Compute the context specific interventional discrepeancy
of a staged tree with respect to a reference staged tree.

Creates and fits staged event tree probability models,
which are probabilistic graphical models capable of representing
asymmetric conditional independence statements
for categorical variables.
Includes functions to create, plot and fit staged
event trees from data, as well as many efficient structure
learning algorithms.
References:
Carli F, Leonelli M, Riccomagno E, Varando G (2022).
<doi: 10.18637/jss.v102.i06>.
Collazo R. A., Görgen C. and Smith J. Q.
(2018, ISBN:9781498729604).
Görgen C., Bigatti A., Riccomagno E. and Smith J. Q. (2018)
<arXiv:1705.09457>.
Thwaites P. A., Smith, J. Q. (2017) <arXiv:1510.00186>.
Barclay L. M., Hutton J. L. and Smith J. Q. (2013)
<doi:10.1016/j.ijar.2013.05.006>.
Smith J. Q. and Anderson P. E. (2008)
<doi:10.1016/j.artint.2007.05.004>.

Gherardo Varando

stagedtrees

Staged Event Trees

Federico Carli

Manuele Leonelli

Eva Riccomagno

cid function

<dl><dt>object1</dt>
<dd>an object of class <code>sevt</code>.</dd>
<dt>object2</dt>
<dd>an object of class <code>sevt</code>.</dd>
<dt>FUN</dt>
<dd>a function that is used to aggregate CID for each variable.
The default <code>mean</code> will obtain the CID
as defined in Leonelli and Varando (2023).</dd></dl>

Arguments

Context specific interventional discrepancy — cid

<dl>

<dt>object1</dt>
<dd>an object of class <code>sevt</code>.</dd>


<dt>object2</dt>
<dd>an object of class <code>sevt</code>.</dd>


<dt>FUN</dt>
<dd>a function that is used to aggregate CID for each variable.
The default <code>mean</code> will obtain the CID
as defined in Leonelli and Varando (2023).</dd>

</dl>

cid: Context specific interventional discrepancy

Description

Usage

Value

Arguments

References

Examples