In terms of functionality, the mid-term end goal is to achieve feature parity with the statistics routine in numpy (here) and Julia StatsBase (here).

For the next version:

• Order statistics:
  • partialord version for quantiles methods;
• Histograms:
  • merge method;

For version 0.2.0:

• Order statistics:
  • optimized computations of multiple quantiles if requested all at once (Bulk quantiles #26) ;
  • argmin / argmax (Implement argmin argmax #30);
• Summary statistics:
  • harmonic mean (Means #20);
  • geometric mean (Means #20);
  • higher order central moments (Central moments #23);
  • standardized moments (they include kurtosis and skewness) (Central moments #23);
• Histograms:
  • Fix error handling (Issue: Reduce cases where histogram strategies panic #16 - PR: Histogram error handling #25 )
• Entropy:
  • Feature parity with StatsBase.jl (Entropy #24)

For version 0.1.0:

• max / nanmax (@jturner314)
• min / nanmin (@jturner314)
• quantile / nanquantile (it includes percentile / nanpercentile as a special case) (@LukeMathWalker & @jturner314)
• correlation-methods:
  • cov (@LukeMathWalker) - One last fix to be made (Remove 'static bound from type A in CorrelationExt.cov #3) [On hold for now]
  • corrcoef (@LukeMathWalker - Pearson correlation #5)
• histogram-methods (@LukeMathWalker - Histogram (revisited) #9)