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+(function(f){if(typeof exports==="object"&&typeof module!=="undefined"){module.exports=f()}else if(typeof define==="function"&&define.amd){define([],f)}else{var g;if(typeof window!=="undefined"){g=window}else if(typeof global!=="undefined"){g=global}else if(typeof self!=="undefined"){g=self}else{g=this}g.ss = f()}})(function(){var define,module,exports;return (function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s})({1:[function(require,module,exports){
+/* @flow */
+'use strict';
+// # simple-statistics
+//
+// A simple, literate statistics system.
+var ss = module.exports = {};
+// Linear Regression
+ss.linearRegression = require(21);
+ss.linearRegressionLine = require(22);
+ss.standardDeviation = require(54);
+ss.rSquared = require(43);
+ss.mode = require(32);
+ss.modeSorted = require(33);
+ss.min = require(29);
+ss.max = require(23);
+ss.minSorted = require(30);
+ss.maxSorted = require(24);
+ss.sum = require(56);
+ss.sumSimple = require(58);
+ss.product = require(39);
+ss.quantile = require(40);
+ss.quantileSorted = require(41);
+ss.iqr = ss.interquartileRange = require(19);
+ss.medianAbsoluteDeviation = ss.mad = require(27);
+ss.chunk = require(8);
+ss.shuffle = require(51);
+ss.shuffleInPlace = require(52);
+ss.sample = require(45);
+ss.ckmeans = require(9);
+ss.uniqueCountSorted = require(61);
+ss.sumNthPowerDeviations = require(57);
+ss.equalIntervalBreaks = require(14);
+// sample statistics
+ss.sampleCovariance = require(47);
+ss.sampleCorrelation = require(46);
+ss.sampleVariance = require(50);
+ss.sampleStandardDeviation = require(49);
+ss.sampleSkewness = require(48);
+// combinatorics
+ss.permutationsHeap = require(36);
+ss.combinations = require(10);
+ss.combinationsReplacement = require(11);
+// measures of centrality
+ss.geometricMean = require(17);
+ss.harmonicMean = require(18);
+ss.mean = ss.average = require(25);
+ss.median = require(26);
+ss.medianSorted = require(28);
+ss.rootMeanSquare = ss.rms = require(44);
+ss.variance = require(62);
+ss.tTest = require(59);
+ss.tTestTwoSample = require(60);
+// ss.jenks = require('./src/jenks');
+// Classifiers
+ss.bayesian = require(2);
+ss.perceptron = require(35);
+// Distribution-related methods
+ss.epsilon = require(13); // We make ε available to the test suite.
+ss.factorial = require(16);
+ss.bernoulliDistribution = require(3);
+ss.binomialDistribution = require(4);
+ss.poissonDistribution = require(37);
+ss.chiSquaredGoodnessOfFit = require(7);
+// Normal distribution
+ss.zScore = require(63);
+ss.cumulativeStdNormalProbability = require(12);
+ss.standardNormalTable = require(55);
+ss.errorFunction = ss.erf = require(15);
+ss.inverseErrorFunction = require(20);
+ss.probit = require(38);
+ss.mixin = require(31);
+// Root-finding methods
+ss.bisect = require(5);
+},{"10":10,"11":11,"12":12,"13":13,"14":14,"15":15,"16":16,"17":17,"18":18,"19":19,"2":2,"20":20,"21":21,"22":22,"23":23,"24":24,"25":25,"26":26,"27":27,"28":28,"29":29,"3":3,"30":30,"31":31,"32":32,"33":33,"35":35,"36":36,"37":37,"38":38,"39":39,"4":4,"40":40,"41":41,"43":43,"44":44,"45":45,"46":46,"47":47,"48":48,"49":49,"5":5,"50":50,"51":51,"52":52,"54":54,"55":55,"56":56,"57":57,"58":58,"59":59,"60":60,"61":61,"62":62,"63":63,"7":7,"8":8,"9":9}],2:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* [Bayesian Classifier](http://en.wikipedia.org/wiki/Naive_Bayes_classifier)
+*
+* This is a naïve bayesian classifier that takes
+* singly-nested objects.
