## Aliases

This section is empty.

## Constants

This section is empty.

## Sum types

This section is empty.

## Functions

`fn absdev_mean[T](data []T, mean T) T`

absdev_mean calculates the average distance between each data point and the provided mean Based on https://en.wikipedia.org/wiki/Average_absolute_deviation

`fn covariance[T](data1 []T, data2 []T) T`

covariance calculates directional association between datasets positive value denotes variables move in same direction and negative denotes variables move in opposite directions

`fn covariance_mean[T](data1 []T, data2 []T, mean1 T, mean2 T) T`

covariance_mean computes the covariance of a dataset with means provided the recurrence relation

`fn freq[T](data []T, val T) int`

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

`fn geometric_mean[T](data []T) T`

geometric_mean calculates the central tendency of the given input array, product(data)**1/data.len Based on https://www.mathsisfun.com/numbers/geometric-mean.html

`fn harmonic_mean[T](data []T) T`

harmonic_mean calculates the reciprocal of the average of reciprocals of the given input array Based on https://www.mathsisfun.com/numbers/harmonic-mean.html

`fn kurtosis[T](data []T) T`

kurtosis calculates the measure of the 'tailedness' of the data by finding mean and standard of deviation

`fn kurtosis_mean_stddev[T](data []T, mean T, sd T) T`

kurtosis_mean_stddev calculates the measure of the 'tailedness' of the data using the fourth moment the deviations, normalized by the sd

`fn lag1_autocorrelation[T](data []T) T`

lag1_autocorrelation_mean calculates the correlation between values that are one time period apart of a dataset, based on the mean

`fn lag1_autocorrelation_mean[T](data []T, mean T) T`

lag1_autocorrelation_mean calculates the correlation between values that are one time period apart of a dataset, using the recurrence relation

`fn mean[T](data []T) T`

mean calculates the average of the given input array, sum(data)/data.len Based on https://www.mathsisfun.com/data/central-measures.html

`fn median[T](sorted_data []T) T`

median returns the middlemost value of the given input array ( input array is assumed to be sorted ) Based on https://www.mathsisfun.com/data/central-measures.html

`fn minmax[T](data []T) (T, T)`

minmax finds the minimum and maximum value from the dataset

minmax_index finds the first index of the minimum and maximum value

`fn mode[T](data []T) T`

mode calculates the highest occuring value of the given input array Based on https://www.mathsisfun.com/data/central-measures.html

`fn population_stddev[T](data []T) T`

population_stddev calculates how spread out the dataset is Based on https://www.mathsisfun.com/data/standard-deviation.html

`fn population_stddev_mean[T](data []T, mean T) T`

population_stddev_mean calculates how spread out the dataset is, with the provide mean Based on https://www.mathsisfun.com/data/standard-deviation.html

`fn population_variance[T](data []T) T`

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

`fn population_variance_mean[T](data []T, mean T) T`

population_variance_mean is the Measure of Dispersion / Spread of the given input array, with the provided mean Based on https://www.mathsisfun.com/data/standard-deviation.html

`fn range[T](data []T) T`

range calculates the difference between the min and max Range ( Maximum - Minimum ) of the given input array Based on https://www.mathsisfun.com/data/range.html

`fn rms[T](data []T) T`

rms, Root Mean Square, calculates the sqrt of the mean of the squares of the given input array Based on https://en.wikipedia.org/wiki/Root_mean_square

`fn sample_stddev[T](data []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](data []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](data []T) T`

sample_variance calculates the spread of dataset around the mean Based on https://www.mathsisfun.com/data/standard-deviation.html

`fn sample_variance_mean[T](data []T, mean T) T`

sample_variance calculates the spread of dataset around the provided mean Based on https://www.mathsisfun.com/data/standard-deviation.html

`fn skew[T](data []T) T`

skew calculates the mean and standard of deviation to find the skew from the data

`fn skew_mean_stddev[T](data []T, mean T, sd T) T`

skew_mean_stddev calculates the skewness of data

`fn tss[T](data []T) T`

tts, Sum of squares, calculates the sum over all squared differences between values and overall mean

## Structs

This section is empty.

## Interfaces

This section is empty.

## Enums

This section is empty.

absdev calculates the average distance between each data point and the mean Based on https://en.wikipedia.org/wiki/Average_absolute_deviation