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 31 articles , 1 standard article )

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  1. Meyer, Mark J.; Morris, Jeffrey S.; Gazes, Regina Paxton; Coull, Brent A.: Ordinal probit functional outcome regression with application to computer-use behavior in rhesus monkeys (2022)
  2. Santi, F., Dickson, M. M., Espa, G., Giuliani, D.: plot3logit: Ternary Plots for Interpreting Trinomial Regression Models (2022) not zbMATH
  3. Ejike R. Ugba: serp: An R package for smoothing in ordinal regression (2021) not zbMATH
  4. 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)
  5. Scalera, Valentino; Iannario, Maria; Monti, Anna Clara: Robust link functions (2021)
  6. Zhou, Thomas J.; Raza, Sughra; Nelson, Kerrie P.: Methods of assessing categorical agreement between correlated screening tests in clinical studies (2021)
  7. Achim Zeileis, Susanne Köll, Nathaniel Graham: Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R (2020) not zbMATH
  8. 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)
  9. Bu, Xianwei; Majumdar, Dibyen; Yang, Jie: D-optimal designs for multinomial logistic models (2020)
  10. Kristensen, Simon Bang; Sandberg, Kristian; Bibby, Bo Martin: Regression methods for metacognitive sensitivity (2020)
  11. Maurizio Manuguerra, Gillian Z. Heller, Jun Ma: Continuous Ordinal Regression for Analysis of Visual Analogue Scales: The R Package ordinalCont (2020) not zbMATH
  12. Rainer Hirk, Kurt Hornik, Laura Vana: mvord: An R Package for Fitting Multivariate Ordinal Regression Models (2020) not zbMATH
  13. Torsten Hothorn: Most Likely Transformations: The mlt Package (2020) not zbMATH
  14. Tutz, Gerhard: Modelling heterogeneity: on the problem of group comparisons with logistic regression and the potential of the heterogeneous choice model (2020)
  15. Haag, Fridolin; Zürcher, Sara; Lienert, Judit: Enhancing the elicitation of diverse decision objectives for public planning (2019)
  16. Jorge Cimentada: perccalc: An R package for estimating percentiles from categorical variables (2019) not zbMATH
  17. Khosravi, Ramezan; Owlia, Mohammad Saleh; Fallahnezhad, Mohammad Saber; Amiri, Amirhossein: Phase I risk-adjusted control charts for surgical data with ordinal outcomes (2018)
  18. M. Cristina Heredia-Gómez; Salvador García; Pedro Antonio Gutiérrez; Francisco Herrera: OCAPIS: R package for Ordinal Classification And Preprocessing In Scala (2018) arXiv
  19. Agresti, Alan; Kateri, Maria: Ordinal probability effect measures for group comparisons in multinomial cumulative link models (2017)
  20. 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)

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