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: | 1.4.0 |
| Suggests: | testthat, knitr, rmarkdown, igraph |
| Published: | 2019-08-20 |
| 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: |
An example |
| Package source: | dst_1.4.0.tar.gz |
| Windows binaries: | r-devel: dst_1.4.0.zip, r-release: dst_1.4.0.zip, r-oldrel: dst_1.4.0.zip |
| OS X binaries: | r-release: dst_1.4.0.tgz, r-oldrel: dst_1.4.0.tgz |
| Old sources: | dst archive |
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