Package: FedIRT 1.1.0

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.

Authors:Biying Zhou [cre], Feng Ji [aut]

FedIRT_1.1.0.tar.gz
FedIRT_1.1.0.zip(r-4.5)FedIRT_1.1.0.zip(r-4.4)FedIRT_1.1.0.zip(r-4.3)
FedIRT_1.1.0.tgz(r-4.4-any)FedIRT_1.1.0.tgz(r-4.3-any)
FedIRT_1.1.0.tar.gz(r-4.5-noble)FedIRT_1.1.0.tar.gz(r-4.4-noble)
FedIRT_1.1.0.tgz(r-4.4-emscripten)FedIRT_1.1.0.tgz(r-4.3-emscripten)
FedIRT.pdf |FedIRT.html✨
FedIRT/json (API)

# Install 'FedIRT' in R:

install.packages('FedIRT', repos = c('https://biyingzhou.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

example_data_2PL - Binary Response Dataset for Federated 2PL Model
example_data_2PL_1 - Binary Response Dataset for Federated 2PL Model
example_data_2PL_2 - Binary Response Dataset for Federated 2PL Model
example_data_graded - Graded Response Dataset for Federated Graded Model
example_data_graded_and_binary - Graded Response Dataset for Federated Graded Model

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.30 score 10 scripts 253 downloads 16 exports 73 dependencies

Last updated 1 months agofrom:38abff33cc. Checks:OK: 7. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Oct 29 2024
R-4.5-win	OK	Oct 29 2024
R-4.5-linux	OK	Oct 29 2024
R-4.4-win	OK	Oct 29 2024
R-4.4-mac	OK	Oct 29 2024
R-4.3-win	OK	Oct 29 2024
R-4.3-mac	OK	Oct 29 2024

Exports:fedirt fedirt_2PL fedirt_file fedirt_gpcm g_logL g_logL_entry g_logL_gpcm logL logL_entry logL_gpcm mem personfit personscore runclient runserver SE

Dependencies:askpass base64enc bslib cachem callr cli colorspace commonmark crayon crosstalk curl digest DT evaluate fansi farver fastmap fontawesome fs ggplot2 glue gtable highr htmltools htmlwidgets httpuv httr isoband jquerylib jsonlite knitr labeling later lattice lazyeval lifecycle magrittr MASS Matrix memoise mgcv mime munsell nlme openssl pillar pkgconfig pracma processx promises ps purrr R6 rappdirs RColorBrewer Rcpp rlang rmarkdown sass scales shiny shinyjs sourcetools sys tibble tinytex utf8 vctrs viridisLite withr xfun xtable yaml

Citation

Readme and manuals

Help Manual

Help page	Topics
Binary Response Dataset for Federated 2PL Model	example_data_2PL
Binary Response Dataset for Federated 2PL Model	example_data_2PL_1
Binary Response Dataset for Federated 2PL Model	example_data_2PL_2
Graded Response Dataset for Federated Graded Model	example_data_graded
Graded Response Dataset for Federated Graded Model	example_data_graded_and_binary
Federated IRT model	fedirt
Federated 2PL model	fedirt_2PL
Federated IRT model	fedirt_file
Federated Graded Response Model Estimation Function	fedirt_gpcm
Gradient of Log-Likelihood for the federated 2PL Model	g_logL
Aggregated Gradient of Log-Likelihood for Federated Learning	g_logL_entry
Gradient of Log-Likelihood for the federated graded Model	g_logL_gpcm
Log-Likelihood of the federated 2PL Model	logL
Aggregate Log-Likelihood Function for Federated Learning	logL_entry
Log-Likelihood of the federated graded Model	logL_gpcm
Memoization Function for Speed Optimization	mem
Federated IRT person fit	personfit
Federated IRT person score	personscore
Client for Federated IRT Model Estimation	runclient
Server for Federated IRT Model Estimation	runserver
Federated IRT SE	SE

Package: FedIRT 1.1.0

FedIRT: Federated Item Response Theory Models

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)