# NaNStatistics

*Because* `NaN`

*is just* `missing`

*with hardware support!*

Fast (often LoopVectorization-based) summary statistics, histograms, and binning — all ignoring `NaN`

s, as if `NaN`

represented missing data.

### Summary statistics

Summary statistics exported by NaNStatistics are generally named the same as their normal counterparts, but with "nan" in front of the name, similar to the Matlab and NumPy conventions. Options include:

`nansum`

`nanmean`

`nanmedian`

`nanpctile`

`nanstd`

(standard deviation)`nanmad`

(median absolute deviation from the median)`nanaad`

(mean (average) absolute deviation from the mean)`nanminimum`

`nanmaximum`

`nanextrema`

`nanrange`

(range between nanmaximum and nanminimum)

These functions will generally support the same `dims`

keyword as their normal Julia counterparts (though are most efficient when operating on an entire collection).

```
julia> a = rand(100000);
julia> @btime minimum($a)
51.950 μs (0 allocations: 0 bytes)
7.630517166790085e-6
julia> using NaNStatistics
julia> @btime nanminimum($a)
19.690 μs (0 allocations: 0 bytes)
7.630517166790085e-6
julia> a[rand(1:100000, 10000)] .= NaN;
julia> @btime nanminimum($a)
19.663 μs (0 allocations: 0 bytes)
7.630517166790085e-6
```

### Histograms

The main histogram function is `histcounts`

(with an in-place variant `histcounts!`

), and will, as you might expect for this package, ignore NaNs. However, it might be worth using for speed even if your data don't contain any NaNs:

```
julia> b = 10 * rand(100000);
julia> using StatsBase
julia> @btime fit(Histogram, $b, 0:1:10, closed=:right)
2.633 ms (2 allocations: 224 bytes)
Histogram{Int64, 1, Tuple{StepRange{Int64, Int64}}}
edges:
0:1:10
weights: [10128, 10130, 10084, 9860, 9973, 10062, 10003, 10045, 9893, 9822]
closed: right
isdensity: false
julia> using NaNStatistics
julia> @btime histcounts($b, 0:1:10)
1.037 ms (1 allocation: 160 bytes)
10-element Vector{Int64}:
10128
10130
10084
9860
9973
10062
10003
10045
9893
9822
```

### Binning

NaNStatistics also provides functions that will efficiently calculate the summary statistics of a given dependent variable `y`

binned by an independent variable `x`

. These currently include:

`nanbinmean`

/`nanbinmean!`

`nanbinmedian`

/`nanbinmedian!`

```
julia> x = 10 * rand(100000);
julia> y = x.^2 .+ randn.();
julia> xmin, xmax, nbins = 0, 10, 10;
julia> @btime nanbinmean($x,$y,xmin,xmax,nbins)
364.082 μs (2 allocations: 320 bytes)
10-element Vector{Float64}:
0.3421697507351903
2.3065542448799015
6.322448227456871
12.340306767007629
20.353233411797074
30.347815506059405
42.31866909140384
56.32256214256441
72.35387230251672
90.35682945641588
```

### Other functions

`movmean`

There is also a simple moving average function,`movmean`

, which can operate in 1D or 2D.

```
julia> A = rand(1:10, 4,4)
4×4 Matrix{Int64}:
3 5 10 3
4 2 5 8
5 6 8 8
2 6 10 6
julia> movmean(A, 3)
4×4 Matrix{Float64}:
3.5 4.83333 5.5 6.5
4.16667 5.33333 6.11111 7.0
4.16667 5.33333 6.55556 7.5
4.75 6.16667 7.33333 8.0
```

### Room for future improvement (PRs welcome!):

- Currently,
`nanmedian`

,`nanbinmedian`

, etc. simply filter for`NaN`

s and then fall back to`Statistics.median`

. Similarly,`nanpctile`

falls back to`StatsBase.percentile`

. Adding fast pure-julia SIMD median and percentile implementations would allow for significant performance improvement. - Sufficiently high-dimensional or multidiminsional summary statistics (e.g.
`nanmean(ones(10,10,10,10), dims=(2,4))`

) could probably be made faster, and are not currently supported for`nanmedian`

or`nanpctile`