Collate: 'helper.R' 'methods.R' 'zzz.R' 'cache.R' 'fail.R' 'worker.R'

Description: More comfortable interface to work with a directory of R data or

Title: File Abstraction Interface Layer (FAIL)
Description: More comfortable interface to work with R data or source files
Imports: stats, utils, BBmisc, checkmate

Title: Fair Data Adaptation with Quantile Preservation
Description: 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

Description: An implementation of the fair data adaptation with quantile
preservation described in Plecko & Meinshausen (2019) <arXiv:1911.06685>.
Imports: ranger (>= 0.13.1), assertthat, quantreg, qrnn, igraph,
Suggests: testthat (>= 2.1.0), knitr, rmarkdown, markdown, data.table,

Suggests: testthat (>= 3.0.3), knitr, rmarkdown, markdown, data.table,
rticles, mvtnorm, magick, ggraph, pdftools

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

Description: 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.

Imports: methods, optiSolve, CVXR, glmnet
(2019) <https://www.jmlr.org/papers/volume20/18-262/18-262.pdf> and my own
approach that uses ridge regression to enforce fairness.

Title: Flexible Tool for Bias Detection, Visualization, and Mitigation
c(person("Jakub", "Wiśniewski", role = c("aut", "cre"),
person("Przemysław", "Biecek", role = c("aut"),
Description: Measure fairness metrics in one place for many models. Check how big is model's bias towards different races, sex, nationalities etc. Use measures such as Statistical Parity, Equal odds to detect the discrimination against unprivileged groups. Visualize the bias using heatmap, radar plot, biplot, bar chart (and more!). There are various preprocessing and post-processing bias mitigation algorithms implemented.
Imports: DALEX, ggplot2, patchwork, ggdendro, ggrepel, scales
Suggests: ranger, gbm, knitr, rmarkdown, covr, testthat, spelling