Published February 29, 2024 | Version v1.0.0
Software Open

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

  • 1. ROR icon University of Bonn

Description

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

Files

johanneskopton/evpi-v1.0.0.zip

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Additional details

Related works

Funding

Deutsche Forschungsgemeinschaft
PhenoRob EXC-2070–390732324

Software

Repository URL
https://github.com/johanneskopton/evpi
Programming language
C, R, Python