machine learning algorithms.  Data sets are automatically added to the
dictionary of tasks if 'mlr3' is loaded.

Description: A small collection of interesting and educational machine
learning data sets which are used as examples in the 'mlr3' book
(<https://mlr3book.mlr-org.com>), the use case gallery

Description: Extends the 'mlr3' package with a backend to
transparently work with data bases. Internally relies on the
abstraction of package 'dbplyr' to interact with one of the many
supported data base management systems (DBMS).
Imports: checkmate, data.table, digest, dplyr, mlr3 (>= 0.1.1), R6
Suggests: DBI, dbplyr, lgr, RSQLite, testthat, tibble

Imports: checkmate, data.table, digest, dplyr, mlr3 (>= 0.1.2), R6

Imports: checkmate, data.table, digest, dplyr, mlr3 (>= 0.1.4), R6

Imports: backports, checkmate, data.table, digest, dplyr, mlr3 (>=
Suggests: DBI, RSQLite, dbplyr, future, future.apply, future.callr,

transparently work with databases. Includes two extra backends:
One relies on relies on the abstraction of package 'dbplyr' to interact with
one of the many supported database management systems (DBMS). The other one
Imports: R6, backports, checkmate, data.table, digest, mlr3misc,
Suggests: DBI, RSQLite, dbplyr, dplyr, duckdb, future, future.apply,
future.callr, lgr, testthat (>= 3.0.0), tibble

Imports: R6, backports, checkmate, data.table, mlr3misc (>= 0.9.3),
Suggests: DBI, RSQLite, dbplyr, dplyr, duckdb (>= 0.3.1), future,
future.apply, future.callr, lgr, testthat (>= 3.0.0), tibble

transparently work with databases such as 'SQLite', 'DuckDB', 'MySQL',
'MariaDB', or 'PostgreSQL'. The package provides two additional backends:
'DataBackendDplyr' relies on the abstraction of package 'dbplyr' to
interact with most DBMS. 'DataBackendDuckDB' operates on 'DuckDB' data bases
Imports: R6, backports, checkmate, data.table, mlr3misc (>= 0.10.0)
Suggests: DBI, RSQLite, dbplyr, dplyr, duckdb (>= 0.4.0), future,

Title: Fairness Auditing and Debiasing for 'mlr3'
Integrates fairness auditing and bias mitigation methods for the 'mlr3' ecosystem.
This includes fairness metrics, reporting tools, visualizations and bias mitigation techniques such as
"Reweighing" described in 'Kamiran, Calders' (2012) <doi:10.1007/s10115-011-0463-8>  and
"Equalized Odds" described in 'Hardt et al.' (2016) <https://papers.nips.cc/paper/2016/file/9d2682367c3935defcb1f9e247a97c0d-Paper.pdf>.
Integration with 'mlr3' allows for auditing of ML models as well as convenient joint tuning of
machine learning algorithms and debiasing methods.
Imports: checkmate, R6 (>= 2.4.1), data.table (>= 1.13.6), paradox,
Suggests: mlr3viz, rmarkdown, knitr, rpart, testthat (>= 3.0.0),
patchwork, ranger, mlr3learners, linprog, posterdown,