+*
+* @class
+* @example
+* var bayes = new BayesianClassifier();
+* bayes.train({
+*   species: 'Cat'
+* }, 'animal');
+* var result = bayes.score({
+*   species: 'Cat'
+* })
+* // result
+* // {
+* //   animal: 1
+* // }
+*/
+function BayesianClassifier() {
+// The number of items that are currently
+// classified in the model
+this.totalCount = 0;
+// Every item classified in the model
+this.data = {};
+}
+/**
+* Train the classifier with a new item, which has a single
+* dimension of Javascript literal keys and values.
+*
+* @param {Object} item an object with singly-deep properties
+* @param {string} category the category this item belongs to
+* @return {undefined} adds the item to the classifier
+*/
+BayesianClassifier.prototype.train = function(item, category) {
+// If the data object doesn't have any values
+// for this category, create a new object for it.
+if (!this.data[category]) {
+this.data[category] = {};
+}
+// Iterate through each key in the item.
+for (var k in item) {
+var v = item[k];
+// Initialize the nested object `data[category][k][item[k]]`
+// with an object of keys that equal 0.
+if (this.data[category][k] === undefined) {
+this.data[category][k] = {};
+}
+if (this.data[category][k][v] === undefined) {
+this.data[category][k][v] = 0;
+}
+// And increment the key for this key/value combination.
+this.data[category][k][v]++;
+}
+// Increment the number of items classified
+this.totalCount++;
+};
+/**
+* Generate a score of how well this item matches all
+* possible categories based on its attributes
+*
+* @param {Object} item an item in the same format as with train
+* @returns {Object} of probabilities that this item belongs to a
+* given category.
+*/
+BayesianClassifier.prototype.score = function(item) {
+// Initialize an empty array of odds per category.
+var odds = {}, category;
+// Iterate through each key in the item,
+// then iterate through each category that has been used
+// in previous calls to `.train()`
+for (var k in item) {
+var v = item[k];
+for (category in this.data) {
+// Create an empty object for storing key - value combinations
+// for this category.
+odds[category] = {};
+// If this item doesn't even have a property, it counts for nothing,
+// but if it does have the property that we're looking for from
+// the item to categorize, it counts based on how popular it is
+// versus the whole population.
+if (this.data[category][k]) {
+odds[category][k + '_' + v] = (this.data[category][k][v] || 0) / this.totalCount;
+} else {
+odds[category][k + '_' + v] = 0;
+}
+}
+}
+// Set up a new object that will contain sums of these odds by category
+var oddsSums = {};
+for (category in odds) {
+// Tally all of the odds for each category-combination pair -
+// the non-existence of a category does not add anything to the
+// score.
+oddsSums[category] = 0;
+for (var combination in odds[category]) {
+oddsSums[category] += odds[category][combination];
+}
+}
+return oddsSums;
+};
+module.exports = BayesianClassifier;
+},{}],3:[function(require,module,exports){
+'use strict';
+/* @flow */
+var binomialDistribution = require(4);
+/**
+* The [Bernoulli distribution](http://en.wikipedia.org/wiki/Bernoulli_distribution)
+* is the probability discrete
+* distribution of a random variable which takes value 1 with success
+* probability `p` and value 0 with failure
+* probability `q` = 1 - `p`. It can be used, for example, to represent the
+* toss of a coin, where "1" is defined to mean "heads" and "0" is defined
+* to mean "tails" (or vice versa). It is
+* a special case of a Binomial Distribution
+* where `n` = 1.
+*
+* @param {number} p input value, between 0 and 1 inclusive
+* @returns {number} value of bernoulli distribution at this point
+* @example
+* bernoulliDistribution(0.5); // => { '0': 0.5, '1': 0.5 }
+*/
+function bernoulliDistribution(p/*: number */) {
+// Check that `p` is a valid probability (0 ≤ p ≤ 1)
+if (p < 0 || p > 1 ) { return NaN; }
+return binomialDistribution(1, p);
+}
+module.exports = bernoulliDistribution;
+},{"4":4}],4:[function(require,module,exports){
+'use strict';
+/* @flow */
+var epsilon = require(13);
+var factorial = require(16);
+/**
+* The [Binomial Distribution](http://en.wikipedia.org/wiki/Binomial_distribution) is the discrete probability
+* distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields
+* success with probability `probability`. Such a success/failure experiment is also called a Bernoulli experiment or
+* Bernoulli trial; when trials = 1, the Binomial Distribution is a Bernoulli Distribution.
