Functions and an example dataset for the psychometric theory of
  knowledge spaces.  This package implements data analysis methods and
  procedures for simulating data and quasi orders and transforming different
  formulations in knowledge space theory.  See package?DAKS for an overview.

Any unverified black box model is the path to 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 direct interpretability.
  DALEX package contains various methods that help to understand the link between input variables 
  and model output. Implemented methods help to explore the 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>.

Provides wrapper of various machine learning models. 
  In applied machine learning, there 
  is a strong belief that we need to strike a balance 
  between interpretability and accuracy. 
  However, in field of the interpretable machine learning, 
  there are more and more new ideas for explaining black-box models, 
  that are implemented in 'R'. 
  'DALEXtra' creates 'DALEX' Biecek (2018) <arXiv:1806.08915> explainer for many type of models
  including those created using 'python' 'scikit-learn' and 'keras' libraries, and 'java' 'h2o' library. 
  Important part of the package is Champion-Challenger analysis and innovative approach
  to model performance across subsets of test data presented in Funnel Plot.