This function creates a missing data indicator for each pattern. The continuous
probability distributions (Van Buuren, 2012, pp. 63, 64) will be induced on the
weighted sum scores, calculated earlier in the multivariate amputation function
`ampute`

.

`ampute.continuous(P, scores, prop, type)`

P

A vector containing the pattern numbers of the cases's candidacies. For each case, a value between 1 and #patterns is given. For example, a case with value 2 is candidate for missing data pattern 2.

scores

A list containing vectors with the candidates's weighted sum scores,
the result of an underlying function in `ampute`

.

prop

A scalar specifying the proportion of missingness. Should be a value between 0 and 1. Default is a missingness proportion of 0.5.

type

A vector of strings containing the type of missingness for each
pattern. Either `"LEFT"`

, `"MID"`

, `"TAIL"`

or '`"RIGHT"`

.
If a single missingness type is entered, all patterns will be created by the same
type. If missingness types should differ over patterns, a vector of missingness
types should be entered. Default is RIGHT for all patterns and is the result of
`ampute.default.type`

.

A list containing vectors with `0`

if a case should be made missing
and `1`

if a case should remain complete. The first vector refers to the
first pattern, the second vector to the second pattern, etcetera.

#'Van Buuren, S. (2018).
*Flexible Imputation of Missing Data. Second Edition.*
Chapman & Hall/CRC. Boca Raton, FL.