+*
+* @param {number} trials number of trials to simulate
+* @param {number} probability
+* @returns {Object} output
+*/
+function binomialDistribution(
+trials/*: number */,
+probability/*: number */)/*: ?Object */ {
+// Check that `p` is a valid probability (0 ≤ p ≤ 1),
+// that `n` is an integer, strictly positive.
+if (probability < 0 || probability > 1 ||
+trials <= 0 || trials % 1 !== 0) {
+return undefined;
+}
+// We initialize `x`, the random variable, and `accumulator`, an accumulator
+// for the cumulative distribution function to 0. `distribution_functions`
+// is the object we'll return with the `probability_of_x` and the
+// `cumulativeProbability_of_x`, as well as the calculated mean &
+// variance. We iterate until the `cumulativeProbability_of_x` is
+// within `epsilon` of 1.0.
+var x = 0,
+cumulativeProbability = 0,
+cells = {};
+// This algorithm iterates through each potential outcome,
+// until the `cumulativeProbability` is very close to 1, at
+// which point we've defined the vast majority of outcomes
+do {
+// a [probability mass function](https://en.wikipedia.org/wiki/Probability_mass_function)
+cells[x] = factorial(trials) /
+(factorial(x) * factorial(trials - x)) *
+(Math.pow(probability, x) * Math.pow(1 - probability, trials - x));
+cumulativeProbability += cells[x];
+x++;
+// when the cumulativeProbability is nearly 1, we've calculated
+// the useful range of this distribution
+} while (cumulativeProbability < 1 - epsilon);
+return cells;
+}
+module.exports = binomialDistribution;
+},{"13":13,"16":16}],5:[function(require,module,exports){
+'use strict';
+/* @flow */
+var sign = require(53);
+/**
+* [Bisection method](https://en.wikipedia.org/wiki/Bisection_method) is a root-finding
+* method that repeatedly bisects an interval to find the root.
+*
+* This function returns a numerical approximation to the exact value.
+*
+* @param {Function} func input function
+* @param {Number} start - start of interval
+* @param {Number} end - end of interval
+* @param {Number} maxIterations - the maximum number of iterations
+* @param {Number} errorTolerance - the error tolerance
+* @returns {Number} estimated root value
+* @throws {TypeError} Argument func must be a function
+*
+* @example
+* bisect(Math.cos,0,4,100,0.003); // => 1.572265625
+*/
+function bisect(
+func/*: (x: any) => number */,
+start/*: number */,
+end/*: number */,
+maxIterations/*: number */,
+errorTolerance/*: number */)/*:number*/ {
+if (typeof func !== 'function') throw new TypeError('func must be a function');
+for (var i = 0; i < maxIterations; i++) {
+var output = (start + end) / 2;
+if (func(output) === 0 || Math.abs((end - start) / 2) < errorTolerance) {
+return output;
+}
+if (sign(func(output)) === sign(func(start))) {
+start = output;
+} else {
+end = output;
+}
+}
+throw new Error('maximum number of iterations exceeded');
+}
+module.exports = bisect;
+},{"53":53}],6:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* **Percentage Points of the χ2 (Chi-Squared) Distribution**
+*
+* The [χ2 (Chi-Squared) Distribution](http://en.wikipedia.org/wiki/Chi-squared_distribution) is used in the common
+* chi-squared tests for goodness of fit of an observed distribution to a theoretical one, the independence of two
+* criteria of classification of qualitative data, and in confidence interval estimation for a population standard
+* deviation of a normal distribution from a sample standard deviation.
+*
+* Values from Appendix 1, Table III of William W. Hines & Douglas C. Montgomery, "Probability and Statistics in
+* Engineering and Management Science", Wiley (1980).
