#maximum Source

maximum :: Sample -> Point

Maximum value.

#minimum Source

minimum :: Sample -> Point

Minimum value.

#mean Source

mean :: Sample -> Point


#harmean Source

harmean :: Sample -> Point

Harmonic mean.

#geomean Source

geomean :: Sample -> Point

Geometric mean.

#median Source

median :: Sample -> Point


#modes Source

modes :: Sample -> Array (Tuple Int Point)

Sorted array of modes in descending order.

#mode Source

mode :: Sample -> Point

Mode for the non-empty sample.

#skew Source

skew :: Sample -> Number

Calculate skew.

#pearsonSkew Source

pearsonSkew :: Sample -> Number

Calculates pearson skew.

#stddev Source

stddev :: Sample -> Number

Standard deviation of sample.

#stddevp Source

stddevp :: Sample -> Number

Standard deviation of population.

#var Source

var :: Sample -> Number

Sample variance.

#pvar Source

pvar :: Sample -> Number

Population variance.

#centralMoment Source

centralMoment :: Int -> Sample -> Number

Central moments.

#range Source

range :: Sample -> Number


#avgdev Source

avgdev :: Sample -> Number

Average deviation.

#iqr Source

iqr :: Sample -> Number

Interquartile range.

#iqr' Source

iqr' :: Sample -> Number

Interquartile range for sorted data.

#kurt Source

kurt :: Sample -> Number


#quantile Source

quantile :: Number -> Sample -> Number

Arbitrary quantile q of an unsorted list. The quantile /q/ of /N/ data points is the point whose (zero-based) index in the sorted data set is closest to /q(N-1)/.

#quantile' Source

quantile' :: Number -> Sample -> Number

As 'quantile' specialized for sorted data.

#covMatrix Source

covMatrix :: Array Sample -> Array (Array Number)

Covariance matrix.

#pearson Source

pearson :: Sample -> Sample -> Number

Pearson's product-moment correlation coefficient.

#covar Source

covar :: Sample -> Sample -> Number

Sample Covariance.

#linreg Source

linreg :: Sample -> Sample -> Tuple3 Number Number Number

Least-squares linear regression of /y/ against /x/ for a collection of (/x/, /y/) data, in the form of (/b0/, /b1/, /r/) where the regression is /y/ = /b0/ + /b1/ * /x/ with Pearson coefficient /r/

#devsq Source

devsq :: Sample -> Number

Sum of square deviations from their sample mean.