diabetes

Discretized version of the widely-used Pima Indians Diabetes Database

An implementation of sensitivity and robustness methods in Bayesian networks in R. It includes methods to perform parameter variations via a variety of co-variation schemes, to compute sensitivity functions and to quantify the dissimilarity of two Bayesian networks via distances and divergences. It further includes diagnostic methods to assess the goodness of fit of a Bayesian networks to data, including global, node and parent-child monitors. Reference: M. Leonelli, R. Ramanathan, R.L. Wilkerson (2022) <doi:10.1016/j.knosys.2023.110882>.

Manuele Leonelli

bnmonitor

An Implementation of Sensitivity Analysis in Bayesian Networks

Ramsiya Ramanathan

Rachel Wilkerson

diabetes function

A dataframe with 392 observations on the following 9 binary variables:<ul>
<li>PREG: number of times pregnant (low/high)</li>
<li>GLUC: plasma glucose concentration (low/high)</li>
<li>PRES: diastolic blood pressure (low/high)</li>
<li>TRIC: triceps skin fold thickness (low/high)</li>
<li>INS: 2-hour serum insulin (low/high)</li>
<li>MASS: body mass index (low/high)</li>
<li>PED: diabetes pedigree function (low/high)</li>
<li>AGE: age (low/high)</li>
<li>DIAB: test for diabetes (neg/pos)</li>
</ul>

diabetes: Pima Indian Diabetes Data

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