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r-ordinal

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Implementation of cumulative link (mixed) models also known as ordered regression models, proportional odds models, proportional hazards models for grouped survival times and ordered logit/probit/... models. Estimation is via maximum likelihood and mixed models are fitted with the Laplace approximation and adaptive Gauss-Hermite quadrature. Multiple random effect terms are allowed and they may be nested, crossed or partially nested/crossed. Restrictions of symmetry and equidistance can be imposed on the thresholds (cut-points/intercepts). Standard model methods are available (summary, anova, drop-methods, step, confint, predict etc.) in addition to profile methods and slice methods for visualizing the likelihood function and checking convergence.

Installation

To install this package, run one of the following:

Conda
$conda install conda-forge::r-ordinal

Usage Tracking

2025.12_29
2023.12_4.1
2023.12_4
2022.11_16
2019.12_10
5 / 8 versions selected
Downloads (Last 6 months): 0

About

Summary

Implementation of cumulative link (mixed) models also known as ordered regression models, proportional odds models, proportional hazards models for grouped survival times and ordered logit/probit/... models. Estimation is via maximum likelihood and mixed models are fitted with the Laplace approximation and adaptive Gauss-Hermite quadrature. Multiple random effect terms are allowed and they may be nested, crossed or partially nested/crossed. Restrictions of symmetry and equidistance can be imposed on the thresholds (cut-points/intercepts). Standard model methods are available (summary, anova, drop-methods, step, confint, predict etc.) in addition to profile methods and slice methods for visualizing the likelihood function and checking convergence.

Last Updated

Aug 21, 2024 at 06:41

License

GPL-2.0-or-later

Total Downloads

138.6K

Version Downloads

14.9K

Supported Platforms

linux-aarch64
linux-64
macOS-64
linux-ppc64le
win-64
macOS-arm64