Title: A Penalty Approach to Differential Item Functioning in Rasch
Description: Performs DIFlasso, a method to detect DIF (Differential Item Functioning) in Rasch Models. It can handle settings with many variables and also metric variables.
Packaged: 2016-07-21 13:38:23 UTC; schaubergerg

Packaged: 2017-05-09 13:21:34 UTC; schaubergerg

Description: Performs DIFlasso as proposed by Tutz and Schauberger (2015) <doi:10.1007/s11336-013-9377-6>, a method to detect DIF (Differential Item Functioning) in Rasch Models. It can handle settings with many variables and also metric variables.

Packaged: 2013-08-23 14:30:11 UTC; schaubergerg

Title: Multilevel Mantel-Haenszel Statistics for Differential Item
Authors@R: c(person("Shenghai", "Dai", role = c("aut", "cre"),
person("Brian F.", "French", role = "aut"),
person("W. Holmes", "Finch", role = "aut"),
person("Andrew", "Iverson", role = "aut"))
Description: Clustered or multilevel data structures are common in the assessment of differential item functioning (DIF), particularly in the context of large-scale assessment programs. This package allows users to implement extensions of the Mantel-Haenszel DIF detection procedures in the presence of multilevel data based on the work of Begg (1999) <doi:10.1111/j.0006-341X.1999.00302.x>, Begg & Paykin (2001) <doi:10.1080/00949650108812115>,

Title: Differential Item Functioning via Shiny Application
Description: Differential Item Functioning (DIF) Analysis with shiny application interfaces. You can run the functions in this package without any arguments and perform your DIF analysis using user-friendly interfaces.
Imports: shiny, shinydashboard, difR, stats, readxl

Title: Item Focused Trees for the Identification of Items in
Description: Item focused recursive partitioning for simultaneous selection of
items and variables that induce Differential Item Functioning (DIF) in Item Response models.

items and variables that induce Differential Item Functioning (DIF) based on the
Rasch Model or the Logistic Regression Approach for DIF detection.