Calculates population sensitivity for a large or unknown population and allowing for imperfect test sensitivity and specificity, using Binomial distribution an allowing for a variable cut-point number of positives to classify as positive

1 | ```
sep.binom.imperfect(n, c = 1, se, sp = 1, pstar)
``` |

`n` |
sample size (scalar or vector) |

`c` |
The cut-point number of positives to classify a cluster as positive, default=1, if positives < c result is negative, >= c is positive (scalar or vector of same length as n) |

`se` |
test unit sensitivity (scalar or vector of same length as n) |

`sp` |
test unit specificity, default=1 (scalar or vector of same length as n) |

`pstar` |
design prevalence as a proportion (scalar or vector of same length as n) |

a vector of population-level sensitivities

1 2 3 4 5 | ```
# examples for sep.imperfect.binom
sep.binom.imperfect(1:10*5, 2, 0.95, 0.98, 0.1)
sep.binom.imperfect(50, 1:5, 0.95, 0.98, 0.1)
sep.binom.imperfect(30, 2, 0.9, 0.98, 0.1)
sep.binom.imperfect(30, 1, 0.9, 0.98, 0.1)
``` |

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