intLik_p: ILR CI for misclassification parameter p
In BriceonWiley/IntegratedLikelihood.R:

Description Usage Arguments Value Note Author(s) Examples

Function that produces ILR (integrated likelihood ratio) CI (confidence interval) for the misclassification parameter p when the data are subject to a double sampling scheme as seen in __add citation__.

intLik_p(
  n00 = 49, n01 = 1, n10 = 1, n11 = 2, X = 14, Y = 433,
  alpha = 0.05, closed_form = FALSE
)

`n00`	the number of true negative counts in the substudy, where the infallible classifier is the first index and the fallible classifier is the second index.
`n01`	the number of false positive counts in the substudy, where the infallible classifier is the first index and the fallible classifier is the second index.
`n10`	the number of false negative counts in the substudy, where the infallible classifier is the first index and the fallible classifier is the second index.
`n11`	the number of true positive counts in the substudy, where the infallible classifier is the first index and the fallible classifier is the second index.
`X`	the number of positive counts in the main study classified by only the fallible classifier.
`Y`	the number of negative counts in the main study classified by only the fallible classifier.
`alpha`	calculated as 1 - confidence level; this is the probability of a type error for the corresponding hypothesis test.
`closed_form`	an argument that allows for the closed form of the ILR CI to be used rather than numerical integration.

The function returns an ILR CI in the form of a two column tibble with the lower bound as the first column and the upper bound as the second.

Due to the nature of the likelihood function that is used for the interval, samples that return invalid MLEs (can be determined using produce_samples()) will produce (NaN, NaN) intervals.

Briceon Wiley

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (x)
{
  }