CMOR: Cross-sectional Multivariate Ordinal Regression Models.
In MultOrd: Multivariate Ordinal Regression Models

CMOR fits a cross-sectional ordinal regression model.

CMOR(formula, data, link = "probit", error.structure = corGeneral(~1),
  weights = NULL, coef.constraints = NULL, coef.values = NULL,
  threshold.constraints = NULL, threshold.values = NULL, se = FALSE,
  start.values = NULL, solver = "newuoa")

`formula`	a `formula` object for multivariate responses in the form of `cbind(Y1, ..., Yj) ~ X1 + ... + Xp`. Responses need to be ordered factors.
`data`	`data.frame` containing the ordinal observations and the regression coefficients of the model
`link`	`"probit"` or `"logit"` link function
`error.structure`	different `error.structures`: general correlation structure (default) `corGeneral(~1)`, general covariance structure `covGeneral(~1)`, factor dependent correlation structure `covGeneral(~f)`, factor dependent covariance structure `covGeneral(~f)` or covariate dependent corEqui structure `corEqui(~X)`. See `error.structures` or 'Details'.
`weights`	(optional) case weights (default is 1). Negative weights are not allowed.
`coef.constraints`	`vector` or `matrix` of constraints on coefficients. See 'Details'.
`coef.values`	`matrix` setting fixed values on the regression coefficients. See 'Details'.
`threshold.constraints`	`vector` of constraints on thresholds. See 'Details'.
`threshold.values`	(optional) `list` of fixed values for threshold parameters. See 'Details'.
`se`	logical, if `TRUE` standard errors are computed.
`start.values`	(optional) vector of starting values.
`solver`	choose applicable solver of `optimx` (default is `newuoa`)