ordinal (2019.12-10)

1 user

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

Released 4 months ago.

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