cv_folds

Obtain indices for splitting observations into \(K\) blocks to be to be
folded into training and test data during \(K\)-fold cross-validation.

Perform sparse estimation of a Gaussian graphical model (GGM) with node aggregation through variable clustering. Currently, the package implements the clusterpath estimator of the Gaussian graphical model (CGGM) (Touw, Alfons, Groenen & Wilms, 2025; <doi:10.48550/arXiv.2407.00644>).

Andreas Alfons

clusterGGM

Sparse Gaussian Graphical Modeling with Variable Clustering

Daniel J.W. Touw

Ines Wilms

Patrick J.F. Groenen

cv_folds function

<dl><dt>n</dt>
<dd>an integer giving the number of observations to be split.</dd>
<dt>K</dt>
<dd>an integer giving the number of blocks into which the observations
should be split (the default is five).</dd></dl>

Arguments

Author

Create Cross-Validation Folds — cv_folds

<dl>

<dt>n</dt>
<dd>an integer giving the number of observations to be split.</dd>


<dt>K</dt>
<dd>an integer giving the number of blocks into which the observations
should be split (the default is five).</dd>

</dl>

cv_folds: Create Cross-Validation Folds

Description

Usage

Value

Arguments

Author

See Also

Examples