and Pesenti S.M. (2021) "Reverse Sensitivity Analysis for Risk Modelling" <https://www.ssrn.com/abstract=3878879>.">

SWIM: Scenario Weights for Importance Measurement (original) (raw)

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. PesentiORCID iD [aut, cre], Alberto Bettini [aut], Pietro MillossovichORCID iD [aut], Andreas Tsanakas ORCID iD [aut], Zhuomin Mao [ctb], Kent Wu [ctb]
Maintainer: Silvana M. Pesenti
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

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