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: 1.6.0
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


Reference manual: dst.pdf
Vignettes: Bayes_Rule
Introduction to Belief Functions
The Monty Hall Game
The original peter, John and Mary example
Peeling algorithm on Zadeh's Example


Package source: dst_1.6.0.tar.gz
Windows binaries: r-devel: dst_1.6.0.zip, r-release: dst_1.6.0.zip, r-oldrel: dst_1.6.0.zip
macOS binaries: r-release (arm64): dst_1.6.0.tgz, r-oldrel (arm64): dst_1.6.0.tgz, r-release (x86_64): dst_1.6.0.tgz, r-oldrel (x86_64): dst_1.6.0.tgz
Old sources: dst archive


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