+*/
+var chiSquaredDistributionTable = { '1':
+{ '0.995': 0,
+'0.99': 0,
+'0.975': 0,
+'0.95': 0,
+'0.9': 0.02,
+'0.5': 0.45,
+'0.1': 2.71,
+'0.05': 3.84,
+'0.025': 5.02,
+'0.01': 6.63,
+'0.005': 7.88 },
+'2':
+{ '0.995': 0.01,
+'0.99': 0.02,
+'0.975': 0.05,
+'0.95': 0.1,
+'0.9': 0.21,
+'0.5': 1.39,
+'0.1': 4.61,
+'0.05': 5.99,
+'0.025': 7.38,
+'0.01': 9.21,
+'0.005': 10.6 },
+'3':
+{ '0.995': 0.07,
+'0.99': 0.11,
+'0.975': 0.22,
+'0.95': 0.35,
+'0.9': 0.58,
+'0.5': 2.37,
+'0.1': 6.25,
+'0.05': 7.81,
+'0.025': 9.35,
+'0.01': 11.34,
+'0.005': 12.84 },
+'4':
+{ '0.995': 0.21,
+'0.99': 0.3,
+'0.975': 0.48,
+'0.95': 0.71,
+'0.9': 1.06,
+'0.5': 3.36,
+'0.1': 7.78,
+'0.05': 9.49,
+'0.025': 11.14,
+'0.01': 13.28,
+'0.005': 14.86 },
+'5':
+{ '0.995': 0.41,
+'0.99': 0.55,
+'0.975': 0.83,
+'0.95': 1.15,
+'0.9': 1.61,
+'0.5': 4.35,
+'0.1': 9.24,
+'0.05': 11.07,
+'0.025': 12.83,
+'0.01': 15.09,
+'0.005': 16.75 },
+'6':
+{ '0.995': 0.68,
+'0.99': 0.87,
+'0.975': 1.24,
+'0.95': 1.64,
+'0.9': 2.2,
+'0.5': 5.35,
+'0.1': 10.65,
+'0.05': 12.59,
+'0.025': 14.45,
+'0.01': 16.81,
+'0.005': 18.55 },
+'7':
+{ '0.995': 0.99,
+'0.99': 1.25,
+'0.975': 1.69,
+'0.95': 2.17,
+'0.9': 2.83,
+'0.5': 6.35,
+'0.1': 12.02,
+'0.05': 14.07,
+'0.025': 16.01,
+'0.01': 18.48,
+'0.005': 20.28 },
+'8':
+{ '0.995': 1.34,
+'0.99': 1.65,
+'0.975': 2.18,
+'0.95': 2.73,
+'0.9': 3.49,
+'0.5': 7.34,
+'0.1': 13.36,
+'0.05': 15.51,
+'0.025': 17.53,
+'0.01': 20.09,
+'0.005': 21.96 },
+'9':
+{ '0.995': 1.73,
+'0.99': 2.09,
+'0.975': 2.7,
+'0.95': 3.33,
+'0.9': 4.17,
+'0.5': 8.34,
+'0.1': 14.68,
+'0.05': 16.92,
+'0.025': 19.02,
+'0.01': 21.67,
+'0.005': 23.59 },
+'10':
+{ '0.995': 2.16,
+'0.99': 2.56,
+'0.975': 3.25,
+'0.95': 3.94,
+'0.9': 4.87,
+'0.5': 9.34,
+'0.1': 15.99,
+'0.05': 18.31,
+'0.025': 20.48,
+'0.01': 23.21,
+'0.005': 25.19 },
+'11':
+{ '0.995': 2.6,
+'0.99': 3.05,
+'0.975': 3.82,
+'0.95': 4.57,
+'0.9': 5.58,
+'0.5': 10.34,
+'0.1': 17.28,
+'0.05': 19.68,
+'0.025': 21.92,
+'0.01': 24.72,
+'0.005': 26.76 },
+'12':
+{ '0.995': 3.07,
+'0.99': 3.57,
+'0.975': 4.4,
+'0.95': 5.23,
+'0.9': 6.3,
+'0.5': 11.34,
+'0.1': 18.55,
+'0.05': 21.03,
+'0.025': 23.34,
+'0.01': 26.22,
+'0.005': 28.3 },
+'13':
+{ '0.995': 3.57,
+'0.99': 4.11,
+'0.975': 5.01,
+'0.95': 5.89,
+'0.9': 7.04,
+'0.5': 12.34,
+'0.1': 19.81,
+'0.05': 22.36,
+'0.025': 24.74,
+'0.01': 27.69,
+'0.005': 29.82 },
+'14':
+{ '0.995': 4.07,
+'0.99': 4.66,
+'0.975': 5.63,
+'0.95': 6.57,
+'0.9': 7.79,
+'0.5': 13.34,
+'0.1': 21.06,
+'0.05': 23.68,
+'0.025': 26.12,
+'0.01': 29.14,
+'0.005': 31.32 },
+'15':
+{ '0.995': 4.6,
+'0.99': 5.23,
+'0.975': 6.27,
+'0.95': 7.26,
+'0.9': 8.55,
+'0.5': 14.34,
