parseMinimalData: Parse input data to fitBabyMonitor
In dhelkey/dghrank: Construct Draper-Gittoes-Helkey Institution Rankings

Parse input data to fitBabyMonitor

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parseMinimalData(minimal_data, num_cat, num_cont, subset = FALSE,
  outcome_na = "set0", subset_na = "category", cat_na = "category",
  cont_na = "median", n_cutoff = 5)

`minimal_data`	Data_frame with a particular format: 1st column: Outcome vector (0-1 encoding) 2nd column: Institution ID 3rd column: Subset (if subset == TRUE): Next: num_cat columns of categorical variables (num_cat can equal 0) Next: num_cont columns of continuous variables (num_cont can equal 0)
`num_cat`	integer. Number of categorical risk adjusters.
`num_cont`	integer. Number of continious risk adjusters.
`subset`	logical; if TRUE perform analysis with a subset variable ( and `minimal_data` must have subset data in the 3rd column)
`outcome_na`	Method for handling any NA values in the outcome vec. 'remove' removes rows with NA outcomes while 'set0' keeps the row and sets the outcome to 0.
`subset_na`	Method for handling any NA subset values. 'remove' removes rows with NA subset values while 'category' makes a new subset category (coded as 99) for NA values.
`cat_na`	Method for handling any NA values in the categorical risk adjusters. 'remove' removes rows with NA values while 'category' makes a new category (coded as 99) for NA catorical risk adjusters.
`cont_na`	Method for handling any NA values in the continous risk adjusters. 'remove' removes rows with NA values while 'median' replaces NA with the median value of the risk adjuster.