find.ordinal: Fits dose interval using ordinal approach
In lixiaomao/personalized-dosing-interval: Determining Personalized Dosing Interval

This function is the wrapper of fitting functions for doseInt

1	find.ordinal(x)

`train`	the training data set.
`test`	the testing data set. If test is NULL, use train as test.
`pred0`	the initial prediction for training data set, if NULL, default initialization is used
`nfolds`	the number of folds used in cross-validation.
`alpha`	the guaranteed probability in PDI and the relative loss in EDI. Default is c(0.5,0.5).
`type`	indicates whether PDI or EDI is to be calculated.
`two.sided`	indicator of whether two-sided interval is considered.
`family`	specifis the methods. 'continuous' leads to DC-algorithm,'ordinal' handles the ordinal treatments, while 'as.ordinal' cuts continuous treatment into several ordinal levels and apply the ordinal algorithm.
`method`	specified methods used for DC and ordinal approaches.
`trace`	the level of detail to be printed: 1,2,3,4
`lower`	True or False, Whether lower boundary is considered when applied to one-sided interval
`K`	level of ordinal treatment, default 10
`global`	if global data are used for ordinal approach, default False
`margin`	how large is the margin if global=False, default 0
`breaks`	ways 'as.ordinal' cuts continuous treatment into several ordinal levels, default 'quantile'. User can also choose 'uniform' or specify customized breaks
`Cost`	cost for the support vector machine