| Type: | Package | 
| Title: | Anderson-Darling GoF test | 
| Version: | 0.3 | 
| Date: | 2011-12-28 | 
| Author: | Carlos J. Gil Bellosta | 
| Maintainer: | Carlos J. Gil Bellosta <cgb@datanalytics.com> | 
| Description: | Anderson-Darling GoF test with p-value calculation based on Marsaglia's 2004 paper "Evaluating the Anderson-Darling Distribution" | 
| License: | GPL-2 | GPL-3 [expanded from: GPL] | 
| LazyLoad: | yes | 
| Packaged: | 2011-12-28 01:41:10 UTC; carlos | 
| Repository: | CRAN | 
| Date/Publication: | 2011-12-28 13:50:19 | 
| NeedsCompilation: | no | 
Implementation of the Anderson-Darling goodness of fit test.
Description
Implementation of the Anderson-Darling goodness of fit test.
Details
| Package: | ADGofTest | 
| Type: | Package | 
| Version: | 0.1 | 
| Date: | 2009-06-26 | 
| License: | GPL | 
| LazyLoad: | yes | 
Author(s)
Carlos J. Gil Bellosta
Maintainer: Carlos J. Gil Bellosta <cjgb@datanalytics.com>
References
G. and J. Marsaglia, "Evaluating the Anderson-Darling Distribution", Journal of Statistical Software, 2004
Anderson-Darling GoF test
Description
Implementation of the Anderson-Darling goodness of fit test.
Usage
ad.test(x, distr.fun, ...)
Arguments
| x | a random sample from a possibly unknown continuous distribution | 
| distr.fun |  a named CDF, such as  | 
| ... | extra parameters for the distribution function above, such as location and scale parameters, etc. | 
Details
If the distr.fun is provided, the function checks whether x is a iid sample from the distribution described by such CDF.
Otherwise, whether they follow a uniform law.
Value
The output is an object of the class htest exactly like for the Kolmogorov-Smirnov test, ks.test. 
The statistic and p.value fields are the most relevant ones.
Author(s)
Carlos J. Gil Bellosta
References
G. and J. Marsaglia, "Evaluating the Anderson-Darling Distribution", Journal of Statistical Software, 2004
Examples
    set.seed( 123 )
    x <- runif( 100 )
    ad.test( x )$p.value
    ad.test( x, pnorm, 0, 1 )$p.value
    replicate( ad.test( rnorm( 100 ), pnorm )$p.value, 100 )