R Observatory: Metrics for NU.Learning version 1.5

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

NU.Learning

1.5

Nonparametric and Unsupervised Learning from Cross-Sectional Observational Data

0packages depend

2Kdownloads / year

–test coverage

13/13checks pass

Overview

About

Maintained by Bob ObenchainFirst published 2023-09-301 releasesCRAN page ↗

Especially when cross-sectional data are observational, effects of treatment selection bias and confounding are best revealed by using Nonparametric and Unsupervised methods to "Design" the analysis of the given data ...rather than the collection of "designed data". Specifically, the "effect-size distribution" that best quantifies a potentially causal relationship between a numeric y-Outcome variable and either a binary t-Treatment or continuous e-Exposure variable needs to consist of BLOCKS of relatively well-matched experimental units (e.g. patients) that have the most similar X-confounder characteristics. Since our NU Learning approach will form BLOCKS by "clustering" experimental units in confounder X-space, the implicit statistical model for learning is One-Way ANOVA. Within Block measures of effect-size are then either [a] LOCAL Treatment Differences (LTDs) between Within-Cluster y-Outcome Means ("new" minus "control") when treatment choice is Binary or else [b] LOCAL Rank Correlations (LRCs) when the e-Exposure variable is numeric with (hopefully many) more than two levels. An Instrumental Variable (IV) method is also provided so that Local Average y-Outcomes (LAOs) within BLOCKS may also contribute information for effect-size inferences when X-Covariates are assumed to influence Treatment choice or Exposure level but otherwise have no direct effects on y-Outcomes. Finally, a "Most-Like-Me" function provides histograms of effect-size distributions to aid Doctor-Patient (or Researcher-Society) communications about Heterogeneous Outcomes. Obenchain and Young (2013) doi:10.1080/15598608.2013.772821; Obenchain, Young and Krstic (2019) doi:10.1016/j.yrtph.2019.104418.

Install

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

install.packages("NU.Learning", 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-nu.learning

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled.

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-NU.Learning

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-NU.Learning

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed).

Health

CRAN checks

13OK

Slowest check: 1.1 min · r-oldrel-windows-x86_64

Code health

None

Tests · ratio 0.00

not tracked

Coverage

100%

Documentation · exports

2

Dependencies · direct

Check history

OK2026-08-05
13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
NOTE2026-08-01
12 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
OK2026-03-10
14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

READMENoVignettesNopkgdown siteNoNEWSNoCode of conductNoContributing guideNo

Examples that run: 50%
Documented parameters: 100%
Return-value docs: 100%
References docs: 67%

Downloads

2K

CRAN downloads in the past year

Rank #18,914 · ~5/day · ~167/mo

Releases R versions

Daily download trend is not available in this view yet.

17730 days

58290 days

2K1 year

Compare downloads with other packages →

Also on137 r2u24 autocran

Dependencies

Declared dependencies

0 external dependencies (excludes base and recommended)

Depends (3)

R >= 3.5.0clusterlattice

Imports (0)

none

LinkingTo (0)

none

Suggests (0)

none

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

Code & Tests

Datasets

People & History

People (2)

Maintainer (1)

Bob Obenchain

Author, Maintainer

Authors (1)

Bob Obenchain

Author, Maintainer

Contributors (1)

Stan Young

Contributor

Package Timeline

1 release. R releases are shown for context.

R
R 4.6.0 released · 2026-04-24
1.5Latest
2026-03-10 · current release
R
R 4.5.0 released · 2025-04-11

Package metadata

First published: 2023-09-30
Total releases: 1 / 3 yrs
License: GPL-2 OSI
Minimum R: ≥ 3.5.0
Bundled data: 2.0 MB / 4 files
Download size: 2.0 MB
Installed size: not tracked yet
With dependencies: not tracked yet

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("NU.Learning")

Format

Obenchain, B., & Young, S. (2023). NU.Learning: Nonparametric and Unsupervised Learning from Cross-Sectional Observational Data (Version 1.5) [Computer software]. https://doi.org/10.32614/CRAN.package.NU.Learning

This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.

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 NU.Learning version 1.5 [Data set]. HJJB, LLC. Data release v2026-08-22. https://doi.org/10.5281/zenodo.21843040

From data release v2026-08-22, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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