Implementations of algorithms from 
 Learning Sparse Penalties for Change-point Detection
 using Max Margin Interval Regression, by
 Hocking, Rigaill, Vert, Bach
 <http://proceedings.mlr.press/v28/hocking13.html>
 published in proceedings of ICML2013.

Computes penalized regression calibration (PRC), a
    statistical method for the dynamic prediction of survival when many
    longitudinal predictors are available. See Signorelli (2024)
    <doi:10.32614/RJ-2024-014> and Signorelli et al. (2021)
    <doi:10.1002/sim.9178> for details.

Estimating Non-Simplified Vine Copulas Using Penalized Splines.

Different regularization approaches for Cox Frailty Models by penalization methods are provided.
	see Groll et al. (2017) <doi:10.1111/biom.12637> for effects selection.
	See also Groll and Hohberg (2024) <doi:10.1002/bimj.202300020> for classical LASSO approach.

Estimation of univariate (conditional) densities using penalized B-splines with automatic selection of optimal smoothing parameter.

Implements statistical methods for estimating disease penetrance in 
    family-based studies. Penetrance refers to the probability of disease§
    manifestation in individuals carrying specific genetic variants. The package 
    provides tools for age-specific penetrance estimation, handling missing data, 
    and accounting for ascertainment bias in family studies.
    Cite as: Kubista, N., Braun, D. & Parmigiani, G. (2024) <doi:10.48550/arXiv.2411.18816>.

Fits single- and multiple-group penalized factor analysis models 
    via a trust-region algorithm with integrated automatic multiple tuning 
    parameter selection (Geminiani et al., 2021 <doi:10.1007/s11336-021-09751-8>). 
    Available penalties include lasso, adaptive lasso, scad, mcp, and ridge.

Offers a comprehensive collection of penguin-related datasets suitable for descriptive statistics, hypothesis testing, and experimental design.
    Derived from open ecological and biological sources such as Palmer Station studies, the package integrates datasets covering adult morphology, clutch size, blood isotope composition, and heart rate.
    It is designed for researchers, students, and educators to explore statistical methods including ANOVA, regression, multivariate analysis, and design of experiments in an accessible and reproducible context.