Adding tau-a to scipy.stats.kendalltau variants and then changing Somers' D calculation to using tau-a instead of crosstab for better significant runtime improvements

Hello, 

this is my first time trying to contribute, so please be not too harsh. 

When I recently used the scipy.stats.somersd function on larger data I experienced quite some runtime problems. I found a way to calculate Somers' D in an equivalent manner by using D(Y|X) = tau_a(X, Y)/tau_a(X, X), for which I added the support for variant "a" to the scipy.stats.kendalltau function. The runtime improvement was significant for large datasets where this approach achieved approx. 30 times faster runtimes. I believe the reason for this runtime improvement is due to the crosstab calculation in the current setup, while kendalltau uses for the disconcordant measures a cypthon implementation making it much faster. 

Would be great to have someone I could ask if I have questions in the process of submitting my contribution and maybe to also review my code. 

Thanks a lot and best regards coming from Vienna 

Paul
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