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. PRC is described in Signorelli
    (2024) <doi:10.48550/arXiv.2309.15600> and in Signorelli et al. (2021)
    <doi:10.1002/sim.9178>.

Estimating Non-Simplified Vine Copulas Using Penalized Splines.

A regularization approach for Cox Frailty Models by penalization methods is provided.

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

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.

Currently incorporate the generalized odds-rate model (a type of linear 
    transformation model) for interval-censored data based on penalized monotonic B-Spline. 
    More methods under other semiparametric models such as cure model or additive model will
    be included in future versions. For more details see Lu, M., Liu, Y., Li, C. and Sun, J. 
    (2019) <arXiv:1912.11703>.

Structured fusion Lasso penalized estimation of multi-state models with the penalty applied to absolute effects and absolute effect differences (i.e., effects on transition-type specific hazard rates).

Implementation of the semi-parametric proportional-hazards (PH) of Sy and Taylor (2000) <doi:10.1111/j.0006-341X.2000.00227.x> extended to time-varying covariates. Estimation and variable selection are based on the methodology described in Beretta and Heuchenne (2019) <doi:10.1080/02664763.2018.1554627>; confidence intervals of the parameter estimates may be computed using a bootstrap approach. Moreover, data following the PH cure model may be simulated using a method similar to Hendry (2014) <doi:10.1002/sim.5945>, where the event-times are generated on a continuous scale from a piecewise exponential distribution conditional on time-varying covariates.