DOC+RF+BF: use "ds" as variable, and deal properly with samples that have no prediction
RF: use args for cross-val measure directly in classifier as well
RF: simplified writing FT data
RF+BK: change order of haystack and needle
RF+ENH: support matlab FT structs directly
NF+RF: support subsequent n-fold partitioning scheme on other features
RF+ENH: properly deal with output from dataset measure, and added some checks early on to crash if output is unexpected
RF+BK: remove support for selecting center_ids as this runs pretty fast and adds a lot of headaches when using this in a searchlight later
SML+RF: faster check for dimension size
ENH: properly deal with cases of no winner
NF: partition balancer
NF: chunkize function to set chunks in best-possible balanced fashion
NF: selective naive bayesian classifier, based on Langly & Sage, Induction of Selective Bayesian Classifiers
NF: feature dimension match function
NF: feature dimension pruning function (mostly for MEEG)