Anatol Sargin <anatol.sargin@math.uni-augsburg.de>,
Ali Uenlue <ali.uenlue@math.uni-augsburg.de>
Functions and example datasets for the psychometric
theory of knowledge spaces.  This package implements data
analysis methods and procedures for simulating data and
transforming different formulations in knowledge space
theory.

Anatol Sargin <anatol.sargin@math.uni-augsburg.de>, Ali Uenlue
<ali.uenlue@math.uni-augsburg.de>
Anatol Sargin <anatol.sargin@math.uni-augsburg.de>, Ali
Uenlue <ali.uenlue@math.uni-augsburg.de>
Functions and example datasets for the psychometric theory
of knowledge spaces.  This package implements data analysis
methods and procedures for simulating data and transforming
different formulations in knowledge space theory.

Anatol Sargin <sargin@statistik.tu-dortmund.de>, Ali Uenlue
<uenlue@statistik.tu-dortmund.de>
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.

c(person("Ali", "Uenlue", email = "ali.uenlue@tum.de", role
= c("aut", "cre")), person("Anatol", "Sargin", email =
"anatol.sargin@generali.de", role = c("aut")))
Ali Uenlue [aut, cre], Anatol Sargin [aut]

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.
c(person("Ali", "Uenlue", role = c("aut", "cre"),
email = "ali.uenlue@tum.de"),
person("Anatol", "Sargin", role = c("aut"),
email = "anatol.sargin@generali.de"))

person("Przemyslaw", "Biecek", email = "przemyslaw.biecek@gmail.com", role = c("aut", "cre"))
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 interpretability.
'DALEX' package contains various explainers that help to understand the link between input variables and model output.
The single_variable() explainer extracts conditional response of a model as a function of a single selected variable.
It is a wrapper over packages 'pdp' and 'ALEPlot'.
The single_prediction() explainer attributes arts of model prediction to articular variables used in the model.
It is a wrapper over 'breakDown' package.
The variable_dropout() explainer assess variable importance based on consecutive permutations.
All these explainers can be plotted with generic plot() function and compared across different models.