Using the Theory of Belief Functions for evidence calculus. Basic probability assignments, or mass functions, can be defined on the subsets of a set of possible values and combined. A mass function can be extended to a larger frame. Marginalization, i.e. reduction to a smaller frame can also be done. These features can be combined to analyze small belief networks and take into account situations where information cannot be satisfactorily described by probability distributions.
| Version: | 1.5.1 |
| Depends: | R (≥ 2.10) |
| Suggests: | testthat, knitr, rmarkdown, igraph |
| Published: | 2022-01-03 |
| Author: | Claude Boivin, Stat.ASSQ |
| Maintainer: | Claude Boivin <webapp.cb at gmail.com> |
| BugReports: | https://github.com/RAPLER/dst-1/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | dst results |
| Reference manual: | dst.pdf |
| Vignettes: |
Captain_Example Introduction to Belief Functions The Monty Hall Game Peeling algorithm on Zadeh's Example |
| Package source: | dst_1.5.1.tar.gz |
| Windows binaries: | r-devel: dst_1.5.1.zip, r-release: dst_1.5.1.zip, r-oldrel: dst_1.5.1.zip |
| macOS binaries: | r-release (arm64): dst_1.5.1.tgz, r-oldrel (arm64): dst_1.5.1.tgz, r-release (x86_64): dst_1.5.1.tgz, r-oldrel (x86_64): dst_1.5.1.tgz |
| Old sources: | dst archive |
Please use the canonical form https://CRAN.R-project.org/package=dst to link to this page.