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difNLR

DIF and DDF Detection by Non-Linear Regression Models.

Description

The difNLR package provides methods for detecting differential item functioning (DIF) using non-linear regression models. Both uniform and non-uniform DIF effects can be detected when considering a single focal group. Additionally, the method allows for testing differences in guessing or inattention parameters between the reference and focal group. DIF detection is performed using either a likelihood-ratio test, an F-test, or Wald's test of a submodel. The software offers a variety of algorithms for estimating item parameters.

Furthermore, the difNLR package includes methods for detecting differential distractor functioning (DDF) using multinomial log-linear regression model. It also introduces DIF detection approaches for ordinal data via adjacent category logit and cumulative logit regression models.

Installation

The easiest way to get difNLR package is to install it from CRAN:

install.packages("difNLR")

Or you can get the newest development version from GitHub:

# install.packages("devtools")
devtools::install_github("adelahladka/difNLR")

Version

Current version on CRAN is 1.5.1-4. The newest development version available on GitHub is 1.5.1-4.

Reference

To cite difNLR package in publications, please, use:

To cite new estimation approaches provided in the difNLR() function, please, use:

Try online

You can try some functionalities of the difNLR package online using ShinyItemAnalysis application and package and its DIF/Fairness section.

Getting help

In case you find any bug or just need help with the difNLR package, you can leave your message as an issue here or directly contact us at hladka@cs.cas.cz

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Version

Install

install.packages('difNLR')

Monthly Downloads

526

Version

1.5.1-4

License

GPL-3

Maintainer

Adela Hladka

Last Published

June 30th, 2025

Functions in difNLR (1.5.1-4)

coef.ddfMLR

Extract model coefficients from an object of "ddfMLR" class.
coef.difNLR

Extract item parameter estimates from an object of the "difNLR" class.
plot.difNLR

ICC and test statistics plots for an object of the "difNLR" class.
coef.difORD

Extract model coefficients from an object of "difORD" class.
formulaNLR

Creates a formula for non-linear regression DIF models.
genNLR

Generates data set based on generalized logistic regression DIF and DDF models.
plot.difORD

ICC plots for an object of "difORD" class.
predict.ddfMLR

Predicted values for an object of "ddfMLR" class.
ORD

DIF likelihood ratio statistics for ordinal data.
estimNLR

Non-linear regression DIF models estimation.
NLR

DIF statistics for non-linear regression models.
difORD

DIF detection among ordinal data.
ddfMLR

DDF detection for nominal data.
predict.difORD

Predicted values for an object of "difORD" class.
predict.difNLR

Predicted values for an object of the "difNLR" class.
startNLR

Calculates starting values for non-linear regression DIF models.
difNLR-package

DIF and DDF Detection by Non-Linear Regression Models.
fitted.difNLR

Fitted values and residuals for an object of the "difNLR" class.
logLik.ddfMLR

Log-likelihood and information criteria for an object of "ddfMLR" class.
logLik.difNLR

Log-likelihood and information criteria for an object of the "difNLR" class.
plot.ddfMLR

ICC plots for an object of "ddfMLR" class.
logLik.difORD

Log-likelihood and information criteria for an object of "difORD" class.
difNLR

DIF detection using non-linear regression method.
MSATB

Dichotomous dataset of Medical School Admission Test in Biology.
GMAT2test

Dataset based on GMAT.
MSATBkey

Key of correct answers for MSATBtest dataset.
MSATBtest

Dataset of School Admission Test in Biology.
GMAT

Dichotomous dataset based on GMAT with the same total score distribution for groups.
GMATtest

Dataset based on GMAT with the same total score distribution for groups.
GMAT2

Dichotomous dataset based on GMAT.
GMATkey

Key of correct answers for GMATtest dataset.
GMAT2key

Key of correct answers for GMAT2test dataset.
MLR

DDF likelihood ratio statistics based on multinomial log-linear regression model.