Information-theoretic subset selection

This new feature adds information theoretic subset selection methods for biological data formats using the [PyFEAST](https://github.com/EESI/PyFeast) module. We have a pull request open, but it was closed several months ago. We have made some adjustments to the code and updated the versioning information as well.   
BIOM 2.0 has some API changes that will impact this code and is likely part of the source of conflict. This method though can be rewritten as:

```python
from biom import load_table
biom_table = load_table(biom_f)
sample_ids = biom_table.ids().tolist() # is a list more useful than a numpy array?
variable_names = biom_table.ids(axis='observation')

# the code is creating a dense representation of the table, which is expensive in time and space. Is it possible for fizzy to use sparse? (if so you, you can access the scipy sparse matrix via biom_table.matrix_data
data_matrix = asarray([d for d in biom_table.iter_data(dense=True)]) 

# no need for transpose as the data were pulled out over the samples instead of observations
return data_matrix, variable_names, sample_ids
```