Spearson

A poor man's stats library.

Keep in mind that I am neither a statistician nor an expert on numerical computing. This library was only created because I needed to calculate both Spearman and Pearson correlation coefficients in the browser and couldn't find anything else that does it for me.

Contributions are always welcome.

API

sort

sort(x), x = array of numbers

round

round(x, [n]), x = number to round, n = number of decimal places

min

min(x), x = array of numbers

max

max(x), x = array of numbers

range

range(start, stop) start = start number, stop = stop number

sum

sum(x), x = array of numbers

median

median(x), x = array of numbers

mean

mean(x), x = array of numbers

deviation

deviation(x), x = array of numbers

variance

variance(x, [bias]), x = array of numbers, bias = flag whether to use biased sample variance

standardDeviation

standardDeviation(x, [bias]), x = array of numbers, bias = flag whether to use biased sample variance

standardize

standardize(x), x = array of numbers

rank

rank(x), x = array of numbers

correlation

pearson

correlation.pearson(x, y, [standardize]), x = array of numbers, y = array of numbers, standarize = flag whether to standardize x and y

spearman

correlation.spearman(x, y, [rank]), x = array of numbers, y = array of numbers, rank = flag whether to rank x and y

distance

euclidean

distance.euclidean(x, y), x = array of numbers, y = array of numbers

manhattan

distance.manhattan(x, y), x = array of numbers, y = array of numbers

pairwiseDistance

pairwiseDistance(x, distanceMetric), x = array of numbers, distanceMetric = distance function (x, y)

hierarchicalClustering

hierarchicalClustering(pairwiseDistances, linkageCriterion), pairwiseDistances = pairwise distance matrix, linkageCriterion = one of single, complete, upgma