Some traditional methods to detect dichotomous differential item
functionning (DIF) in psychometrics.
Sebastien Beland (UQAM), David Magis (KU Leuven) and Gilles
Raiche (UQAM)
The difR package contains several traditional methods to
detect DIF in dichotomously scored items. Both uniform and
non-uniform DIF effects can be detected, with methods relying
upon item response models or not. Some methods deal with more
than one focal group.

Some traditional methods to detect dichotomous differential item
functioning (DIF) in psychometrics

Collection of methods to detect dichotomous differential item
functioning (DIF) in psychometrics
Sebastien Beland (UQAM), David Magis (U Liège) and Gilles
Raiche (UQAM)

Sebastien Beland (UQAM), David Magis (U Liege) and Gilles
Raiche (UQAM)

David Magis (U Liege), Sebastien Beland (UQAM) and Gilles
Raiche (UQAM)

Collection of Methods to Detect Dichotomous Differential Item
Functioning (DIF)
David Magis (U Liege), Sebastien Beland (UQAM) and Gilles Raiche (UQAM)
Collection of traditional methods to detect differential item functioning among dichotomously scored items.

Provides a collection of standard methods to detect differential item functioning among dichotomously scored items. Methods for uniform and non-uniform DIF, based on test-score or IRT methods, for comparing two or more than two groups of respondents, are available (Magis, Beland, Tuerlinckx and De Boeck,A General Framework and an R Package for the Detection of Dichotomous Differential Item Functioning, Behavior Research Methods, 42, 2010, 847-862 <doi:10.3758/BRM.42.3.847>).
Versions prior to 4.7 had 'License_restricts_use: yes'.

David Magis (U Liege), Sebastien Beland (U Montreal) and Gilles Raiche (UQAM)

c(person("Elpidio-Emmanuel", "Gonzalez-Valbuena", role = c("aut")),
person("Victor", "Trevino", role = c("aut", "cre"),email = "vtrevino@itesm.mx"))
stats, utils, stringr, data.table, mvtnorm, graphics,
grDevices
Estimation of DIFferential COexpressed NETworks using diverse and user metrics.
This package is basically used for three functions related to the estimation
of differential coexpression.
First, to estimate differential coexpression where
the coexpression is estimated, by default, by Spearman correlation. For this,
a metric to compare two correlation distributions is needed. The package includes
6 metrics. Some of them needs a threshold. A new metric can also be specified as
a user function with specific parameters (see difconet.run). The significance is
be estimated by permutations.
Second, to generate datasets with controlled differential correlation data. This
is done by either adding noise, or adding specific correlation structure.
Third, to show the results of differential correlation analyses. Please see
<http://bioinformatica.mty.itesm.mx/difconet> for further information.
Elpidio-Emmanuel Gonzalez-Valbuena [aut],
Victor Trevino [aut, cre]