FedIRT: Federated Item Response Theory Models

Integrate Item Response Theory (IRT) and Federated Learning to estimate traditional IRT models, including the 2-Parameter Logistic (2PL) and the Graded Response Models, with enhanced privacy. It allows for the estimation in a distributed manner without compromising accuracy. A user-friendly 'shiny' application is included. For more details, see Biying Zhou, Feng Ji (2024) "'FedIRT': An R package and 'shiny' app for estimating federated item response theory models" <https://github.com/Feng-Ji-Lab/FedIRT/blob/main/paper/paper.pdf>.

Version:	0.1.0
Depends:	R (≥ 3.5.0)
Imports:	purrr, pracma, shiny, httr, callr, DT, ggplot2, shinyjs
Suggests:	testthat (≥ 3.0.0)
Published:	2024-04-10
DOI:	10.32614/CRAN.package.FedIRT
Author:	Biying Zhou [cre], Feng Ji [aut]
Maintainer:	Biying Zhou <zby.zhou at mail.utoronto.ca>
License:	MIT + file LICENSE
NeedsCompilation:	no
Materials:	README
CRAN checks:	FedIRT results

Documentation:

Reference manual:

FedIRT.pdf

Downloads:

Package source:	FedIRT_0.1.0.tar.gz
Windows binaries:	r-devel: FedIRT_0.1.0.zip, r-release: FedIRT_0.1.0.zip, r-oldrel: FedIRT_0.1.0.zip
macOS binaries:	r-release (arm64): FedIRT_0.1.0.tgz, r-oldrel (arm64): FedIRT_0.1.0.tgz, r-release (x86_64): FedIRT_0.1.0.tgz, r-oldrel (x86_64): FedIRT_0.1.0.tgz

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