Package: serp 0.2.5

serp: Smooth Effects on Response Penalty for CLM

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>.

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

serp_0.2.5.tar.gz
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serp_0.2.5.tgz(r-4.4-any)serp_0.2.5.tgz(r-4.3-any)
serp_0.2.5.tar.gz(r-4.5-noble)serp_0.2.5.tar.gz(r-4.4-noble)
serp_0.2.5.tgz(r-4.4-emscripten)serp_0.2.5.tgz(r-4.3-emscripten)
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'))

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

Datasets:
  • wine - Bitterness of wine dataset

On CRAN:

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

3.82 score 1 stars 44 scripts 1.5k downloads 2 exports 8 dependencies

Last updated 3 months agofrom:b6eec7672a. Checks:7 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKJan 24 2025
R-4.5-winOKJan 24 2025
R-4.5-linuxOKJan 24 2025
R-4.4-winOKJan 24 2025
R-4.4-macOKJan 24 2025
R-4.3-winOKJan 24 2025
R-4.3-macOKJan 24 2025

Exports:serpserp.control

Dependencies:crayonlatticeMASSMatrixnlmenumDerivordinalucminf