Packaged: 2015-07-06 11:35:00 UTC; Alberto.Cassese

Author: Cyprien Mbogning and Philippe Broet
Description: Fit a bagging survival tree on a mixture of population (susceptible and nonsusceptible)
using either a pseudo R2 criterion or an adjusted Logrank criterion. The predictor is
evaluated using the Out Of Bag Integrated Brier Score (IBS) and several scores of importance
are computed for variable selection. The thresholds values for variable selection are
computed using a nonparametric permutation test.

See 'Cyprien Mbogning' and 'Philippe Broet' (2016)<doi:10.1186/s12859-016-1090-x> for
an overview about the methods implemented in this package.

Title: An Interface to R2WinBUGS by gWidgets
Description: This package has provided an interface to WinBUGS/OpenBUGS
via R2WinBUGS. Some options will be intelligently guessed, e.g.
the path to WinBUGS/OpenBUGS and the data/parameter list. Users

Title: An Interface to R2WinBUGS/R2jags by gWidgets
Description: This package has provided an interface to
WinBUGS/OpenBUGS/JAGS via R2WinBUGS and R2jags. Some options
will be intelligently guessed, e.g.  the path to
WinBUGS/OpenBUGS/JAGS and the data/parameter list. Users can

Title: An Interface to WinBUGS/OpenBUGS/JAGS by gWidgets

Description: This package has provided an interface to WinBUGS/OpenBUGS/JAGS
via R2WinBUGS and R2jags. Some options will be intelligently guessed, e.g.
the path to WinBUGS/OpenBUGS/JAGS and the data/parameter list. Users can

Title: Model Agnostic Instance Level Variable Attributions
person("Alicja", "Gosiewska", email = "alicjagosiewska@gmail.com", role = c("aut")),
person("Dariusz", "Komosinski", role = c("ctb")))
Supports additive attributions and attributions with interactions.
The Break Down Table shows contributions of every variable to a final prediction.
The Break Down Plot presents variable contributions in a concise graphical way.
This package works for classification and regression models.
It is an extension of the 'breakDown' package (Staniak and Biecek 2018) <doi:10.32614/RJ-2018-072>,
with new and faster strategies for orderings.
It supports interactions in explanations and has interactive visuals (implemented with 'D3.js' library).
The methodology behind is described in the 'iBreakDown' article (Gosiewska and Biecek 2019) <arXiv:1903.11420>
This package is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.
Suggests: knitr, rmarkdown, caret, randomForest, e1071, xgboost,