check_fast_vs_generic

Computes both <code>loglik_gamlss_newdata_fast()</code> and <code>loglik_gamlss_newdata()</code>
and reports the absolute difference. Useful for sanity checks.

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

check_fast_vs_generic function

<dl><dt>fit</dt>
<dd>A <code>gamlss</code> fit.</dd>
<dt>newdata</dt>
<dd>Data frame to evaluate on.</dd>
<dt>tol</dt>
<dd>Tolerance for pass/fail (default 1e-8).</dd></dl>

Arguments

Numerical check: fast vs generic deviance log-likelihood — check_fast_vs_generic

<dl>

<dt>fit</dt>
<dd>A <code>gamlss</code> fit.</dd>


<dt>newdata</dt>
<dd>Data frame to evaluate on.</dd>


<dt>tol</dt>
<dd>Tolerance for pass/fail (default 1e-8).</dd>

</dl>

check_fast_vs_generic: Numerical check: fast vs generic deviance log-likelihood

Description

Usage

Value

Arguments