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).

A set of tools that enables efficient estimation of penalized 
    Poisson Pseudo Maximum Likelihood regressions, using lasso or ridge penalties, for models 
    that feature one or more sets of high-dimensional fixed effects. The methodology is based on 
    Breinlich, Corradi, Rocha, Ruta, Santos Silva, and Zylkin (2021) <http://hdl.handle.net/10986/35451> 
    and takes advantage of the method of alternating projections of Gaure (2013) 
    <doi:10.1016/j.csda.2013.03.024> for dealing with HDFE, as well as 
    the coordinate descent algorithm of Friedman, Hastie and Tibshirani (2010) 
    <doi:10.18637/jss.v033.i01> for fitting lasso regressions. The package is also able to carry out 
    cross-validation and to implement the plugin lasso of Belloni, Chernozhukov, Hansen and Kozbur (2016) 
    <doi:10.1080/07350015.2015.1102733>.

Robust penalized (adaptive) elastic net S and M estimators for
    linear regression. The adaptive methods are proposed in
    Kepplinger, D. (2023) <doi:10.1016/j.csda.2023.107730> and the
    non-adaptive methods in
    Cohen Freue, G. V., Kepplinger, D., Salibián-Barrera, M., and Smucler, E.
    (2019) <doi:10.1214/19-AOAS1269>.
    The package implements robust hyper-parameter selection with robust
    information sharing cross-validation according to Kepplinger & Wei (2025)
    <doi:10.1080/00401706.2025.2540970>.

Simulation of continuous, correlated high-dimensional data with 
    time to event or binary response, and parallelized functions for Lasso, 
    Ridge, and Elastic Net penalized regression with repeated starts and 
    two-dimensional tuning of the Elastic Net.