SWIM: Scenario Weights for Importance Measurement
An efficient sensitivity analysis for stochastic models based on
Monte Carlo samples. Provides weights on simulated scenarios from a
stochastic model, such that stressed random variables fulfil given
probabilistic constraints (e.g. specified values for risk measures),
under the new scenario weights. Scenario weights are selected by
constrained minimisation of the relative entropy to the baseline model.
The 'SWIM' package is based on Pesenti S.M., Millossovich P., Tsanakas A. (2019)
"Reverse Sensitivity Testing: What does it take to break the model"
<openaccess.city.ac.uk/id/eprint/18896/> and Pesenti S.M. (2021)
"Reverse Sensitivity Analysis for Risk Modelling" <https://www.ssrn.com/abstract=3878879>.
| Version: |
1.0.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
Rdpack (≥ 0.7), Hmisc, nleqslv, reshape2, plyr, ggplot2, stats |
| Suggests: |
testthat, mvtnorm, spelling, Weighted.Desc.Stat, knitr, rmarkdown, bookdown, ggpubr |
| Published: |
2022-01-09 |
| DOI: |
10.32614/CRAN.package.SWIM |
| Author: |
Silvana M. Pesenti
 [aut, cre],
Alberto Bettini [aut],
Pietro Millossovich
[aut],
Andreas Tsanakas
[aut],
Zhuomin Mao [ctb],
Kent Wu [ctb] |
| Maintainer: |
Silvana M. Pesenti <swimpackage at gmail.com> |
| BugReports: |
https://github.com/spesenti/SWIM/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/spesenti/SWIM,
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3515274,
https://utstat.toronto.edu/pesenti/?page_id=138 |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Citation: |
SWIM citation info |
| Materials: |
README NEWS |
| CRAN checks: |
SWIM results |
Documentation:
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