Package: serp 0.2.4

serp: Smooth Effects on Response Penalty for CLM

A regularization method for the cumulative link models. The smooth-effect-on-response penalty (SERP) provides flexible modelling of the ordinal model by enabling the smooth transition from the general cumulative link model to a coarser form of the same model. In other words, as the tuning parameter goes from zero to infinity, the subject-specific effects associated with each variable in the model tend to a unique global effect. The parameter estimates of the general cumulative model are mostly unidentifiable or at least only identifiable within a range of the entire parameter space. Thus, by maximizing a penalized rather than the usual non-penalized log-likelihood, this and other numerical problems common with the general model are to a large extent eliminated. Fitting is via a modified Newton's method. Several standard model performance and descriptive methods are also available. For more details on the penalty implemented here, see, Ugba (2021) <doi:10.21105/joss.03705> and Ugba et al. (2021) <doi:10.3390/stats4030037>.

Authors:Ejike R. Ugba [aut, cre, cph]

serp_0.2.4.tar.gz
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serp.pdf |serp.html
serp/json (API)
NEWS

# Install 'serp' in R:
install.packages('serp', repos = c('https://ejikeugba.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/ejikeugba/serp/issues

Datasets:
  • wine - Bitterness of wine dataset

On CRAN:

categorical-dataordinal-regressionpenalized-regressionproportional-odds-regressionregularization-techniques

3.18 score 1 stars 22 scripts 1.4k downloads 2 exports 8 dependencies

Last updated 3 years agofrom:5ac5400e21. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 14 2024
R-4.5-winOKOct 14 2024
R-4.5-linuxOKOct 14 2024
R-4.4-winOKOct 14 2024
R-4.4-macOKOct 14 2024
R-4.3-winOKOct 14 2024
R-4.3-macOKOct 14 2024

Exports:serpserp.control

Dependencies:crayonlatticeMASSMatrixnlmenumDerivordinalucminf