dplyr, ggplot2, ggtree (>= 3.0.0), tidytree, tidyr, purrr, ape,
Description: Simulates and computes the (maximum) likelihood of a dynamical
model of island biota assembly through speciation, immigration and
extinction. See e.g. Valente et al. 2015. Ecology Letters 18: 844-852,

Title: Data Analysis and Knowledge Spaces
Description: 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
Packaged: Thu Feb 26 14:42:35 2009; uenlueal

Author: Anatol Sargin <anatol.sargin@math.uni-augsburg.de>, Ali Uenlue
Maintainer: Anatol Sargin <anatol.sargin@math.uni-augsburg.de>, Ali
Description: 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.
Packaged: 2009-09-02 09:43:57 UTC; anatolsargin

Author: Anatol Sargin <sargin@statistik.tu-dortmund.de>, Ali Uenlue
Maintainer: Anatol Sargin <sargin@statistik.tu-dortmund.de>, Ali Uenlue
Description: Functions and an example dataset for the psychometric
analysis methods and procedures for simulating data and quasi
orders and transforming different formulations in knowledge

Authors@R: c(person("Ali", "Uenlue", email = "ali.uenlue@tum.de", role
= c("aut", "cre")), person("Anatol", "Sargin", email =
Author: Ali Uenlue [aut, cre], Anatol Sargin [aut]

Description: 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.
Authors@R: c(person("Ali", "Uenlue", role = c("aut", "cre"),
person("Anatol", "Sargin", role = c("aut"),

Title: Descriptive mAchine Learning EXplanations
Authors@R: person("Przemyslaw", "Biecek", email = "przemyslaw.biecek@gmail.com", role = c("aut", "cre"))
Description: 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.
Imports: pdp, ggplot2, ALEPlot, breakDown