predict_params

Predict distribution parameters on newdata

Extends the 'SelectBoost' approach to Generalized Additive Models for
Location, Scale and Shape (GAMLSS). Implements bootstrap stability-selection
across parameter-specific formulas (mu, sigma, nu, tau) via gamlss::stepGAIC().
Includes optional standardization of predictors and helper functions for
corrected AIC calculation. More details can be found in Bertrand and Maumy (2024)
<https://hal.science/hal-05352041> that highlights correlation-aware resampling to improve
variable selection for GAMLSS and quantile regression when predictors are
numerous and highly correlated.

Frederic Bertrand

SelectBoost.gamlss

Stability-Selection via Correlated Resampling for 'GAMLSS'
Models

predict_params function

<dl><dt>fit</dt>
<dd>a gamlss fit</dd>
<dt>newdata</dt>
<dd>data.frame</dd></dl>

Arguments

Predict distribution parameters on newdata — predict_params

<dl>

<dt>fit</dt>
<dd>a gamlss fit</dd>


<dt>newdata</dt>
<dd>data.frame</dd>

</dl>

predict_params: Predict distribution parameters on newdata

Description

Usage

Value

Arguments