Ordered homogeneity pursuit lasso (OHPL)
algorithm for group variable selection proposed in Lin et al. (2017)
<DOI:10.1016/j.chemolab.2017.07.004>. The OHPL method exploits the
homogeneity structure in high-dimensional data and enjoys the
grouping effect to select groups of important variables
automatically. This feature makes it particularly useful for
high-dimensional datasets with strongly correlated variables,
such as spectroscopic data.

Ordered Homogeneity Pursuit Lasso for Group Variable Selection

package-ubuntu-20.04-r-4.0.5#cran#OHPL-1.4

<p>Compute D, L, and C in the Fisher Optimal Partitions Algorithm
</p>

<table summary="R argblock">
<tr valign="top">
<td><code>X</code></td>
<td>
<p>a set of samples</p>
</td>
</tr>
<tr valign="top">
<td><code>maxk</code></td>
<td>
<p>maximum number of k</p>
</td>
</tr>
</table>

<p>The D, L, and C statistics in the
Fisher optimal partitions algorithm
</p>

package-ubuntu-20.04-r-4.0.5#cran#OHPL-1.4#docs#dlc

dlc

Description

Usage

Arguments

Value

OHPL

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`X`	a set of samples
`maxk`	maximum number of k