R package ordinal: Regression Models for Ordinal Data. 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.

References in zbMATH (referenced in 28 articles , 1 standard article )

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  1. Ejike R. Ugba: serp: An R package for smoothing in ordinal regression (2021) not zbMATH
  2. Riveiro, Maria; Thill, Serge: “That’s (not) the output I expected!” On the role of end user e I ctations in creating explanations of AI systems (2021)
  3. Scalera, Valentino; Iannario, Maria; Monti, Anna Clara: Robust link functions (2021)
  4. Achim Zeileis, Susanne Köll, Nathaniel Graham: Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R (2020) not zbMATH
  5. Barahona, Sonia; Centella, Pablo; Gual-Arnau, Ximo; Ibáñez, M. Victoria; Simó, Amelia: Generalized linear models for geometrical current predictors: an application to predict garment fit (2020)
  6. Bu, Xianwei; Majumdar, Dibyen; Yang, Jie: D-optimal designs for multinomial logistic models (2020)
  7. Kristensen, Simon Bang; Sandberg, Kristian; Bibby, Bo Martin: Regression methods for metacognitive sensitivity (2020)
  8. Maurizio Manuguerra, Gillian Z. Heller, Jun Ma: Continuous Ordinal Regression for Analysis of Visual Analogue Scales: The R Package ordinalCont (2020) not zbMATH
  9. Rainer Hirk, Kurt Hornik, Laura Vana: mvord: An R Package for Fitting Multivariate Ordinal Regression Models (2020) not zbMATH
  10. Torsten Hothorn: Most Likely Transformations: The mlt Package (2020) not zbMATH
  11. Tutz, Gerhard: Modelling heterogeneity: on the problem of group comparisons with logistic regression and the potential of the heterogeneous choice model (2020)
  12. Haag, Fridolin; Zürcher, Sara; Lienert, Judit: Enhancing the elicitation of diverse decision objectives for public planning (2019)
  13. Jorge Cimentada: perccalc: An R package for estimating percentiles from categorical variables (2019) not zbMATH
  14. Khosravi, Ramezan; Owlia, Mohammad Saleh; Fallahnezhad, Mohammad Saber; Amiri, Amirhossein: Phase I risk-adjusted control charts for surgical data with ordinal outcomes (2018)
  15. M. Cristina Heredia-Gómez; Salvador García; Pedro Antonio Gutiérrez; Francisco Herrera: OCAPIS: R package for Ordinal Classification And Preprocessing In Scala (2018) arXiv
  16. Agresti, Alan; Kateri, Maria: Ordinal probability effect measures for group comparisons in multinomial cumulative link models (2017)
  17. De Lara, I. A. R.; Hinde, J. P.; De Castro, A. C.; Da Silva, I. J. O.: A proportional odds transition model for ordinal responses with an application to pig behaviour (2017)
  18. De Lara, Idemauro Antonio Rodrigues; Hinde, John; Taconeli, Cesar Augusto: An alternative method for evaluating stationarity in transition models (2017)
  19. Paul-Christian Bürkner: brms: An R Package for Bayesian Multilevel Models Using Stan (2017) not zbMATH
  20. Ekstrøm, Claus Thorn: The R primer (2016)

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