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.