stats #

fn absdev[T](t &vtl.Tensor[T]) T

Measure of Dispersion / Spread Mean Absolute Deviation of the given input array Based on https://en.wikipedia.org/wiki/Average_absolute_deviation

fn absdev_mean[T](t &vtl.Tensor[T], provided_mean T) T

Measure of Dispersion / Spread Mean Absolute Deviation of the given input array Based on https://en.wikipedia.org/wiki/Average_absolute_deviation

fn covariance[T](a &vtl.Tensor[T], b &vtl.Tensor[T]) T

fn covariance_mean[T](a &vtl.Tensor[T], b &vtl.Tensor[T], mean1 T, mean2 T) T

Compute the covariance of a dataset using the recurrence relation

fn freq[T](t &vtl.Tensor[T], val T) int

Measure of Occurrence Frequency of a given number Based on https://www.mathsisfun.com/data/frequency-distribution.html

fn geometric_mean[T](t &vtl.Tensor[T]) T

Measure of Central Tendency Geometric Mean of the given input array Based on https://www.mathsisfun.com/numbers/geometric-mean.html

fn harmonic_mean[T](t &vtl.Tensor[T]) T

Measure of Central Tendency Harmonic Mean of the given input array Based on https://www.mathsisfun.com/numbers/harmonic-mean.html

fn kurtosis[T](t &vtl.Tensor[T]) T

fn kurtosis_mean_stddev[T](t &vtl.Tensor[T], mean T, sd T) T

Takes a dataset and finds the kurtosis using the fourth moment the deviations, normalized by the sd

fn lag1_autocorrelation[T](t &vtl.Tensor[T]) T

fn lag1_autocorrelation_mean[T](t &vtl.Tensor[T], provided_mean T) T

Compute the lag-1 autocorrelation of a dataset using the recurrence relation

fn max[T](t &vtl.Tensor[T]) T

Maximum of the given input array

fn max_index[T](t &vtl.Tensor[T]) int

Maximum of the given input array

fn mean[T](t &vtl.Tensor[T]) T

Measure of Central Tendency Mean of the given input array Based on https://www.mathsisfun.com/data/central-measures.html

fn median[T](t &vtl.Tensor[T]) T

Measure of Central Tendency Median of the given input array ( input array is assumed to be sorted ) Based on https://www.mathsisfun.com/data/central-measures.html

fn min[T](t &vtl.Tensor[T]) T

Minimum of the given input array

fn min_index[T](t &vtl.Tensor[T]) int

Minimum of the given input array

fn minmax[T](t &vtl.Tensor[T]) (T, T)

Minimum and maximum of the given input array

fn minmax_index[T](t &vtl.Tensor[T]) (int, int)

Minimum and maximum of the given input array

fn mode[T](t &vtl.Tensor[T]) T

Measure of Central Tendency Mode of the given input array Based on https://www.mathsisfun.com/data/central-measures.html

fn population_stddev[T](t &vtl.Tensor[T]) T

Measure of Dispersion / Spread Population Standard Deviation of the given input array Based on https://www.mathsisfun.com/data/standard-deviation.html

fn population_stddev_mean[T](t &vtl.Tensor[T], mean T) T

Measure of Dispersion / Spread Population Standard Deviation of the given input array Based on https://www.mathsisfun.com/data/standard-deviation.html

fn population_variance[T](t &vtl.Tensor[T]) T

Measure of Dispersion / Spread Population Variance of the given input array Based on https://www.mathsisfun.com/data/standard-deviation.html

fn population_variance_mean[T](t &vtl.Tensor[T], provided_mean T) T

Measure of Dispersion / Spread Population Variance of the given input array Based on https://www.mathsisfun.com/data/standard-deviation.html

fn prod[T](t &vtl.Tensor[T]) T

prod returns the product of all elements of the given tensor

fn prod_axis[T](t &vtl.Tensor[T], data AxisData) T

prod_axis_dims returns the product of a given Tensor along a provided axis with the reduced dimension intact

fn prod_axis_with_dims[T](t &vtl.Tensor[T], data AxisData) T

prod_axis_dims returns the product of a Tensor along a provided axis with the reduced dimension intact

fn quantile[T](sorted_t &vtl.Tensor[T], f T) T

fn range[T](t &vtl.Tensor[T]) T

Measure of Dispersion / Spread Range ( Maximum - Minimum ) of the given input array Based on https://www.mathsisfun.com/data/range.html

fn rms[T](t &vtl.Tensor[T]) T

Root Mean Square of the given input array Based on https://en.wikipedia.org/wiki/Root_mean_square

fn sample_stddev[T](t &vtl.Tensor[T]) T

Measure of Dispersion / Spread Sample Standard Deviation of the given input array Based on https://www.mathsisfun.com/data/standard-deviation.html

fn sample_stddev_mean[T](t &vtl.Tensor[T], mean T) T

Measure of Dispersion / Spread Sample Standard Deviation of the given input array Based on https://www.mathsisfun.com/data/standard-deviation.html

fn sample_variance[T](t &vtl.Tensor[T]) T

Measure of Dispersion / Spread Sample Variance of the given input array Based on https://www.mathsisfun.com/data/standard-deviation.html

fn sample_variance_mean[T](t &vtl.Tensor[T], provided_mean T) T

Measure of Dispersion / Spread Sample Variance of the given input array Based on https://www.mathsisfun.com/data/standard-deviation.html

fn skew[T](t &vtl.Tensor[T]) T

fn skew_mean_stddev[T](t &vtl.Tensor[T], mean T, sd T) T

fn sum[T](t &vtl.Tensor[T]) T

sum returns the sum of all elements of the given tensor

fn sum_axis[T](t &vtl.Tensor[T], data AxisData) T

sum_axis returns the sum of a given Tensor along a provided axis

fn sum_axis_with_dims[T](t &vtl.Tensor[T], data AxisData) T

sum_axis_dims returns the sum of a given Tensor along a provided axis with the reduced dimension intact

fn tss[T](t &vtl.Tensor[T]) T

Sum of squares

fn tss_mean[T](t &vtl.Tensor[T], provided_mean T) T

Sum of squares about the mean

struct AxisData {
pub:
	axis int
}