dst: Using the Theory of Belief Functions

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: 1.3.0
Suggests: testthat, knitr, rmarkdown, igraph
Published: 2018-12-05
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

Downloads:

Reference manual: dst.pdf
Vignettes: An example
Package source: dst_1.3.0.tar.gz
Windows binaries: r-devel: dst_1.3.0.zip, r-release: dst_1.3.0.zip, r-oldrel: dst_1.3.0.zip
OS X binaries: r-release: dst_1.3.0.tgz, r-oldrel: dst_1.3.0.tgz
Old sources: dst archive

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