Imports: pdp, ggplot2, ALEPlot, breakDown, factorMerger, ggpubr

Suggests: gbm, randomForest, xgboost, testthat, dplyr

DALEX package contains various explainers that help to understand the link between input variables and model output.
The single_prediction() explainer attributes parts of a model prediction to particular variables used in the model.
The variable_dropout() explainer calculates variable importance scores based on variable shuffling.

Authors@R: person("Przemyslaw", "Biecek", email = "przemyslaw.biecek@gmail.com",
Author: Przemyslaw Biecek [aut, cre] (<https://orcid.org/0000-0001-8423-1823>)

It is a wrapper over packages 'pdp' (Greenwell 2017) <doi:10.32614/RJ-2017-016>,
'ALEPlot' (Apley 2018) <arXiv:1612.08468>  and 'factorMerger' (Sitko and Biecek 2017) <arXiv:1709.04412>.
It is a wrapper over 'breakDown' package (Staniak and Biecek 2018) <doi:10.32614/RJ-2018-072>.
The variable_dropout() explainer calculates variable importance scores based on variable shuffling
'DALEX' is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.
Suggests: gbm, randomForest, xgboost, ALEPlot, breakDown, pdp,

Suggests: ALEPlot, breakDown, factorMerger, gbm, ggpubr, iBreakDown,

breakDown, pdp, factorMerger, ggpubr, testthat, dplyr

Package: DALEX
Title: moDel Agnostic Language for Exploration and eXplanation
Version: 1.0.1
Authors@R: c(person("Przemyslaw", "Biecek", email = "przemyslaw.biecek@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-8423-1823")),
person("Szymon", "Maksymiuk", role = "ctb"))
Description: Unverified black box model is the path to the failure. Opaqueness leads to distrust.
Distrust leads to ignoration. Ignoration leads to rejection.
DALEX package xrays any model and helps to explore and explain its behaviour.
Machine Learning (ML) models are widely used and have various applications in classification
or regression. Models created with boosting, bagging, stacking or similar techniques are often
used due to their high performance. But such black-box models usually lack of direct interpretability.
DALEX package contains various methods that help to understand the link between input variables
and model output. Implemented methods help to explore model on the level of a single instance
as well as a level of the whole dataset.
All model explainers are model agnostic and can be compared across different models.
DALEX package is the cornerstone for 'DrWhy.AI' universe of packages for visual model exploration.
Find more details in (Biecek 2018) <arXiv:1806.08915>.
License: GPL
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
Depends: R (>= 3.5)
Imports: ggplot2
Suggests: gower, ingredients, iBreakDown, ggpubr, ranger, testthat
URL: https://ModelOriented.github.io/DALEX/,
https://github.com/ModelOriented/DALEX
BugReports: https://github.com/ModelOriented/DALEX/issues
NeedsCompilation: no
Packaged: 2020-02-25 13:19:28 UTC; p.biecek
Author: Przemyslaw Biecek [aut, cre] (<https://orcid.org/0000-0001-8423-1823>),
Szymon Maksymiuk [ctb]
Maintainer: Przemyslaw Biecek <przemyslaw.biecek@gmail.com>
Repository: CRAN
Date/Publication: 2020-02-26 13:00:15 UTC