Multivariate Ordinal Regression Models
A flexible framework for fitting multivariate
ordinal regression models with composite likelihood methods.
News
This file documents updates and changes in the package mvord
changes in version 0.2.1 (2017-11-29)
- adapted predict(), get.prob(), marginal.predict()
- added contrasts as argument to mvord() and mvord2()
- fixed bug in VGAM design of coef.constraints
- changed internal design matrices
changes in version 0.2.0 (2017-11-10)
- implemented nobs.mvord()
- implemented vcov.mvord()
- implemented terms.mvord()
- implemented model.matrix.mvord()
- implemented fitted.mvord()
- implemented logLik.mvord()
- AIC() and BIC() are now available
- additional argument scale
- new (additional) design for coef.constraints in analogy to constraints in VGAM
- category-specific regression parameters
- additional argument offset
- new class "mvlink"
- implemented coef.constraints()
- logPL(), claic() and clbic() are not exported anymore. use logLik(), AIC() and BIC() instead.
- renamed cor_general, cor_ar1, cor_equi, cov_general
- implemented names_constraints()
changes in version 0.1.0 (2017-10-17)
- changed function name from multord() to mvord()
- changed function name from multord2() to mvord2()
- implemented predict function()
- implemented predict.marginal function()
- implemented get.prob function()
- changed link probit to mvprobit()
- changed link logit to mvlogit(df = 8L); default value of degrees of freedom is 8
- implemented a more flexible multivariate logistic distribution with logistic marginals and t copula (df are settable)
- additional argument PL.lag