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\name{computeTestAndersonDarlingNormal}
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\alias{computeTestAndersonDarlingNormal}
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\title{Compute the Anderson-Darling test for Normal Distribution.}
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This ROT function, called from a Test C++ object, is given a sample,
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and optionnaly a test level. It then returns the result of a A-D test
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against the null hypothesis that the sample has un underlying Normal
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distribution and returns a list containing the result and test p-value.
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computeTestAndersonDarlingNormal(numericalSample, testLevel = 0.95)
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\item{numericalSample}{the sample to be tested (numeric vector)}
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\item{testLevel}{the test level. (scalar in [0:1])}
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A list is returned, containing :
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\item{testResult}{The result. 1 means H0 is not rejected. (scalar)}
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\item{threshold}{The threshold applied to the p-value when deciding the outcome of the test.}
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\item{pValue}{The test p-value. (scalar)}
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\author{Pierre-Matthieu Pair, Softia for EDF.}
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# Standard Normal distribution example.
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print(computeTestAndersonDarlingNormal(rnorm(1000, 3, 1.5)))
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print(computeTestAndersonDarlingNormal(rexp(1000, 2.5)))
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\keyword{distribution}