partykit, doParallel, data.table, foreach, iterators,
parallel, stats

Salary Analysis by the Swiss Federal Office for Gender Equality
c(
person(given = "Jonathan", family = "Chassot", email = "logib@ebg.admin.ch",
role = c("aut", "cre")),
person(given = "Jeremy", family = "Kolly", email = "logib@ebg.admin.ch",
role = "ctb"),
person(given = "Federal Office for Gender Equality of Switzerland",
email = "ebg@ebg.admin.ch", role = c("cph", "fnd"))
)
Implementation of the Swiss Confederation's standard analysis model for salary analyses <https://www.ebg.admin.ch/dam/ebg/en/dokumente/lohngleichheit/infos-zu-analysen/standard-analysemodellzurueberpruefungderlohngleichheitzwischenf.pdf.download.pdf/methodological_approachformonitoringcompliancewithwageequalitybe.pdf> in R. The analysis is run at company-level and the model is intended for companies with 50 or more employees (apprentices, trainees/interns and expats are not included in the analysis). Employees with at least 100 employees are required by the Gender Equality Act to conduct an equal pay analysis.
This package allows users to run the equal salary analysis in R, providing additional transparency with respect to the methodology and simple automation possibilities.
Jonathan Chassot [aut, cre],
Jeremy Kolly [ctb],
Federal Office for Gender Equality of Switzerland [cph, fnd]

c(
person(given = "Jonathan", family = "Chassot", email = "jonathanlaurentdominique.chassot@ebg.admin.ch",
role = c("aut", "cre")),
person(given = "Jeremy", family = "Kolly", email = "logib@ebg.admin.ch",
role = "ctb"),
person(given = "Federal Office for Gender Equality of Switzerland",
email = "ebg@ebg.admin.ch", role = c("cph", "fnd"))
)

Identifying Interactions Between Binary Predictors
person("Michael", "Lau", email = "michael.lau@hhu.de",
role = c("aut", "cre"))
A global statistical learning method
which tries to find the best set of predictors and interactions
between predictors for modeling binary or quantitative response data.
Several search algorithms and ensembling techniques are implemented
allowing for finetuning the method to the specific problem.
Interactions with single quantitative covariables can be properly
taken into account by also splitting after those or by fitting
local four parameter logistic models.