# ciCASx: Continuity corrected ArcSine method of CI estimation In proportion: Inference on Single Binomial Proportion and Bayesian Computations

## Description

Continuity corrected ArcSine method of CI estimation

## Usage

 `1` ```ciCASx(x, n, alp, c) ```

## Arguments

 `x` - Number of successes `n` - Number of trials `alp` - Alpha value (significance level required) `c` - Continuity correction

## Details

Wald-type interval for the arcsine transformation using the test statistic (abs(sin^(-1)phat-sin^(-1)p)-c)/SE where c > 0 is a constant for continuity correction and for all x = 0, 1, 2 ..n

## Value

A dataframe with

 `x` Number of successes (positive samples) `LCAx ` ArcSine Lower limit `UCAx ` ArcSine Upper Limit `LABB ` ArcSine Lower Abberation `UABB ` ArcSine Upper Abberation `ZWI ` Zero Width Interval

## References

[1] 1998 Agresti A and Coull BA. Approximate is better than "Exact" for interval estimation of binomial proportions. The American Statistician: 52; 119 - 126.

[2] 1998 Newcombe RG. Two-sided confidence intervals for the single proportion: Comparison of seven methods. Statistics in Medicine: 17; 857 - 872.

[3] 2008 Pires, A.M., Amado, C. Interval Estimators for a Binomial Proportion: Comparison of Twenty Methods. REVSTAT - Statistical Journal, 6, 165-197.

`prop.test and binom.test` for equivalent base Stats R functionality, `binom.confint` provides similar functionality for 11 methods, `wald2ci` which provides multiple functions for CI calculation , `binom.blaker.limits` which calculates Blaker CI which is not covered here and `propCI` which provides similar functionality.
 ```1 2``` ```x=5; n=5; alp=0.05;c=1/2*n ciCASx(x,n,alp,c) ```