A fast implementation of the Expected Value of Perfect Parameter Information (EVPPI) for large Monte Carlo simulations


General Information In this repository you can find 4 things: a Python/Numpy implementation in python a C implementation in c R bindings to the C implementation in r Python bindings to the C implementation using CFFI in python cffi The Expected Value of Perfect Parameter Information (EVPPI) is a concept from decision analysis (modeling decisions under uncertainty). It can be described as a measure for what a (rational) decision-maker would be willing to pay for zero uncertainty on a certain variable. In general, the functions in this repository take in samples from a Monte Carlo model that predicts utility as a function of uncertain input parameters. Here, x denotes the values of the (uncertain) parameter inputs and y the resulting utility. More detailed documentation can be found in the respective packages. Running the C implementation from R was found to be many times faster than existing R implementations, especially for a large number of Monte Carlo samples. Version Information First version. All 4 parts (C, Python, Python/CFFI and R) should be ready to be used in production. Full Changelog: https://github.com/johanneskopton/evpi/commits/v1.0.0