mfrmr: Estimation and Diagnostics for Many-Facet Measurement Models

Estimates many-facet ordered-response measurement models with arbitrary facet counts, rating-scale and partial-credit parameterizations, a generalized partial-credit extension with discrimination varying across levels of one selected facet. Marginal maximum likelihood permits a different facet to own category steps; joint maximum likelihood requires the same facet for both roles. Conditional maximum likelihood fits many-facet rating-scale and partial-credit models by conditioning on person totals, with structural contrast intervals and no assumed ability distribution. Experimental two-family estimation and conditional scoring retain explicit uncertainty limitations. The fit, diagnose and report workflow covers anchoring, linking, bias and differential-functioning screening, with saved results for replay. Observed-score generalizability and decision studies support assessment planning under declared rating designs. Agreement, rater feedback and conditional classification summaries connect analysis to assessment decisions. See 'Andrich' (1978) <doi:10.1007/BF02293814>, 'Masters' (1982) <doi:10.1007/BF02296272>, and 'Muraki' (1992) <doi:10.1177/014662169201600206> for the underlying ordered-response models.

Version:	0.2.4
Depends:	R (≥ 4.1)
Imports:	cli, dplyr, Matrix, grDevices, graphics, tidyr, tibble, purrr, stringr, psych, lifecycle, rlang, methods, parallel, stats, tools, utils
LinkingTo:	cpp11 (≥ 0.5.4)
Suggests:	RTMB (≥ 2.0), cluster, mice, nleqslv, testthat (≥ 3.0.0), callr, pkgload, withr, covr, knitr, rmarkdown, igraph, ergm (≥ 4.0), network, lme4, minqa, nloptr, numDeriv, ordinal, kableExtra, flextable, future, future.apply, shiny, ggplot2 (≥ 3.4.0), mirt, TAM, eRm, lpSolve
Published:	2026-10-30
DOI:	10.32614/CRAN.package.mfrmr
Author:	Ryuya Komuro [aut, cre, cph]
Maintainer:	Ryuya Komuro <ryuya.komuro.c4 at tohoku.ac.jp>
BugReports:	https://github.com/Ryuya-dot-com/mfrmr/issues
License:	MIT + file LICENSE
URL:	https://ryuya-dot-com.github.io/mfrmr/, https://github.com/Ryuya-dot-com/mfrmr
NeedsCompilation:	yes
Language:	en
Citation:	mfrmr citation info
Materials:	README, NEWS
CRAN checks:	mfrmr results

Documentation:

Reference manual:

mfrmr.html , mfrmr.pdf

Vignettes:

Exploring person, rater, and task attributes (source, R code) Uncertainty in rater estimates and differences (source, R code) Migrating from FACETS to mfrmr (source, R code) GPCM scope and current limitations (source, R code) Plan an assessment with G theory (source, R code) mfrmr Linking and DFF (source, R code) MML estimation and marginal-fit diagnostics (source, R code) Choose a model and estimator for your assessment (source) Connect rating designs, network diagnostics and reports (source, R code) Portable calibration and fresh-session scoring (source, R code) Shared random raters: feedback and replacement raters (source, R code) Rating agreement and score comparisons (source, R code) mfrmr Reporting and APA (source, R code) Missing scores on assigned ratings (source, R code) How often does a rater screen give the right warning? (source, R code) Tasks, rubric criteria and testlet dependence (source, R code) Dependent ratings within a person: testlet models (source, R code) mfrmr Visual Diagnostics (source, R code) mfrmr Workflow (source, R code)

Downloads:

Package source:	mfrmr_0.2.4.tar.gz
Windows binaries:	r-devel: mfrmr_0.2.3.1.zip, r-release: mfrmr_0.2.3.1.zip, r-oldrel: mfrmr_0.2.3.1.zip
macOS binaries:	r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): mfrmr_0.2.3.1.tgz, r-oldrel (x86_64): not available
Old sources:	mfrmr archive

Linking:

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