bnsens: bnsens: A package for sensitivity analysis in Bayesian...
In rramsiya/bnsens: Sensitivity analysis for discrete data in Bayesian networks

Description Details Co-variation schemes Dissimilarity measures Sensitivity functions

The package performs sensitivity analysis for discrete Bayesian networks.

It provides three categories of functions:covariation schemes, dissimilarity measures and sensitivity related functions.
The co-variation schemes available are uniform-covariation scheme, proportional co-variation scheme and order-preserving co-variation scheme.
The dissimilarity measures available are the Chan-Darwiche distance and the Kullback-Leibler divergence. The available sensitivity related functions are the standard sensitivity function and sensquery, a new function which allows to determine the parameter changes that are needed to get a certain probabilistic query of interest.

The available co-variation schemes are:

Uniform co-variation scheme (uniform_covar): distributes the probability mass to be co-varied uniformly among the co-varying parameters.
Proportional co-variation scheme (proportional_covar): distributes the probability mass to be co-varied in the same proportion as in the original Bayesian network.
Order-preserving co-variation scheme (orderp_covar):distributes the to be co-varied probability mass among the co-varying parameters so that the original order of parameters is preserved.

The dissimilarity measures quantify the difference between a Bayesian network and its update after parameter variation.
The available dissimilarity measures are:

Chan-Darwiche distance (CD_distance)
Kullback-Leibler divergence (KL)

The available function for sensitivity analysis are:

Sensitivity function (sensitivity): returns a certain probability of interest given a parameter change. Evidence can be considered.
Sensitivity query (sensquery): returns the parameter changes needed to get a certain probability of interest. Evidence can be considered.