title: Is there an Anderson-Darling implementation for python that returns p-value?
body: <p>I want to find the distribution that best fit some data. This would typically be some sort of measurement data, for instance force or torque.</p>
  
  <p>Ideally I want to run Anderson-Darling with multiple distributions and select the distribution with the highest p-value. This would be similar to the 'Goodness of fit' test in <em>Minitab</em>. I am having trouble finding a python implementation of Anderson-Darling that calculates the p-value.</p>
  
  <p>I have tried <em>scipy's</em> <code>stats.anderson()</code> but it only returns the AD-statistic and a list of critical values with the corresponding significance levels, not the p-value itself.</p>
  
  <p>I have also looked into <code>statsmodels</code>, but it seems to only support the normal distribution. I need to compare the fit of several distributions (normal, weibull, lognormal etc.).</p>
  
  <p>Is there an implementation of the Anderson-Darling in python that returns p-value and supports nonnormal distributions?</p>