More comfortable interface to work with R data or source files
in a key-value fashion.

Fair Data Adaptation with Quantile Preservation
An implementation of the fair data adaptation with quantile preservation described in
Plecko & Meinshausen (2019) <arXiv:1911.06685>.
The adaptation procedure uses the specified causal graph to pre-process the
given training and testing data in such a way to remove the bias caused by
the protected attribute. The procedure uses tree ensembles for quantile
regression.

An implementation of the fair data adaptation with quantile
preservation described in Plecko & Meinshausen (2019) <arXiv:1911.06685>.
The adaptation procedure uses the specified causal graph to pre-process the
given training and testing data in such a way to remove the bias caused by
the protected attribute. The procedure uses tree ensembles for quantile
regression.
c(
person(given = "Drago",
family = "Plecko",
role = c("aut", "cre"),
email = "drago.plecko@stat.math.ethz.ch")
)
ranger (>= 0.13.1), assertthat, quantreg, qrnn, igraph,
ggplot2, cowplot, scales
testthat (>= 2.1.0), knitr, rmarkdown, markdown, data.table,
rticles, mvtnorm

c(
person(given = "Drago",
family = "Plecko",
role = c("aut", "cre"),
email = "drago.plecko@stat.math.ethz.ch"),
person(given = "Nicolas",
family = "Bennett",
role = "aut",
email = "nicolas.bennett@stat.math.ethz.ch")
)
testthat (>= 3.0.3), knitr, rmarkdown, markdown, data.table,
rticles, mvtnorm, magick, ggraph, pdftools
Drago Plecko [aut, cre],
Nicolas Bennett [aut]

testthat (>= 3.0.3), knitr, rmarkdown, rticles, mvtnorm,
magick, ggraph, pdftools, microbenchmark, xtable

Fair machine learning regression models which take sensitive attributes into account in
model estimation. Currently implementing Komiyama et al. (2018)
<http://proceedings.mlr.press/v80/komiyama18a/komiyama18a.pdf>.

Fair machine learning regression models which take sensitive attributes into account in
model estimation. Currently implementing Komiyama et al. (2018)
<http://proceedings.mlr.press/v80/komiyama18a/komiyama18a.pdf> and an improvement over the former
that uses ridge regression to enforce fairness.