+'0.1': 22.31,
+'0.05': 25,
+'0.025': 27.49,
+'0.01': 30.58,
+'0.005': 32.8 },
+'16':
+{ '0.995': 5.14,
+'0.99': 5.81,
+'0.975': 6.91,
+'0.95': 7.96,
+'0.9': 9.31,
+'0.5': 15.34,
+'0.1': 23.54,
+'0.05': 26.3,
+'0.025': 28.85,
+'0.01': 32,
+'0.005': 34.27 },
+'17':
+{ '0.995': 5.7,
+'0.99': 6.41,
+'0.975': 7.56,
+'0.95': 8.67,
+'0.9': 10.09,
+'0.5': 16.34,
+'0.1': 24.77,
+'0.05': 27.59,
+'0.025': 30.19,
+'0.01': 33.41,
+'0.005': 35.72 },
+'18':
+{ '0.995': 6.26,
+'0.99': 7.01,
+'0.975': 8.23,
+'0.95': 9.39,
+'0.9': 10.87,
+'0.5': 17.34,
+'0.1': 25.99,
+'0.05': 28.87,
+'0.025': 31.53,
+'0.01': 34.81,
+'0.005': 37.16 },
+'19':
+{ '0.995': 6.84,
+'0.99': 7.63,
+'0.975': 8.91,
+'0.95': 10.12,
+'0.9': 11.65,
+'0.5': 18.34,
+'0.1': 27.2,
+'0.05': 30.14,
+'0.025': 32.85,
+'0.01': 36.19,
+'0.005': 38.58 },
+'20':
+{ '0.995': 7.43,
+'0.99': 8.26,
+'0.975': 9.59,
+'0.95': 10.85,
+'0.9': 12.44,
+'0.5': 19.34,
+'0.1': 28.41,
+'0.05': 31.41,
+'0.025': 34.17,
+'0.01': 37.57,
+'0.005': 40 },
+'21':
+{ '0.995': 8.03,
+'0.99': 8.9,
+'0.975': 10.28,
+'0.95': 11.59,
+'0.9': 13.24,
+'0.5': 20.34,
+'0.1': 29.62,
+'0.05': 32.67,
+'0.025': 35.48,
+'0.01': 38.93,
+'0.005': 41.4 },
+'22':
+{ '0.995': 8.64,
+'0.99': 9.54,
+'0.975': 10.98,
+'0.95': 12.34,
+'0.9': 14.04,
+'0.5': 21.34,
+'0.1': 30.81,
+'0.05': 33.92,
+'0.025': 36.78,
+'0.01': 40.29,
+'0.005': 42.8 },
+'23':
+{ '0.995': 9.26,
+'0.99': 10.2,
+'0.975': 11.69,
+'0.95': 13.09,
+'0.9': 14.85,
+'0.5': 22.34,
+'0.1': 32.01,
+'0.05': 35.17,
+'0.025': 38.08,
+'0.01': 41.64,
+'0.005': 44.18 },
+'24':
+{ '0.995': 9.89,
+'0.99': 10.86,
+'0.975': 12.4,
+'0.95': 13.85,
+'0.9': 15.66,
+'0.5': 23.34,
+'0.1': 33.2,
+'0.05': 36.42,
+'0.025': 39.36,
+'0.01': 42.98,
+'0.005': 45.56 },
+'25':
+{ '0.995': 10.52,
+'0.99': 11.52,
+'0.975': 13.12,
+'0.95': 14.61,
+'0.9': 16.47,
+'0.5': 24.34,
+'0.1': 34.28,
+'0.05': 37.65,
+'0.025': 40.65,
+'0.01': 44.31,
+'0.005': 46.93 },
+'26':
+{ '0.995': 11.16,
+'0.99': 12.2,
+'0.975': 13.84,
+'0.95': 15.38,
+'0.9': 17.29,
+'0.5': 25.34,
+'0.1': 35.56,
+'0.05': 38.89,
+'0.025': 41.92,
+'0.01': 45.64,
+'0.005': 48.29 },
+'27':
+{ '0.995': 11.81,
+'0.99': 12.88,
+'0.975': 14.57,
+'0.95': 16.15,
+'0.9': 18.11,
+'0.5': 26.34,
+'0.1': 36.74,
+'0.05': 40.11,
+'0.025': 43.19,
+'0.01': 46.96,
+'0.005': 49.65 },
+'28':
+{ '0.995': 12.46,
+'0.99': 13.57,
+'0.975': 15.31,
+'0.95': 16.93,
+'0.9': 18.94,
+'0.5': 27.34,
+'0.1': 37.92,
+'0.05': 41.34,
+'0.025': 44.46,
+'0.01': 48.28,
+'0.005': 50.99 },
+'29':
+{ '0.995': 13.12,
+'0.99': 14.26,
+'0.975': 16.05,
+'0.95': 17.71,
+'0.9': 19.77,
+'0.5': 28.34,
+'0.1': 39.09,
+'0.05': 42.56,
+'0.025': 45.72,
+'0.01': 49.59,
