Power Analysis Module

The module imperially estimates the power of a dataset using Monte Carlo simulation. The functions take test, a function which accepts a list of sample ids or values, and returns a p-value.

In conjunction with Statsmodels, the code can be used to generate power curves, for a sufficiently large number of samples. (Depending on the effect size, about 20 - 30 in each group).

I will move the statsmodel dependent example into an ipython notebook and either keep it in one of my repositories or else submit to Greg's Introduction to Applied Bioinformatics, unless statsmodel can be added.

*This code likely needs to be optimized, given the iterative nature*
I tried to update this documentation. Because this uses pandas `groupby`, it automatically ignores NaNs that appear in `cat`. You can have a value in `cats` that is defined, but is missing a value in one or more of the columns within `control_cats`. `strict` was put in place as a name to reflect a strict match, where values had to be defined and the same to be matched, while `strict = False` lets you match to undefined individuals in a given column in `control_cats`