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.

The aim of most plant breeding programmes is simultaneous improvement of several characters. An objective method involving simultaneous selection for several attributes then becomes necessary. It has been recognised that most rapid improvements in the economic value is expected from selection applied simultaneously to all the characters which determine the economic value of a plant, and appropriate assigned weights to each character according to their economic importance, heritability and correlations between characters. So the selection for economic value is a complex matter. If the component characters are combined together into an index in such a way that when selection is applied to the index, as if index is the character to be improved, most rapid improvement of economic value is expected. Such an index was first proposed by Smith (1937 <doi:10.1111/j.1469-1809.1936.tb02143.x>) based on the Fisher's (1936 <doi:10.1111/j.1469-1809.1936.tb02137.x>) "discriminant function" Dabholkar (1999 <https://books.google.co.in/books?id=mlFtumAXQ0oC&lpg=PA4&ots=Xgxp1qLuxS&dq=elements%20of%20biometrical%20genetics&lr&pg=PP1#v=onepage&q&f=false>). In this package selection index is calculated based on the Smith (1937) selection index method.

Computes bounds and sensitivity parameters as part of sensitivity 
    analysis for selection bias. Different bounds are provided: the SV (Smith 
    and VanderWeele), sharp bounds,  AF (assumption-free) bound, GAF (generalized 
    AF), and CAF (counterfactual AF) bounds. The calculation of the sensitivity 
    parameters for the SV, sharp, and GAF bounds assume an additional dependence 
    structure in form of a generalized M-structure. The bounds can be
    calculated for any structure as long as the necessary assumptions hold. See 
    Smith and VanderWeele (2019) <doi:10.1097/EDE.0000000000001032>,
    Zetterstrom, Sjölander, and Waernabum (2025) <doi:10.1177/09622802251374168>,
    Zetterstrom and Waernbaum (2022) <doi:10.1515/em-2022-0108>, and
    Zetterstrom (2024) <doi:10.1515/em-2023-0033>.