cor_test

This function simply calls the <code>pcalg::gaussCItest()</code> function from the pcalg package.

Tools for causal structure learning from observational data, with emphasis on temporally ordered variables.
The package implements the Temporal Peter–Clark (TPC) algorithm (Petersen, Osler & Ekstrøm, 2021;
<doi:10.1093/aje/kwab087>), the Temporal Greedy Equivalence Search (TGES) algorithm (Larsen, Ekstrøm & Petersen,
2025; <doi:10.48550/arXiv.2502.06232>) and Temporal Fast Causal Inference (TFCI).
It provides a unified framework for specifying background knowledge,
which can be incorporated into the implemented algorithms from the R packages 'bnlearn' (Scutari, 2010;
<doi:10.18637/jss.v035.i03>) and
'pcalg' (Kalish et al., 2012; <doi:10.18637/jss.v047.i11>), as well as the Java library 'Tetrad'
(Scheines et al., 1998; <doi:10.1207/s15327906mbr3301_3>).
The package further includes utilities for visualization, comparison, and evaluation of graph structures,
facilitating performance evaluation and methodological studies.

Bjarke Hautop Kristensen

causalDisco

Tools for Causal Discovery on Observational Data

Frederik Fabricius-Bjerre

Anne Helby Petersen

Claus Thorn Ekstrøm

Tobias Ellegaard Larsen

cor_test function

<dl><dt>x</dt>
<dd>Index of x variable.</dd>
<dt>y</dt>
<dd>Index of y variable.</dd>
<dt>conditioning_set</dt>
<dd>Index vector of conditioning variable(s), possibly <code>NULL</code>.</dd>
<dt>suff_stat</dt>
<dd>Sufficient statistic; list with data,
binary variables and order.</dd></dl>

Arguments

Test for Vanishing Partial Correlations — cor_test

<dl>

<dt>x</dt>
<dd>Index of x variable.</dd>


<dt>y</dt>
<dd>Index of y variable.</dd>


<dt>conditioning_set</dt>
<dd>Index vector of conditioning variable(s), possibly <code>NULL</code>.</dd>


<dt>suff_stat</dt>
<dd>Sufficient statistic; list with data,
binary variables and order.</dd>

</dl>

cor_test: Test for Vanishing Partial Correlations

Description

Usage

Value

Arguments