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EnhancementNew feature or requestNew feature or requestGood first issueGood for newcomersGood for newcomersHelp wantedExtra attention is neededExtra attention is needed
Description
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 forquantiles
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 includespercentile
/nanpercentile
as a special case) (@LukeMathWalker & @jturner314) -
correlation
-methods:-
cov
(@LukeMathWalker) -One last fix to be made (Remove 'static bound from type[On hold for now]A
inCorrelationExt.cov
#3) -
corrcoef
(@LukeMathWalker - Pearson correlation #5)
-
-
histogram
-methods (@LukeMathWalker - Histogram (revisited) #9)
legokichi, Cpapa97 and pbower
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EnhancementNew feature or requestNew feature or requestGood first issueGood for newcomersGood for newcomersHelp wantedExtra attention is neededExtra attention is needed