+'0.005': 52.34 },
+'30':
+{ '0.995': 13.79,
+'0.99': 14.95,
+'0.975': 16.79,
+'0.95': 18.49,
+'0.9': 20.6,
+'0.5': 29.34,
+'0.1': 40.26,
+'0.05': 43.77,
+'0.025': 46.98,
+'0.01': 50.89,
+'0.005': 53.67 },
+'40':
+{ '0.995': 20.71,
+'0.99': 22.16,
+'0.975': 24.43,
+'0.95': 26.51,
+'0.9': 29.05,
+'0.5': 39.34,
+'0.1': 51.81,
+'0.05': 55.76,
+'0.025': 59.34,
+'0.01': 63.69,
+'0.005': 66.77 },
+'50':
+{ '0.995': 27.99,
+'0.99': 29.71,
+'0.975': 32.36,
+'0.95': 34.76,
+'0.9': 37.69,
+'0.5': 49.33,
+'0.1': 63.17,
+'0.05': 67.5,
+'0.025': 71.42,
+'0.01': 76.15,
+'0.005': 79.49 },
+'60':
+{ '0.995': 35.53,
+'0.99': 37.48,
+'0.975': 40.48,
+'0.95': 43.19,
+'0.9': 46.46,
+'0.5': 59.33,
+'0.1': 74.4,
+'0.05': 79.08,
+'0.025': 83.3,
+'0.01': 88.38,
+'0.005': 91.95 },
+'70':
+{ '0.995': 43.28,
+'0.99': 45.44,
+'0.975': 48.76,
+'0.95': 51.74,
+'0.9': 55.33,
+'0.5': 69.33,
+'0.1': 85.53,
+'0.05': 90.53,
+'0.025': 95.02,
+'0.01': 100.42,
+'0.005': 104.22 },
+'80':
+{ '0.995': 51.17,
+'0.99': 53.54,
+'0.975': 57.15,
+'0.95': 60.39,
+'0.9': 64.28,
+'0.5': 79.33,
+'0.1': 96.58,
+'0.05': 101.88,
+'0.025': 106.63,
+'0.01': 112.33,
+'0.005': 116.32 },
+'90':
+{ '0.995': 59.2,
+'0.99': 61.75,
+'0.975': 65.65,
+'0.95': 69.13,
+'0.9': 73.29,
+'0.5': 89.33,
+'0.1': 107.57,
+'0.05': 113.14,
+'0.025': 118.14,
+'0.01': 124.12,
+'0.005': 128.3 },
+'100':
+{ '0.995': 67.33,
+'0.99': 70.06,
+'0.975': 74.22,
+'0.95': 77.93,
+'0.9': 82.36,
+'0.5': 99.33,
+'0.1': 118.5,
+'0.05': 124.34,
+'0.025': 129.56,
+'0.01': 135.81,
+'0.005': 140.17 } };
+module.exports = chiSquaredDistributionTable;
+},{}],7:[function(require,module,exports){
+'use strict';
+/* @flow */
+var mean = require(25);
+var chiSquaredDistributionTable = require(6);
+/**
+* The [χ2 (Chi-Squared) Goodness-of-Fit Test](http://en.wikipedia.org/wiki/Goodness_of_fit#Pearson.27s_chi-squared_test)
+* uses a measure of goodness of fit which is the sum of differences between observed and expected outcome frequencies
+* (that is, counts of observations), each squared and divided by the number of observations expected given the
+* hypothesized distribution. The resulting χ2 statistic, `chiSquared`, can be compared to the chi-squared distribution
+* to determine the goodness of fit. In order to determine the degrees of freedom of the chi-squared distribution, one
+* takes the total number of observed frequencies and subtracts the number of estimated parameters. The test statistic
+* follows, approximately, a chi-square distribution with (k − c) degrees of freedom where `k` is the number of non-empty
+* cells and `c` is the number of estimated parameters for the distribution.
