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  "Description": "Implements a regularization method for cumulative link\nmodels using the Smooth-Effect-on-Response Penalty (SERP). This\nmethod allows flexible modeling of ordinal data by enabling a\nsmooth transition from a general cumulative link model to a\nsimplified version of the same model. As the tuning parameter\nincreases from zero to infinity, the subject-specific effects\nfor each variable converge to a single global effect. The\napproach addresses common issues in cumulative link models,\nsuch as parameter unidentifiability and numerical instability,\nby maximizing a penalized log-likelihood instead of the\nstandard non-penalized version. Fitting is performed using a\nmodified Newton's method. Additionally, the package includes\nvarious model performance metrics and descriptive tools. For\ndetails on the implemented penalty method, see Ugba (2021)\n<doi:10.21105/joss.03705> and Ugba et al. (2021)\n<doi:10.3390/stats4030037>.",
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  "Date/Publication": "2024-11-25 13:36:42 UTC",
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