Methods to calculate the expected value of information from a decision-analytic model. This includes the expected value of perfect information (EVPI), partial perfect information (EVPPI) and sample information (EVSI), and the expected net benefit of sampling (ENBS). A range of alternative computational methods are provided under the same user interface. See Heath et al. (2024) <doi:10.1201/9781003156109>, Jackson et al. (2022) <doi:10.1146/annurev-statistics-040120-010730>.
Version: |
1.0.3 |
Depends: |
R (≥ 3.5.0) |
Imports: |
mgcv, earth, mvtnorm, progress, dbarts, posterior, ggplot2, gridExtra, Matrix |
Suggests: |
testthat, BCEA, INLA, splancs, sf, knitr, rmarkdown, rjags, truncnorm, scales, dplyr, heemod |
Published: |
2024-09-16 |
DOI: |
10.32614/CRAN.package.voi |
Author: |
Christopher Jackson [aut, cre],
Anna Heath [aut],
Gianluca Baio [ctb] (Author of code taken from the BCEA package),
Mark Strong [ctb] (Author of code taken from the SAVI package),
Kofi Placid Adragni [ctb] (Author of code taken from the ldr package),
Andrew Raim [ctb] (Author of code taken from the ldr package) |
Maintainer: |
Christopher Jackson <chris.jackson at mrc-bsu.cam.ac.uk> |
BugReports: |
https://github.com/chjackson/voi/issues |
License: |
GPL-3 |
URL: |
https://chjackson.github.io/voi/ |
NeedsCompilation: |
no |
Additional_repositories: |
https://inla.r-inla-download.org/R/stable/ |
Materials: |
README NEWS |
CRAN checks: |
voi results |