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.

Bayesian factor models are effective tools for dimension reduction. This is especially applicable to multivariate large-scale datasets. It allows researchers to understand the latent factors of the data which are the linear or non-linear combination of the variables. Dynamic Intrinsic Conditional Autocorrelative Priors (ICAR) Spatiotemporal Factor Models 'DIFM' package provides function to run Markov Chain Monte Carlo (MCMC), evaluation methods and visual plots from Shin and Ferreira (2023)<doi:10.1016/j.spasta.2023.100763>. Our method is a class of Bayesian factor model which can account for spatial and temporal correlations. By incorporating these correlations, the model can capture specific behaviors and provide predictions.

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.