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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 |
| 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 |
| Reference manual: | FedIRT.pdf |
| 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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These binaries (installable software) and packages are in development.
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