Calculates optimum sample size and cut-point number of positives to achieve specified population sensitivity, for given population size and other parameters, using freecalc algorithm, all paramaters must be scalars

1 | ```
n.c.freecalc(N, sep = 0.95, c = 1, se, sp = 1, pstar, minSpH = 0.95)
``` |

`N` |
population size |

`sep` |
target population sensitivity |

`c` |
The maximum allowed cut-point number of positives to classify a cluster as positive, default=1, if positives < c result is negative, >= c is positive |

`se` |
test unit sensitivity |

`sp` |
test unit specificity, default=1 |

`pstar` |
design prevalence as a proportion or integer (number of infected units) |

`minSpH` |
minimium desired population specificity |

a list of 3 elements, a dataframe with 1 row and six columns for the recommended sample size and corresponding values for population sensitivity (SeP), population specificity (SpP), N, c and pstar, a vector of SeP values and a vector of SpP values, for n = 1:N

1 2 3 | ```
# examples for n.c.hp
n.c.freecalc(120,0.95,c=5,se=0.9,sp=0.99,pstar=0.1, minSpH=0.9)[[1]]
n.c.freecalc(65,0.95,c=5,se=0.95,sp=0.99,pstar=0.05, minSpH=0.9)
``` |

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