Description: Using Dempster-Shafer Theory of Evidence, also called "Theory of Belief Functions". Basic probability assignments, or mass functions, can be defined on the subsets of a set of possible values. Two mass functions can be combined using Dempster's rule of combination. 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.
Collate: 'addTobca.R' 'bca.R' 'bcaRel.R' 'belplau.R' 'decode.R'
'dotprod.R' 'doubles.R' 'dsrwon.R' 'dst.R' 'elim.R' 'encode.R'
'extmin.R' 'inters.R' 'nameRows.R' 'nzdsr.R' 'plautrans.R'
Suggests: testthat, knitr, rmarkdown, igraph

Title: Using the Theory of Belief Functions
Description: 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.

Authors@R: person("Vitaly", "Khudobakhshov", email = "vitaly@dstack.ai",
Description: A native R package that allows to publish, share and track revisions
of plots using your favorite plotting package, e.g. 'ggplot2'. It also provides
a kind of interactivity for such plots by specifying certain parameters for any
specific plot view. See <https://docs.dstack.ai> for more information.
Packaged: 2020-03-21 19:04:00 UTC; vitaly.khudobakhshov

Packaged: 2020-04-09 13:51:07 UTC; vitaly.khudobakhshov

Packaged: 2020-08-12 14:47:08 UTC; vitaly.khudobakhshov

Title: Conditional Sensitivity Analysis for Matched Observational
Maintainer: Paul R. Rosenbaum <rosenbaum@wharton.upenn.edu>
Description: A d-statistic tests the null hypothesis of no treatment effect in a matched, nonrandomized study of the effects caused by treatments.  A d-statistic focuses on subsets of matched pairs that demonstrate insensitivity to unmeasured bias in such an observational study, correcting for double-use of the data by conditional inference. This conditional inference can, in favorable circumstances, substantially increase the power of a sensitivity analysis (Rosenbaum (2010) <doi:10.1007/978-1-4419-1213-8_14>).  There are two examples, one concerning unemployment from Lalive et al. (2006) <doi:10.1111/j.1467-937X.2006.00406.x>, the other concerning smoking and periodontal disease from Rosenbaum (2017) <doi:10.1214/17-STS621>.
Packaged: 2019-04-15 13:23:56 UTC; Rosenbaum