An R package for uncertainty quantification and
propagation in the framework of Dempster-Shafer Theory and
imprecise probabilities. This package was developed in the
context of a collaborative research project between EDF R&D
(http//rd.edf.com) and the University of Duisburg-Essen,
Information Logistics (http//www.uni-due.de/il).

R (>= 3.5), AlgDesign, copula, evd, triangle, kolmim
Uncertainty quantification and propagation in the framework of
Dempster-Shafer Theory and imprecise probabilities. This toolbox offers
easy-to-use methods for using imprecise probabities for applied uncertainty
modelling and simulation. The package comprises the basic functionality
needed, with usability similar to standard probabilistic analysis:
- Fit imprecise probability distributions from data,
- Define imprecise probabilities based on distribution functions,
- Combine with various aggregation rules (e. g. Dempster's rule),
- Plotting tools,
- Propagate through arbitrary functions / simulations via Monte Carlo,
- Perform sensitivity analyses with imprecise distributions,
- Example models for a quick start.

Dr. Jean-Benoit Rossel, Prof. Valentin Rousson, Dr. Yves Eggli
Let us consider a sample of patients who can suffer from several diseases simultaneously, in a given set of diseases. The goal of the implemented algorithm is to estimate the individual average cost of each disease, starting from the global health costs available for each patient.

Andrea Peters <Andrea.Peters@imbe.imed.uni-erlangen.de> and
Torsten Hothorn <Torsten.Hothorn@rzmail.uni-erlangen.de>
Improved predictive models by direct and indirect
bootstrap aggregation in classification and regression as well as
resampling based estimators of prediction error.

Improved predictive models by indirect classification and
bagging for classification, regression and survival problems
as well as resampling based estimators of prediction error.
rpart (>= 3.1-8), MASS, mlbench, survival, nnet, class

Improved predictive models by indirect classification and
bagging for classification, regression and survival problems as
well as resampling based estimators of prediction error.