ModalNet: Regularized Parametric Modal Regression: Lasso, Elastic Net and
Ridge
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>.
| Version: |
0.3.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
glmnet, stats, graphics |
| Suggests: |
survival |
| Published: |
2026-11-04 |
| DOI: |
10.32614/CRAN.package.ModalNet |
| Author: |
Christian E. Galarza
 [aut, cre,
cph] (affiliation: Escuela Superior Politécnica del Litoral,
Guayaquil, Ecuador),
Víctor H. Lachos
[aut] (affiliation: University of Connecticut, Storrs, CT, USA),
Dashun Liu [aut] (affiliation: University of Connecticut, Storrs, CT,
USA) |
| Maintainer: |
Christian E. Galarza <chedgala at espol.edu.ec> |
| BugReports: |
https://github.com/chedgala/ModalNet/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/chedgala/ModalNet |
| NeedsCompilation: |
no |
| Materials: |
NEWS |
| CRAN checks: |
ModalNet results |
Documentation:
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