# Internal functions

### Description

Internal functions, not to be called by user

### Usage

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### Arguments

`object` |
object, usually a boundary of some class |

`from` |
an object of class abparms |

`S` |
vector of number of successes |

`N` |
vector of number of trials |

`K` |
vector of number of ways to reach each bounary point |

`order` |
vector of ordering of boundary points |

`theta0` |
null value of probability of success each binary random variable |

`alternative` |
character, either 'two.sided', 'less', or 'greater' |

`ponly` |
logical, should only the specific p-value type given by alternative be calculated |

`type` |
character, type of one-sided confidence interval to calculate, either 'upper' or 'lower' |

`alpha` |
numeric, amount of error to allow on the one side of the confidence interval |

`ab` |
object of class 'abparms' |

`missN` |
numeric vector, the N values where assessments are missed |

`...` |
arguments passed to other functions, not used |

### Details

The validXX functions check that the object is a valid member of the class XX. For example, validBound checks that a bound object is OK by sum the probability distribution using the N,S, and K values and checking that it is within computer error of 1. The validity checks are run automatically by the new() function as part of the S4 implementation.

The function `abBindBothCalcK`

takes an abparms object and creates a bound object. It requires calculating K, which is the number of ways
to reach each boundary point. It ignores the `binding`

argument and assumes all boundaries are binding. The `abtoBound`

function
uses the `binding`

argument to create either a `bound`

object (for `binding`

='both') or a `boundNBF`

object otherwise.
Users can use the `as`

function to coerce an `abparms`

object to a `bound`

object.

The function `pCalc`

takes a boundary and calculates p-values, and outputs a vector of p-values (ponly=TRUE) or list of 3 vectors (plower,pupper, pval).

The function `cCalc`

takes a boundary and calculates one of the one sided confidence intervals as directed by the type argument (either 'upper' or 'lower').

The functions `analyzeBound`

and `analyzeBoundNBF`

take objects of the
`bound`

and `boundNBF`

classes and create ones of the `boundEst`

and
`boundNBFEst`

classes. This means basically that the confidence intervals and p-values
are calculated that go with those bounds.

The functions `getAlternative`

and `getTSalpha`

get those parameters from the inputs.

The function `missNAbparms`

modifies abparms objects to reflect missing assessments. This is the working function for the missN option in `modify`

.

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