Quantitative Support of Decision Making under Uncertainty

Supporting the quantitative analysis of binary welfare based decision making processes using Monte Carlo simulations. Decision support is given on two levels: (i) The actual decision level is to choose between two alternatives under probabilistic uncertainty. This package calculates the optimal decision based on maximizing expected welfare. (ii) The meta decision level is to allocate resources to reduce the uncertainty in the underlying decision problem, i.e to increase the current information to improve the actual decision making process. This problem is dealt with using the Value of Information Analysis. The Expected Value of Information for arbitrary prospective estimates can be calculated as well as Individual Expected Value of Perfect Information. The probabilistic calculations are done via Monte Carlo simulations. This Monte Carlo functionality can be used on its own.


Reference manual

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1.106 by Eike Luedeling, 2 months ago


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

Authors: Eike Luedeling [cre, aut] (University of Bonn) , Lutz Goehring [aut] (ICRAF and Lutz Goehring Consulting) , Katja Schiffers [aut] (University of Bonn) , Cory Whitney [aut] (University of Bonn) , Eduardo Fernandez [aut] (University of Bonn)

Documentation:   PDF Manual  

GPL-3 license

Imports assertthat, chillR, dplyr, fANCOVA, ggplot2, ggstance, grDevices, magrittr, msm, mvtnorm, nleqslv, patchwork, rriskDistributions, stats, stringr, tidyr, tidyselect

Suggests eha, knitr, mc2d, pls, rmarkdown, scales, testthat

See at CRAN