RunningStatistics

Online (single pass) algorithms for statistical measures based on the Julia package OnlineStats.jl by Josh Day. Useful for streaming and big data.

This package is available on NuGet

Supports .NET Standard 2.0

List of Statistics

Statistic	Description
Mean	The univariate mean
Sum	The overall sum of double observations
Sum<T>	The overall sum of any type that implements IAdditionOperators and IAdditiveIdentity
Variance	The univariate variance
Extrema	The min and max of double observations
Extrema<T>	The min and max of any type that implements IComparable<T>
Moments	The Mean, Variance, Skewness, and (excess) Kurtosis
EmpiricalCdf	Approximate order statistics (quantiles)
CountMap<T>	Counts for each unique value
Histogram	A histogram with specified bin edges

List of Distributions

Distribution	Description
Normal	The univariate mean and variance
Beta	The number of successes and failures

Common Interface

All running statistics implement the following interfaces:

public interface IRunningStatistic<TObs>
{
    public long Nobs { get; }
    
    public void Fit(TObs value);
    
    public void Fit(TObs value, long count);

    public void Fit(IEnumerable<TObs> values);
    
    public void Fit(IEnumerable<KeyValuePair<TObs, long>> keyValuePairs);

    public void Reset();
    
    public IRunningStatistic<TObs> CloneEmpty();
    
    public IRunningStatistic<TObs> Clone();
    
    public void UnsafeMerge(IRunningStatistic<TObs> other);
}

public interface IRunningStatistic<TObs, TSelf> : IRunningStatistic<TObs> 
    where TSelf : IRunningStatistic<TObs, TSelf>
{
    public TSelf CloneEmpty();

    public TSelf Clone();
    
    public void Merge(TSelf other);
}

The interface is split into a base interface IRunningStatistic<TObs> and a derived interface IRunningStatistic<TObs, TSelf>. The base interface contains the generic methods, while the derived interface contains more type information. The base interface allows for collections of statistics that can be fit to the same observation type.

Abstract Base Class

The abstract class RunningStatisticBase implements the IRunningStatistic<TObs, TSelf> interface and provides default implementations when possible.

Examples

See the RunningStatistics.Examples project for more examples.

Basic Usage

using RunningStatistics;

var mean1 = new Mean();
var mean2 = new Mean();
var ecdf = new EmpiricalCdf();

var rng = new Random();
for (var i = 0; i < 1000; i++)
{
    var x = rng.NextDouble();
    
    mean1.Fit(x);
    mean2.Fit(2*x);
    
    ecdf.Fit(x);
}

mean1.Merge(mean2);
var q1 = ecdf.Quantile(0.25);