create_penalty

Function to create mgcv-type penalty

Allows for the specification of semi-structured deep distributional regression models which are fitted in a neural network as
proposed by Ruegamer et al. (2023) <doi:10.18637/jss.v105.i02>.
Predictors can be modeled using structured (penalized) linear effects, structured non-linear effects or using an unstructured deep network model.

David Ruegamer

deepregression

Fitting Deep Distributional Regression

Christopher Marquardt

Laetitia Frost

Florian Pfisterer

Philipp Baumann

Chris Kolb

Lucas Kook

create_penalty function

<dl><dt>evaluated_gam_term</dt>
<dd>a list resulting from a smoothConstruct call</dd>
<dt>df</dt>
<dd>integer; specified degrees-of-freedom for the gam term</dd>
<dt>controls</dt>
<dd>list; further arguments defining the smooth</dd>
<dt>Z</dt>
<dd>matrix; matrix for constraint(s)</dd></dl>

Arguments

Function to create mgcv-type penalty — create_penalty

<dl>

<dt>evaluated_gam_term</dt>
<dd>a list resulting from a smoothConstruct call</dd>


<dt>df</dt>
<dd>integer; specified degrees-of-freedom for the gam term</dd>


<dt>controls</dt>
<dd>list; further arguments defining the smooth</dd>


<dt>Z</dt>
<dd>matrix; matrix for constraint(s)</dd>

</dl>

create_penalty: Function to create mgcv-type penalty

Description

Usage

Value

Arguments