R Observatory: Metrics for mixedsubjectsirt version 1.0.0

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

mixedsubjectsirt

1.0.0Package record

Item Response Theory Calibration with a Mixed Subjects Design

0packages depend

1.2Kdownloads / year

74.7%test coverage

13/13flavors OK

Overview

About

Maintained by Klint KanopkaCRAN page ↗GitHub ↗Website ↗Issues ↗

Integrates large language model generated item responses into psychometric calibration studies through a mixed-subjects design for unidimensional two-parameter and one-parameter logistic item response theory models. Human pilot responses are augmented with model-generated responses using a prediction-powered inference estimator (Angelopoulos, Bates, Fannjiang, Jordan and Zrnic (2023) doi:10.1126/science.adi6000; Angelopoulos, Duchi and Zrnic (2023) doi:10.48550/arXiv.2311.01453) adapted to marginal maximum-likelihood estimation, following the mixed-subjects design of Broska, Howes and van Loon (2025) doi:10.1177/00491241251326865. The estimator is anchored to the human responses and is asymptotically unbiased for the human item parameters at any tuning weight; the weight on the synthetic responses is chosen to minimize propagated ability-score risk, down-weighting uninformative or biased generated responses. Louis-corrected sandwich standard errors, ability scoring, cross-fitted tuning, and scale linking are also provided.

Changes in 1.0.0

Initial CRAN release.

Mixed-subjects 2PL/1PL IRT calibration that augments human responses with LLM-generated responses through a PPI++ marginal-MML estimator (fit_mixed_subjects_mml() and relatives). The estimator is anchored to the human data and is asymptotically unbiased for the human item parameters at any tuning weight.
Power tuning by **ability-score risk** (tune_lambda_ability_risk()), which selects the tuning weight by direct 1-D optimization of propagated ability-recovery risk (pass method = "grid" to scan a grid instead). Also included: a theoretical PPI++ score diagnostic (tune_lambda_ppi_score()), cross-fitted tuning (tune_lambda_ability_risk_crossfit(), the recommended workflow for reported analyses), and experimental per-item tuning (tune_lambda_ability_risk_item()). All non-experimental tuners use the marginal-MML estimator by default; the frozen expected-count estimator remains available via fit_fn but is discouraged.

Show all changes in 1.0.0Hide the rest of the changes in 1.0.0

Louis-corrected marginal sandwich covariance through the vcov() S3 method (vcov_mixed_subjects_mml()), with ability scoring and item-parameter uncertainty propagation (score_theta(), ability_risk()).
Vignettes covering the recommended workflow, lambda tuning, the 1PL model, per-item tuning, scale linking, and a simulation-validation study; an R-CMD-check GitHub Actions workflow.
Currently predicted and generated data must be **binary 0/1 responses** in the high-level fitting and PPI-score functions; the low-level quadrature utilities accept fractional input.

From NEWS.md in the release

Install

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

install.packages("mixedsubjectsirt", 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("klintkanopka/mixedsubjectsirt")

Development version from klintkanopka/mixedsubjectsirt. 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-mixedsubjectsirt

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-mixedsubjectsirt

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-mixedsubjectsirt

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: 13.9 min · r-oldrel-windows-x86_64

Status changes

OK13 OK2026-08-05
NOTE12 OK, 1 NOTE2026-08-01
OK7 OK2026-06-26

Advisories

none known

How this is measured·How advisories are found

Documentation

In release 1.0.0

READMEYes · 545 wordsVignettesYes · 8 · dynamicNEWSYes · 67% structured · mentions 1.0.0Citation fileNoWebsiteYes · pkgdown

Help pages: 32
Examples that run: 100%27 run in a plain check
Exported functions with examples: 84%
Documented parameters: 91%
Return-value docs: 100%
Help pages citing references: 0%

Vignettes (8)

How these are measured

Downloads

1.2K

CRAN downloads in the past year

Rank #13,224 · ~3/day · ~100/mo

Releases R versions

Daily download trend is not available in this view yet.

28230 days

87390 days

1.2K1 year

Compare downloads with other packages →

Also on26 r2u4 autocran

How downloads are counted

Repository

klintkanopka/mixedsubjectsirt ↗Account: klintkanopka

0Stars

0Forks

0Open issues

0Open PRs

2Releases

29Commits

1Commit identities

data-augmentationpsychometrics

29 commits · Last activity 2026-08-14 · Default branch at 1.1.0, ahead of CRAN 1.0.0 (checked 2026-10-22)

Responsiveness

not measured

Releases over time

2026-06-11 · 12026-07-07 · 2

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/klintkanopka/mixedsubjectsirt on 2026-10-22.

Continuous integration (2)

GitHub ActionsR CMD check workflow

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

Docs source (3)

pkgdown

GitHub Pages site last deployed 2026-09-14 · The repository has a site deploy workflow

pkgdown site last built 2026-08-14

Reproducibility and dev environment (1)

data-raw/

CRAN release process (1)

cran-comments.md

Show all practicesHide 1 practice

Lint, format, editor (1)

RStudio project

How this is detected·Detection ruleset v4 (2026-10-20)

Dependencies

Declared dependencies

6 external dependencies (excludes base and recommended)

Installing mixedsubjectsirt adds 65 packages (32 with compiled code, 27 MB of source tarballs). Heaviest: SimDesign 2.4 MB, Rcpp 1.8 MB, GPArotation 1.6 MB.

Depends (0)

none

Imports (2)

mirt rmutil

LinkingTo (0)

none

Suggests (4)

ggplot2 knitr rmarkdown testthat

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

How these are counted

Code & Tests

People & History

People (1)

Klint Kanopka

Author, Maintainer since 1.0.0 (2026)

Releases (1)

1.0.0Latest
record
2026-06-25 · current release
R
R 4.6.0 released · 2026-04-24

Package metadata

First published: 2026-06-25
Total releases: 1 / 1 yrs
License: MIT + file LICENSE OSI
Download size: 491 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("mixedsubjectsirt")

Format

Kanopka, K. (2026). mixedsubjectsirt: Item Response Theory Calibration with a Mixed Subjects Design (Version 1.0.0) [Computer software]. https://doi.org/10.32614/CRAN.package.mixedsubjectsirt

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 mixedsubjectsirt version 1.0.0 [Data set]. HJJB, LLC. Data release v2026-10-25. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-25, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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