Giuseppe Cardillo

I compared your function with the mine using this matrix:
x=[7 12; 8 2].*1000000

Using Myfisher22(x), the function written by me, the result was:
Elapsed time is 5.715080 seconds.

Using your
fexact(x(1),sum(x(:)),x(1)+x(3),x(1)+x(2)) the results were:

??? Maximum variable size allowed by the program is exceeded.

Error in ==> fexact>tri2sqind at 325
    k = (1:max_k)';
Error in ==> fexact>getLookup at 211
[k n ind] = tri2sqind(M);
Error in ==> fexact at 142
    P = getLookup( M,N,tail);

Same results reducing the complexity to
x=[7 12; 8 2].*100000 and
x=[7 12; 8 2].*10000

When I tried on x=[7 12; 8 2].*1000 the pc looped and after 15 minutes I switched it off.

Finally I tried it on x=[7 12; 8 2].*100
Elapsed time is 6.019058 seconds; using Myfisher22 on this "simple" matrix
Elapsed time is 0.008264 seconds.

So before to write "Other implementations on the file exchange are reportedly buggy and are far too slow for problems I'm working on."
I think that you should test deeper your function.

Regards

Considering that chi square is an approximation of Fisher's test, in my opinion it is completely useless to have chi square p-value. Anyway, if you want, you can simply set up it using this code:
C=sum(x,1);
R=sum(x,2);
N=sum(x(:));
[r c]=size(x);
df=(r-1)*(c-1);
Ex=R*C/N;
chi2=sum(sum(((x-Ex-0.5).^2)./Ex));
p-value=1-chi2cdf(chi2,gl)

the cyclist is right. He did a perfect explanation

Having a few statistics that MATLAB default functions do not provide is awesome especially when it's too cumbersome to write a new one. Good job!

I'm sorry, but using your software I do not get the p-value for the log-rank test that is consistent with the results of either R or Stata (both of which match). For the test data supplied with the function, I get a p-value of 0.0193, whereas you're getting a p-value of 0.01529. The KM curves are the same, but there is apparently something wrong in your logrank computation.