bnmonitor {bnmonitor}  R Documentation 
bnmonitor: A package for sensitivity analysis and robustness in Bayesian networks
Description
Sensitivity and robustness analysis for Bayesian networks.
Details
bnmonitor provides functions to perform sensitivity analysis for both discrete Bayesian networks (DBNs) and Gaussian Bayesian networks (GBNs).
In the discrete case, it provides three categories of functions: covariation schemes, dissimilarity measures and sensitivity related functions.
In the continuous case, both standard and modelpreserving methods are available for perturbation of the mean vector and the covariance matrix.
bnmonitor further provides function to perform robustness studies in DBNs to verify how well a network fits a specific dataset.
DBNs  Robustness
The available functions for robustness are:

Node monitors (
node_monitor
): contribution of each vertex to the overall loglikelihood of the model. 
Observation's influence (
influential_obs
): difference in the loglikelihood of a model learnt with the full dataset and with all but one observation. 
Final node monitors (
node_monitor
): marginal and conditional node monitors to assess the fit of a vertex distribution to the full dataset. 
Sequential node monitors (
seq_node_monitor
): marginal and conditional node monitors for a specific vertex only using sequentially subsets of the dataset. 
Sequential parentchild monitor (
seq_pa_ch_monitor
): parentchild node monitor for a specific vertex and a specific configuration of its parents using sequentially subsets of the dataset.
DBNs  Covariation schemes
The available covariation schemes are:

Uniform covariation scheme (
uniform_covar
): distributes the probability mass to be covaried uniformly among the covarying parameters. 
Proportional covariation scheme (
proportional_covar
): distributes the probability mass to be covaried in the same proportion as in the original Bayesian network. 
Orderpreserving covariation scheme (
orderp_covar
):distributes the to be covaried probability mass among the covarying parameters so that the original order of parameters is preserved.
DBNs  Dissimilarity measures
The dissimilarity measures quantify the difference between a Bayesian network and its update after parameter variation.
The available dissimilarity measures are:
DBNs  Sensitivity functions
The available functions 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.
GBNs  ModelPreserving matrices
The available functions to construct modelpreserving covariation matrices are:

Total covariation matrix (
total_covar_matrix
). 
Partial covariation matrix (
partial_covar_matrix
). 
Rowbased covariation matrix (
row_covar_matrix
). 
Columnbased covariation matrix (
col_covar_matrix
).
GBNs  Mean and Covariance variations
The available functions to perturb the distribution of a GBN are:

Mean variations (
mean_var
). 
Standard covariance variations (
covariance_var
). 
Modelpreserving covariance variations (
model_pres_cov
).
GBNs  Dissimilarity measures
The available dissimilarity measures are:
Frobenius norm (
Fro
).Jeffrey's distance (
Jeffreys
).KullbackLeibler divergence (
KL
).Upper bound to the KL divergence (
KL_bounds
).