Regularized Parametric Modal Regression: Lasso, Elastic Net and Ridge R package ModalNet version 0.3.0 Variable selection and estimation for parametric modal regression with continuous positive responses (Gamma, Weibull and Lognormal families) under Lasso, elastic-net, ridge and adaptive-Lasso penalties. The conditional mode is linked to a linear predictor through a logarithmic link using the mode reparameterization of Galarza and Lachos (2026) <doi:10.48550/arXiv.2603.07099>. The penalized log-likelihood is maximized with a proximal Newton algorithm (iteratively reweighted penalized least squares): at each iteration the modal log-likelihood is replaced by a local quadratic surrogate in the linear predictor, which yields a pseudo-response and a working weight in closed form, and the resulting weighted penalized least-squares subproblem is solved by the coordinate-descent solver of 'glmnet' (Friedman, Hastie and Tibshirani, 2010, <doi:10.18637/jss.v033.i01>). The regularization parameter is chosen along a warm-started path by the extended Bayesian information criterion of Chen and Chen (2008) <doi:10.1093/biomet/asn034>. Software Depends: R (>= 4.1.0) Imports: glmnet, stats, graphics Suggests: survival Christian E. Galarza <chedgala@espol.edu.ec> Comprehensive R Archive Network (CRAN) Christian E. Galarza [aut, cre, cph] (ORCID: <https://orcid.org/0000-0002-4818-6006>, affiliation: Escuela Superior Politécnica del Litoral, Guayaquil, Ecuador), Víctor H. Lachos [aut] (ORCID: <https://orcid.org/0000-0002-7239-2459>, affiliation: University of Connecticut, Storrs, CT, USA), Dashun Liu [aut] (affiliation: University of Connecticut, Storrs, CT, USA) GPL-3 2026-11-04 application/tgz https://CRAN.R-project.org/package=ModalNet doi:10.32614/CRAN.package.ModalNet