ramervonmises(x, 'norm')
>>> res.statistic, res.pvalue
(0.1072085112565724, 0.5508482238203407)

The p-value exceeds our chosen significance level, so we do not
reject the null hypothesis that the observed sample is drawn from the
standard normal distribution.

Now suppose we wish to check whether the same samples shifted by 2.1 is
consistent with being drawn from a normal distribution with a mean of 2.

>>> y = x + 2.1
>>> res = stats.cramervonmises(y, 'norm', args=(2,))
>>> res.statistic, res.pvalue
(0.8364446265294695, 0.00596286797008283)

Here we have used the `args` keyword to specify the mean (``loc``)
of the normal distribution to test the data against. This is equivalent
to the following, in which we create a frozen normal distribution with
mean 2.1, then pass its ``cdf`` method as an argument.

>>> frozen_dist = stats.norm(loc=2)
>>> res = stats.cramervonmises(y, frozen_dist.cdf)
>>> res.statistic, res.pvalue
(0.8364446265294695, 0.00596286797008283)

In either case, we would reject the null hypothesis that the observed
sample is drawn from a normal distribution with a mean of 2 (and default
variance of 1) because the p-value is less than our chosen
significance level.

r