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: | 1.6.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | dplyr, ggplot2, tidyr, Matrix, methods, parallel, rlang, utils |
| Suggests: | igraph, knitr, rmarkdown, tidyverse, testthat |
| Published: | 2024-04-18 |
| Author: | Claude Boivin Peiyuan Zhu |
| 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: | yes |
| Materials: | README NEWS |
| CRAN checks: | dst results |
| Package source: | dst_1.6.0.tar.gz |
| Windows binaries: | r-prerel: dst_1.5.2.zip, r-release: dst_1.5.2.zip, r-oldrel: dst_1.5.2.zip |
| macOS binaries: | r-prerel (arm64): dst_1.5.2.tgz, r-release (arm64): dst_1.5.2.tgz, r-oldrel (arm64): dst_1.5.2.tgz, r-prerel (x86_64): dst_1.5.2.tgz, r-release (x86_64): dst_1.6.0.tgz |
| Old sources: | dst archive |
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