wine

<p>The <code>wine</code> dataset adopted from Randall(1989),
represents the outcome of a factorial experiment on factors
determining the bitterness of wine. Two treatment factors
(temperature and contact) with two levels each are provided,
with the rating of wine taken on a continuous scale in the interval
from 0 (none) to 100 (intense). These were subsequently grouped
into five ordered categories ranging from 1 = 'least bitter'
to 5 = 'most bitter'. Altogether, nine different judges assessed
wine from two bottles and out of the four treatment conditions,
making a total of 72 observations.</p>

dataset

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>.

wine: Bitterness of wine dataset

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