GPCMlass

Author: Gunther Schauberger
Contact: https://cran.r-project.org/web/packages/GPCMlasso/index.html
Description: GPCMlasso: Differential Item Functioning in Generalized Partial Credit Models. Provides a framework to detect Differential Item Functioning (DIF) in Generalized Partial Credit Models (GPCM) and special cases of the GPCM as proposed by Schauberger and Mair (2019) <doi:10.3758/s13428-019-01224-2>. A joint model is set up where DIF is explicitly parametrized and penalized likelihood estimation is used for parameter selection. The big advantage of the method called GPCMlasso is that several variables can be treated simultaneously and that both continuous and categorical variables can be used to detect DIF.
Analyses: Item & Test Analysis; Scoring; Simulation/Resampling
System Requirements: Windows, Mac OS, & Linux
Measurement Model: IRT/Rasch
License Type: Open-Source (R)
Documentation: user manual, sample dataset