Hi andye,

It looks like it may be a trade-off between calling R or coding the algorithm yourself, and a smashing opportunity to write a module:-). Among the options for tests of normality you might consider Shapiro-Wilk, (there is a link to a Fortran version of the algorithm in the Wikipedia article), and the popular Kolmogorov-Smirnov test. The K-S is generalizable to many distributions, but may be more of a pain to implement than Shapiro-Wilk. I would recommend steering clear of the Anderson-Darling test, as it is overly sensitive with sample sizes greater than about 25 (as mentioned in Wikipedia).

If you do roll your own, PDL is great for stats.

In reply to Re: Stats: Testing whether data is normally (Gaussian) distributed by moklevat
in thread Stats: Testing whether data is normally (Gaussian) distributed by andye

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