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mfrmr  

Estimation and Diagnostics for Many-Facet Measurement Models
View on CRAN: Click here


Download and install mfrmr package within the R console
Install from CRAN:
install.packages("mfrmr")

Install from Github:
library("remotes")
install_github("cran/mfrmr")

Install by package version:
library("remotes")
install_version("mfrmr", "0.1.4")



Attach the package and use:
library("mfrmr")
Maintained by
Ryuya Komuro
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-03-30
Latest Update: 2026-03-30
Description:
Fits many-facet measurement models and returns diagnostics, reporting helpers, and reproducible analysis bundles using a native R implementation. Supports arbitrary facet counts, rating-scale and partial-credit parameterizations ('Andrich' (1978) <doi:10.1007/BF02293814>; 'Masters' (1982) <doi:10.1007/BF02296272>), marginal maximum likelihood estimation via the EM algorithm ('Bock' and 'Aitkin' (1981) <doi:10.1007/BF02293801>) and joint maximum likelihood estimation, plus tools for anchor review, interaction screening, linking workflows, and publication-ready summaries.
How to cite:
Ryuya Komuro (2026). mfrmr: Estimation and Diagnostics for Many-Facet Measurement Models. R package version 0.1.4, https://cran.r-project.org/web/packages/mfrmr. Accessed 21 Aug. 2026.
Previous versions and publish date:
(2026-08-21 07:47), 0.1.4 (2026-03-30 21:20), 0.1.5 (2026-04-12 08:20), 0.2.0 (2026-05-16 23:40), 0.2.1 (2026-06-12 19:00), 0.2.2 (2026-07-27 08:20)
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Complete documentation for mfrmr
Functions, R codes and Examples using the mfrmr R package
Full mfrmr package functions and examples
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