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Spearson
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Stats library for Node.js and browsers
Download README (.md)
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Download README
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README.md
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Spearson
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Spearson ======== [](http://www.repostatus.org/#unsupported) [](http://travis-ci.org/agrueneberg/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`
