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. An outline of the penalty implemented here is found in Tutz, G and Gertheiss, J (2016) <doi:10.1177/1471082X16642560>.

Version: 0.1.8
Depends: R (≥ 3.2.0)
Imports: ordinal (≥ 2016-12-12), stats
Suggests: covr, testthat, VGAM (≥ 1.1-4)
Published: 2021-01-29
Author: Ejike R. Ugba [aut, cre, cph]
Maintainer: Ejike R. Ugba <ejike.ugba at outlook.com>
BugReports: https://github.com/ejikeugba/serp/issues
License: GPL-2
URL: https://github.com/ejikeugba/serp
NeedsCompilation: no
Materials: README NEWS
CRAN checks: serp results

Downloads:

Reference manual: serp.pdf
Package source: serp_0.1.8.tar.gz
Windows binaries: r-devel: serp_0.1.8.zip, r-release: serp_0.1.8.zip, r-oldrel: serp_0.1.8.zip
macOS binaries: r-release: serp_0.1.8.tgz, r-oldrel: serp_0.1.8.tgz

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