ordinal (2014.12-22)

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.

Maintainer: Rune Haubo Bojesen Christensen
Author(s): Rune Haubo Bojesen Christensen [aut, cre]

License: GPL (>= 2)

Uses: MASS, Matrix, ucminf, lme4, xtable, nnet
Reverse depends: RcmdrPlugin.MPAStats, sensR
Reverse suggests: agridat, AICcmodavg, broom, dotwhisker, effects, emmeans, ensemblepp, generalhoslem, ggeffects, ggstatsplot, insight, lsmeans, mlt.docreg, nonnest2, performance, RVAideMemoire, sensR, simstudy, sure, tram
Reverse enhances: margins, memisc, MuMIn, prediction, stargazer, texreg

Released almost 5 years ago.