Likelihood-based genome polarisation finds which alleles of genomic markers
belong to which side of the barrier.
Co-estimates which individuals belong to either side of the barrier and barrier strength.
Uses expectation maximisation in likelihood framework. The method is described in
Baird et al. (2022) <doi:10.1111/2041-210X.14010>.

Estimates fractional trophic level from quantitative and qualitative diet data and calculates electivity indices in R. Froese & Pauly (2000, ISBN: 9718709991).

Estimates fractional trophic level from quantitative and qualitative diet data and calculates electivity indices in R. Borstein (2020) <doi:10.1007/s10750-020-04417-5>.

R Interface to the ZEIT ONLINE Content API
A wrapper for the ZEIT ONLINE Content API, available at <http://developer.zeit.de>. 'diezeit' gives access to articles and corresponding metadata from the ZEIT archive and from ZEIT ONLINE. A personal API key is required for usage.
brew, grDevices, httr, jsonlite, methods, utils

Detection of Dichotomous Differential Item Functioning (DIF) by
Non-Linear Regression Function
Adela Drabinova (CUNI, ICS CAS), Patricia Martinkova (ICS CAS) and Karel Zvara (CUNI)
R (>= 3.2.2), ggplot2, gridExtra, methods, stats
Detection of differential item functioning among dichotomously scored items with non-linear regression procedure.

Detection of Dichotomous Differential Item Functioning (DIF) by
Non- Linear Regression Function
Adela Drabinova [aut, cre], Patricia Martinkova [aut], Karel Zvara [aut]
Detection of differential item functioning among dichotomously
scored items with non-linear regression procedure.

Detection of Dichotomous Differential Item Functioning (DIF) and
Differential Distractor Functioning (DDF) by Non-Linear
Regression Models
R (>= 3.2.2), CTT, ggplot2, methods, nnet, reshape2, stats
Detection of DIF among dichotomously scored items and DDF among unscored items with non-linear regression procedures based on generalized logistic regression models.

Detection of Differential Item Functioning (DIF) and
Differential Distractor Functioning (DDF) by Non-Linear
Regression Models
c(person("Adela", "Drabinova", role = c("aut", "cre"), email = "adela.drabinova@gmail.com"), person("Patricia", "Martinkova", role = "aut"), person("Karel", "Zvara", role = "aut"))
R (>= 3.2.2), CTT, ggplot2 (>= 2.2.1), methods, nnet,
reshape2, stats