soft_thresholding

Applies the soft thresholding operation to a numeric input, commonly used in Lasso and sparse modeling to induce shrinkage and sparsity.

Implements statistical methods for detecting evolutionary shifts
in both the optimal trait value (mean) and evolutionary diffusion variance.
The method uses an L1-penalized optimization framework to identify branches
where shifts occur, and the shift magnitudes. It also supports the
inclusion of measurement error. For more details, see Zhang, Ho, and Kenney (2023) <doi:10.48550/arXiv.2312.17480>.

Wensha Zhang

ShiVa

Detection of Evolutionary Shifts in Both Optimal Value and
Variance

Lam Si Tung Ho

Toby Kenney

soft_thresholding function

<dl><dt>z</dt>
<dd>A numeric value to be thresholded.</dd>
<dt>lambda</dt>
<dd>A non-negative numeric value indicating the threshold level (degree of shrinkage).</dd></dl>

Arguments

Soft Thresholding — soft_thresholding

<dl>

<dt>z</dt>
<dd>A numeric value to be thresholded.</dd>


<dt>lambda</dt>
<dd>A non-negative numeric value indicating the threshold level (degree of shrinkage).</dd>

</dl>

soft_thresholding: Soft Thresholding

Description

Usage

Value

Arguments