R Observatory: Metrics for univariateML version 1.5.0

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

univariateML

1.5.0

Maximum Likelihood Estimation for Univariate Densities

9packages depend

16.7Kdownloads / year

95.4%test coverage

13/13checks pass

Overview

About

Maintained by Jonas MossFirst published 2019-12-184 releasesCRAN page ↗GitHub ↗

User-friendly maximum likelihood estimation (Fisher (1921) doi:10.1098/rsta.1922.0009) of univariate densities.

Install

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

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

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

if (!requireNamespace("pak", quietly = TRUE))
  install.packages("pak", repos = "https://cloud.r-project.org/")
pak::pak("JonasMoss/univariateML")

Development version from JonasMoss/univariateML. Installs pak first if it is not already available.

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-univariateml

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-univariateML

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-univariateML

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

Health

CRAN checks

13OK

Slowest check: 4.8 min · r-devel-windows-x86_64

Code health

Yes

Tests · ratio 0.55

95.4%

Coverage · measured lines

100%

Documentation · exports

12

Dependencies · direct

Check history

OK2026-08-04
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

READMEYes · 642 wordsVignettesYes · dynamicpkgdown siteNoNEWSNoCode of conductNoContributing guideNo

Examples that run: 98%
Documented parameters: 99%
Return-value docs: 96%
References docs: 73%

Downloads

16.7K

CRAN downloads in the past year

Rank #3,686 · ~46/day · ~1.4K/mo

Releases R versions

Daily download trend is not available in this view yet.

67730 days

2.5K90 days

16.7K1 year

Compare downloads with other packages →

Also on126 r2u17 autocran

Repository

GitHub ↗ jonasmoss/univariateml

9Stars

6Forks

4Open issues

0Open PRs

6Releases

365Commits

3Contributors

estimationdensitymaximum-likelihood

365 commits · Last activity 2025-03-03 · 0% stars, 30d

Stars over time

2025-07-30 · 92026-07-07 · 9

Repository practices

Upstream repositoryBeta

9 development-tooling and community-health practices detected across 6 families in the upstream repository

Checks run against github.com/jonasmoss/univariateml on 2026-08-16.

Continuous integration (3)

GitHub ActionsTravis CIAppVeyor

CRAN release process (2)

cran-comments.mdCRAN-SUBMISSION

Docs source (1)

README.Rmd

Lint, format, editor (1)

RStudio project

Show all practices

Coverage (1)

Codecov

Git structural (1)

.gitattributes

How this is detected·Detection ruleset v1 (2026-07-18)

Dependencies

Declared dependencies

19 external dependencies (excludes base and recommended)

Depends (1)

R >= 2.10

Imports (12)

assertthat extraDistr tibble logitnorm actuar nakagami fGarch rlang intervals Rfast pracma sads

LinkingTo (0)

none

Suggests (7)

testthat knitr rmarkdown markdown copula dplyr covr

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

9direct

1indirect

EBcoBART RealSurvSim ale autogam kdensity peppwR piecenorms svines bifrost

Code & Tests

Datasets

People & History

People (3)

Maintainer (1)

Jonas Moss

Author, Maintainer

Authors (1)

Jonas Moss

Author, Maintainer

Contributors (2)

Thomas Nagler

Contributor · added in 1.1.0

Chitu Okoli

Contributor · added in 1.5.0

Package Timeline

4 releases. Pick two to compare their code metrics. R releases are shown for context.

R
R 4.6.0 released · 2026-04-24
R
R 4.5.0 released · 2025-04-11
1.5.0Latest
2025-03-04 · current release · diff ↗
R
R 4.4.0 released · 2024-04-24
R
R 4.3.0 released · 2023-04-21
R
R 4.2.0 released · 2022-04-22
1.1.1
2022-01-25 · diff ↗
R
R 4.1.0 released · 2021-05-18
1.1.0
2020-08-05 · diff ↗
R
R 4.0.0 released · 2020-04-24
1.0.0
2019-12-18
R
R 3.6.0 released · 2019-04-26

Package metadata

First published: 2019-12-18
Total releases: 4 / 7 yrs
License: MIT + file LICENSE OSI
Minimum R: ≥ 2.10
Bundled data: 39 KB / 3 files
Download size: 565 KB
Installed size: not tracked yet
With dependencies: not tracked yet

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("univariateML")

Format

Moss, J., Nagler, T., & Okoli, C. (2025). univariateML: Maximum Likelihood Estimation for Univariate Densities (Version 1.5.0) [Computer software]. https://doi.org/10.32614/CRAN.package.univariateML

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 univariateML version 1.5.0 [Data set]. HJJB, LLC. Data release v2026-08-18. https://doi.org/10.5281/zenodo.21843040

From data release v2026-08-18, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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