R Observatory: Metrics for decisionfacets version 0.2.0

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

decisionfacets

0.2.0Package record

Decision Accuracy and Consistency for Rater-Mediated Exams

0packages depend

255downloads / year

89.4%test coverage

13/13flavors OK

Overview

About

Maintained by Daniel EdiCRAN page ↗GitHub ↗Website ↗Issues ↗

Answers "would this candidate have passed with a different set of raters?" for rater-mediated exams such as oral examinations, objective structured clinical examinations and essay scoring. Builds on the many-facet extension of the rating scale model (Andrich, 1978, doi:10.1007/BF02293814) to compute counterfactual pass probabilities under the observed, an average-severity and a random rater panel, under an explicit decision rule (raw total, fair average or measure), and splits expected misclassification into measurement error and rater assignment, extending item response theory classification accuracy (Lee, 2010, doi:10.1111/j.1745-3984.2009.00096.x) to rater effects. Supports panels in which every rater scores every task and designs in which each case is scored by a different examiner, with rating categories or continuous scores such as 0-100 (through a linear many-facet model). Models can be fitted with 'TAM' or built-in estimators.

Changes in 0.2.0

Designs with one examiner per case (or any partially overlapping rating design) are now supported. df_counterfactual() compares each candidate's observed examiners with random reassignments that draw, for each case, an examiner qualified for that case, without repeating an examiner for the same candidate. df_simulate() gains design = "per_item" and raters_per_item.
Continuous scores (for example 0-100) are supported through a linear many-facet model, score = ability - case difficulty - examiner severity + error. df_data() gains scale = c("auto", "ordinal", "continuous"); df_fit() uses the new "linear" engine for continuous scores; df_simulate() gains scale = "continuous".
New decision rule df_cut(value, "raw_mean"): pass if the mean observed rating reaches value (e.g. 70), convenient when candidates have different numbers of ratings.

Show all changes in 0.2.0Hide the rest of the changes in 0.2.0

df_fit() stops with a clear message when the rating network is disconnected, because rater severities would then not be comparable.
New synthetic data set oral_exam: 300 candidates, 12 cases, one examiner per case, 0-100 scores.
Results for the existing crossed designs with ordinal scores are unchanged.

From NEWS.md in the release

Install

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

install.packages("decisionfacets", 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("edidatasolutions/decisionfacets")

Development version from edidatasolutions/decisionfacets. Installs pak first if it is not already available.

# 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-decisionfacets

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-decisionfacets

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: 2.2 min · r-oldrel-macos-x86_64

Status changes

OK13 OKNow
OK2 OK2026-10-07

Advisories

none known

How this is measured·How advisories are found

Documentation

In release 0.2.0

READMEYes · 906 wordsVignettesYes · 1 · dynamicNEWSYes · 100% structured · mentions 0.2.0Citation fileNoWebsiteYes · pkgdown

Help pages: 9
Examples that run: 100%9 run in a plain check
Exported functions with examples: 100%
Documented parameters: 77%
Return-value docs: 100%
Help pages citing references: 0%

Vignettes (1)

Would this candidate have passed with different raters? HTML

How these are measured

Downloads

255

CRAN downloads in the past year

Rank #25,386 · ~1/day · ~21/mo

Releases R versions

Daily download trend is not available in this view yet.

25530 days

25590 days

2551 year

Compare downloads with other packages →

Also on4 autocran

How downloads are counted

Repository

edidatasolutions/decisionfacets ↗Account: edidatasolutions

0Stars

0Forks

0Open issues

0Open PRs

0Releases

Last activity 2026-10-21

Responsiveness

not measured

How these are measured

PracticesBeta

8 development-tooling and community-health practices detected across 5 families in the upstream repository

Checks run against github.com/edidatasolutions/decisionfacets on 2026-10-18.

Continuous integration (2)

GitHub ActionsR CMD check workflow

The R CMD check workflow runs on Linux, macOS and Windows, and on R-devel

Governance and community (1)

Contributing guide

Docs source (3)

pkgdown

GitHub Pages site last deployed 2026-10-10 · The repository has a site deploy workflow

pkgdown site last built 2026-10-10, before release 0.2.0 (2026-10-21)

Reproducibility and dev environment (1)

data-raw/

Show all practicesHide 1 practice

CRAN release process (1)

cran-comments.md

How this is detected·Detection ruleset v3 (2026-09-26)

AI coding toolsBeta

This repository shows signs of AI coding tools.

Claude Codefirst seen 2026-10-07
Commits: at least 4 crediting it in the message

Models named in its commits

Claude Opus 5.54 commits · Oct 2026

Counts are commits, not lines of code. Commits by a tool's accounts count once across all of its accounts. Commits crediting it are the largest count among its credit lines, a minimum.How this is detected·Detection ruleset v4 (updated 2026-09-27)

Dependencies

Declared dependencies

3 external dependencies (excludes base and recommended)

Installing decisionfacets adds no packages beyond those that come with R.

Depends (1)

R >= 4.1

Imports (2)

statsutils

LinkingTo (0)

none

Suggests (3)

TAM knitr markdown

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

How these are counted

Code & Tests

Datasets

People & History

People (1)

Daniel Edi

Releases (2)

Pick two releases to compare their code metrics.

0.2.0Latest
record
2026-10-21 · current release · diff ↗
0.1.0
record
2026-10-07 ·
3 days in CRAN's queue
Listed in CRAN's incoming queue from 2026-10-03 21:57 to 2026-10-07 06:45 UTC, in 16 readings.
R
R 4.6.0 released · 2026-04-24

Across CRAN, the median accepted release of the past year spent 23 hours in the queue, 90% under 11 days.How queue time is measured

Package metadata

First published: 2026-10-07
Total releases: 2 / 1 yrs
License: MIT + file LICENSE OSI
Minimum R: ≥ 4.1
Bundled data: 6.9 KB / 1 file
Download size: 46 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("decisionfacets")

Format

Edi, D. (2026). decisionfacets: Decision Accuracy and Consistency for Rater-Mediated Exams (Version 0.2.0) [Computer software]. https://doi.org/10.32614/CRAN.package.decisionfacets

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 decisionfacets version 0.2.0 [Data set]. HJJB, LLC. Data release v2026-10-21. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-21, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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