
nested.compare_0_5_1
0.5-4
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2024-10-24 · diff ↗
0.5-3
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2024-08-28 · diff ↗ ·
added 1 export
get.id.foldid
0.5-2
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2024-07-12 · diff ↗ ·
added 1 export
boot.factor.foldid
0.5-1
record
2024-05-12 · diff ↗ ·
removed 2 exports
predict_nested_rf, predict_nested_xgb

added 5 exports
diff_time1, nested.cis, nested.compare, orf_tune, rederive_orf
R
R 4.4.0 released · 2024-04-24
0.4-6
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2024-04-21 · diff ↗
0.4-5
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2024-04-20 · diff ↗ ·
added 2 exports
calplot, glmnetr.cis
0.4-4
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2024-03-23 · diff ↗ ·
removed 6 exports
bsint, dtstndrz, prednn_tl, wtlast, wtmiddle, wtzero

added 4 exports
predict_nested_rf, predict_nested_xgb, rederive_rf, rederive_xgb
0.4-3
record
2024-03-04 · diff ↗ ·
added 4 exports
devrat_, glmnetr_seed, plot_perf_glmnetr, roundperf

Show 11 earlier eventsHide 11 earlier events

0.4-2
record
2024-02-09 · diff ↗ ·
removed 1 export
glmnetr.foldid

added 1 export
get.foldid
0.4-1
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2024-01-09 · diff ↗ ·
added 1 export
rf_tune
0.3-1
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2023-08-10 · diff ↗
unarchivedReturned to CRAN
2023-06-07
0.2-1
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2023-06-07 · diff ↗
archivedRemoved from CRAN
2023-05-28
issues were not corrected in time
0.2-0
record
2023-05-10 · diff ↗ ·
removed 1 export
difftime2

added 15 exports
ann_tab_cv, ann_tab_cv_best, bsint, calceloss, diff_time, dtstndrz, factor.foldid, glmnetr.foldid, predict_ann_tab, prednn_tl, wtlast, wtmiddle, wtzero, xgb.simple, xgb.tuned
R
R 4.3.0 released · 2023-04-21
0.1-2
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2023-02-18 · diff ↗
0.1-1
record
2022-12-14
R
R 4.2.0 released · 2022-04-22

Package metadata

First published: 2022-12-14
Total releases: 20 / 4 yrs
License: GPL-3 OSI
Copyright: Mayo Foundation for Medical Education and Research
Minimum R: ≥ 3.4.0
Download size: 2.2 MB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("glmnetr")

Format

Kremers, W. K., & Larson, N. B. (2026). glmnetr: Nested Cross Validation for the Relaxed Lasso and Other Machine Learning Models (Version 0.6-4) [Computer software]. https://doi.org/10.32614/CRAN.package.glmnetr

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

Format

APA

Balamuta, J. J. (2026). R Observatory: Metrics for glmnetr version 0.6-4 [Data set]. HJJB, LLC. Data release v2026-10-22. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-22, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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