Implements a regularization method for cumulative link models using the Smooth-Effect-on-Response Penalty (SERP). This method allows flexible modeling of ordinal data by enabling a smooth transition from a general cumulative link model to a simplified version of the same model. As the tuning parameter increases from zero to infinity, the subject-specific effects for each variable converge to a single global effect. The approach addresses common issues in cumulative link models, such as parameter unidentifiability and numerical instability, by maximizing a penalized log-likelihood instead of the standard non-penalized version. Fitting is performed using a modified Newton's method. Additionally, the package includes various model performance metrics and descriptive tools. For details on the implemented penalty method, see Ugba (2021) <doi:10.21105/joss.03705> and Ugba et al. (2021) <doi:10.3390/stats4030037>.
Package details |
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Author | Ejike R. Ugba [aut, cre, cph] (<https://orcid.org/0000-0003-2572-0023>) |
Maintainer | Ejike R. Ugba <ejike.ugba@outlook.com> |
License | GPL-2 |
Version | 0.2.5 |
URL | https://github.com/ejikeugba/serp |
Package repository | View on CRAN |
Installation |
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