Performs detection of Differential Item Functioning using the method DIFboost as proposed by Schauberger and Tutz (2016) <doi:10.1111/bmsp.12060>.

A penalty approach to Differential Item Functioning in Rasch
Models
A package to perform DIFlasso, a method to detect DIF (Differential Item Functioning) in Rasch Models. It can handle settings with many covariates and also metric covariates.

A Penalty Approach to Differential Item Functioning in Rasch
Models
Performs DIFlasso, a method to detect DIF (Differential Item Functioning) in Rasch Models. It can handle settings with many variables and also metric variables.

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.

Multilevel Mantel-Haenszel Statistics for Differential Item
Functioning Detection
c(person("Shenghai", "Dai", role = c("aut", "cre"),
email = "s.dai@wsu.edu"),
person("Brian F.", "French", role = "aut"),
person("W. Holmes", "Finch", role = "aut"),
person("Andrew", "Iverson", role = "aut"))
Shenghai Dai [aut, cre],
Brian F. French [aut],
W. Holmes Finch [aut],
Andrew Iverson [aut]
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>,
and French & Finch (2013) <doi:10.1177/0013164412472341>.

Differential Item Functioning via Shiny Application
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.
shiny, shinydashboard, difR, stats, readxl