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>.

Version: 0.2.4
Depends: R (≥ 3.2.0)
Imports: ordinal (≥ 2016-12-12), crayon, stats
Suggests: covr, testthat, VGAM (≥ 1.1-4)
Published: 2022-02-16
DOI: 10.32614/CRAN.package.serp
Author: Ejike R. Ugba ORCID iD [aut, cre, cph]
Maintainer: Ejike R. Ugba <ejike.ugba at>
License: GPL-2
NeedsCompilation: no
Materials: README NEWS
CRAN checks: serp results


Reference manual: serp.pdf


Package source: serp_0.2.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): serp_0.2.4.tgz, r-oldrel (arm64): serp_0.2.4.tgz, r-release (x86_64): serp_0.2.4.tgz, r-oldrel (x86_64): serp_0.2.4.tgz
Old sources: serp archive

Reverse dependencies:

Reverse suggests: gofcat, insight, parameters


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