Authors@R: c(person("Przemyslaw", "Biecek", email = "przemyslaw.biecek@gmail.com", role = c("aut", "cre"),
person("Szymon", "Maksymiuk", role = "aut",
Imports: ggplot2, iBreakDown (>= 1.3.1), ingredients (>= 2.0)

Description: Any unverified black box model is the path to failure. Opaqueness leads to distrust.
used due to their high performance. But such black-box models usually lack direct interpretability.
and model output. Implemented methods help to explore the model on the level of a single instance

Suggests: gower, ranger, yardstick, testthat, methods

Description: Machine Learning models are widely used and have various applications in classification or regression tasks.
Due to increasing computational power, availability of new data sources and new methods, ML models are more and more complex.
Models created with techniques like boosting, bagging of neural networks are true black boxes.
It is hard to trace the link between input variables and model outcomes.
They are used because of high performance, but lack of interpretability is one of their weakest sides.
In many applications we need to know, understand or prove how input variables are used in the model and what impact do they have on final model prediction.
DALEX2 is a collection of tools that help to understand how complex predictive models are working.
DALEX2 is a part of DrWhy universe for tools for Explanation, Exploration and Visualisation for Predictive Models.
Suggests: knitr, randomForest, tibble, testthat

Description: Provides wrapper of various machine learning models.
is a strong belief that we need to strike a balance
However, in field of the interpretable machine learning,
there are more and more new ideas for explaining black-box models,
'DALEXtra' creates 'DALEX' Biecek (2018) <arXiv:1806.08915> explainer for many type of models
including those created using 'python' 'scikit-learn' and 'keras' libraries.
Suggests: gbm, h2o, mljar, mlr, testthat

Suggests: auditor, gbm, ggrepel, h2o, mljar, mlr, randomForest,

person("Katarzyna", "Pekala", role = "aut"),
including those created using 'python' 'scikit-learn' and 'keras' libraries, 'java' 'h2o' library and
'mljar' API. Important part of the package is Champion-Challenger analysis and innovative approach
to model performance across subsets of test data presented in Funnel Plot.
Third branch of 'DALEXtra' package is aspect importance analysis
that provides instance-level explanations for the groups of explanatory variables.
Imports: reticulate, ggplot2, glmnet, ggdendro, gridExtra
Suggests: auditor, ingredients, gbm, ggrepel, h2o, mljar, mlr, mlr3,
randomForest, rmarkdown, rpart, xgboost, testthat