Incremental Functions Update the stats with the given value.

Assertion functions used for tests check that the value x equals the expected value y

The kurtosis functions return _excess_ kurtosis, so that the kurtosis of a normal distribution = 0.0. Then kurtosis < 0.0 indicates platykurtic (flat)

Accessor Functions

Incremental Functions Update the stats with a new point.

Batch Functions

Accessor Functions

The kurtosis functions return _excess_ kurtosis

Batch functions These are non-incremental functions that operate only on the data given them. They're prefixed with 'Calc'.

Update the stats with the given array of values.

Update the stats with arrays of x and y values.

benchmark on 10 values, so divide by 10 to estimate the per-value time for array calculations

Find the time to calculate Sample Kurtosis on an input array 100k random values. The benchmark will repeat this test b.N times to determine a stable t

Test the incremental Variance function by itself. This result is how fast the Variance is calculated _including_ the time to incrementally update the

Test the incremental Variance function by itself. This result is how fast the Variance is calculated not including the time to incrementally update th

Benchmark tests run with: go test stats.go stats_test.go -bench="Benchmark"

Test the batch functions. Calculate the descriptive stats on the whole array.

Test batch functions

Degenerate examples tests

Test incremental functions

Incremental stats tests With no updates, these are the results on initialization

Degenerate examples tests Update() 1 0 value

Update() 2 0 values

Update() 3 0 values.

Update() 4 0 values.

Update() 1 value

Update() 2 values

Update() 3 values.

Update() 4 values. Now all of the statistics are available.

Update by array. In this case, we use slices to update via half of the array at a time.