Suggests: covr, knitr, lme4, mvtnorm, rmarkdown, roxygen2, spelling,

Depends: R (>= 2.7.0), AlgDesign, copula, evd, triangle
Description: 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).
Packaged: Fri Nov  7 15:26:31 2008; hornik

Description: 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,

Depends: R (>= 3.5), AlgDesign, copula, evd, triangle, kolmim
Description: 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),
- Propagate through arbitrary functions / simulations via Monte Carlo,
- Perform sensitivity analyses with imprecise distributions,

Title: Iterative Proportional Repartition Algorithm
Author: Dr. Jean-Benoit Rossel, Prof. Valentin Rousson, Dr. Yves Eggli
Description: 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.

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