Implements 'SelectBoost'-style variable selection workflows for
    functional data analysis. The package provides FDA-native design and
    preprocessing objects for raw curves, spline-basis expansions, Functional 
    principal component analysis scores, and scalar covariates; grouped 
    stability-selection routines based on repeated subject-level subsampling; 
    multiple selector backends including lasso, group lasso, and sparse-group 
    lasso; FDA-aware grouping functions and calibration helpers for 'SelectBoost';
    method-comparison utilities; a formula interface; simulation,
    benchmarking, and validation helpers with mapped ground truth;
    targeted sensitivity-study utilities and shipped benchmark summaries
    for mean 'F1' comparisons between FDA-aware and plain 'SelectBoost'
    workflows; small example datasets; and an optional adapter to the
    native stability-selection interface from the 'FDboost' package.

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.

A 'SelectBoost'-inspired workflow for sparse quantile
    regression. The package builds correlation neighborhoods, perturbs
    correlated predictors with a directional sampler inspired by the original
    'SelectBoost' internals, refits penalized quantile regression models on the
    perturbed designs, and aggregates variable-selection frequencies across a
    path of correlation thresholds.

Provides tools for the simultaneous improvement of multiple traits in plant breeding. Building upon the classical selection index (Smith 1937 <doi:10.1111/j.1469-1809.1936.tb02143.x>) and modern quantitative genetics (Kang 2020 <doi:10.1007/978-3-319-91223-3>), this package calculates classical phenotypic, genomic, marker-assisted, restricted/constrained, and eigen selection indices. It also incorporates multi-stage selection evaluation and stochastic simulations to estimate genetic advance based on economic weights, heritability, and genetic correlations.