Detection of differential item functioning (DIF) among dichotomously scored items and differential distractor functioning (DDF) among unscored items with non-linear regression  procedures based on generalized logistic regression models (Hladka & Martinkova, 2020, <doi:10.32614/RJ-2020-014>).

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>.

Methods to detect differential item functioning (DIF) in dichotomous, polytomous, 
    and continuous items, using both classical and modern approaches. These include
    Mantel-Haenszel procedures, logistic regression (including ordinal models), and
    regularization-based methods such as LASSO. Uniform and non-uniform DIF effects
    can be detected, and some methods support multiple focal groups. The package
    also provides tools for anchor purification, rest score matching, effect size
    estimation, and DIF simulation. See Magis, Beland, Tuerlinckx, and De Boeck
    (2010, Behavior Research Methods, 42, 847–862, <doi:10.3758/BRM.42.3.847>) for
    a general overview.

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.

Item focussed recursive partitioning for simultaneous selection of items and variables that induce Differential Item Functioning (DIF) in dichotomous or polytomous items.

Implementation of a function 'digest()' for the creation of hash
 digests of arbitrary R objects (using the 'md5', 'sha-1', 'sha-256', 'crc32',
 'xxhash', 'murmurhash', 'spookyhash', 'blake3', 'crc32c', 'xxh3_64', and 'xxh3_128'
 algorithms) permitting easy comparison of R language objects, as well as functions
 such as 'hmac()' to create hash-based message authentication code. Please note that
 this package is not meant to be deployed for cryptographic purposes for which more
 comprehensive (and widely tested) libraries such as 'OpenSSL' should be used.