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", .


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install.packages("SWIM")

0.2.2 by Silvana M. Pesenti, 9 days ago


https://github.com/spesenti/SWIM, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3515274, https://utstat.toronto.edu/pesenti/?page_id=138


Report a bug at https://github.com/spesenti/SWIM/issues


Browse source code at https://github.com/cran/SWIM


Authors: Silvana M. Pesenti [aut, cre] , Alberto Bettini [aut] , Pietro Millossovich [aut] , Andreas Tsanakas [aut]


Documentation:   PDF Manual  


GPL-3 license


Imports Rdpack, Hmisc, nleqslv, reshape2, plyr, ggplot2, stats

Suggests testthat, mvtnorm, spelling, Weighted.Desc.Stat, knitr, rmarkdown, bookdown, ggpubr


See at CRAN