CRAN/E | serp

serp

Smooth Effects on Response Penalty for CLM

Installation

About

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.

github.com/ejikeugba/serp
Bug report File report

Key Metrics

Version 0.2.4
R ≥ 3.2.0
Published 2022-02-16 964 days ago
Needs compilation? no
License GPL-2
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Maintainer

Maintainer

Ejike R. Ugba

Authors

Ejike R. Ugba

aut / cre / cph

Material

README
NEWS
Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

serp archive

Depends

R ≥ 3.2.0

Imports

ordinal ≥ 2016-12-12
crayon
stats

Suggests

covr
testthat
VGAM ≥ 1.1-4

Reverse Suggests

gofcat