+*
+* @param {Array<number>} data
+* @param {Function} distributionType a function that returns a point in a distribution:
+* for instance, binomial, bernoulli, or poisson
+* @param {number} significance
+* @returns {number} chi squared goodness of fit
+* @example
+* // Data from Poisson goodness-of-fit example 10-19 in William W. Hines & Douglas C. Montgomery,
+* // "Probability and Statistics in Engineering and Management Science", Wiley (1980).
+* var data1019 = [
+*     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+*     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+*     1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+*     2, 2, 2, 2, 2, 2, 2, 2, 2,
+*     3, 3, 3, 3
+* ];
+* ss.chiSquaredGoodnessOfFit(data1019, ss.poissonDistribution, 0.05)); //= false
+*/
+function chiSquaredGoodnessOfFit(
+data/*: Array<number> */,
+distributionType/*: Function */,
+significance/*: number */)/*: boolean */ {
+// Estimate from the sample data, a weighted mean.
+var inputMean = mean(data),
+// Calculated value of the χ2 statistic.
+chiSquared = 0,
+// Degrees of freedom, calculated as (number of class intervals -
+// number of hypothesized distribution parameters estimated - 1)
+degreesOfFreedom,
+// Number of hypothesized distribution parameters estimated, expected to be supplied in the distribution test.
+// Lose one degree of freedom for estimating `lambda` from the sample data.
+c = 1,
+// The hypothesized distribution.
+// Generate the hypothesized distribution.
+hypothesizedDistribution = distributionType(inputMean),
+observedFrequencies = [],
+expectedFrequencies = [],
+k;
+// Create an array holding a histogram from the sample data, of
+// the form `{ value: numberOfOcurrences }`
+for (var i = 0; i < data.length; i++) {
+if (observedFrequencies[data[i]] === undefined) {
+observedFrequencies[data[i]] = 0;
+}
+observedFrequencies[data[i]]++;
+}
+// The histogram we created might be sparse - there might be gaps
+// between values. So we iterate through the histogram, making
+// sure that instead of undefined, gaps have 0 values.
+for (i = 0; i < observedFrequencies.length; i++) {
+if (observedFrequencies[i] === undefined) {
+observedFrequencies[i] = 0;
+}
+}
+// Create an array holding a histogram of expected data given the
+// sample size and hypothesized distribution.
+for (k in hypothesizedDistribution) {
+if (k in observedFrequencies) {
+expectedFrequencies[+k] = hypothesizedDistribution[k] * data.length;
+}
+}
+// Working backward through the expected frequencies, collapse classes
+// if less than three observations are expected for a class.
+// This transformation is applied to the observed frequencies as well.
+for (k = expectedFrequencies.length - 1; k >= 0; k--) {
+if (expectedFrequencies[k] < 3) {
+expectedFrequencies[k - 1] += expectedFrequencies[k];
+expectedFrequencies.pop();
+observedFrequencies[k - 1] += observedFrequencies[k];
+observedFrequencies.pop();
+}
+}
+// Iterate through the squared differences between observed & expected
+// frequencies, accumulating the `chiSquared` statistic.
+for (k = 0; k < observedFrequencies.length; k++) {
+chiSquared += Math.pow(
+observedFrequencies[k] - expectedFrequencies[k], 2) /
+expectedFrequencies[k];
+}
+// Calculate degrees of freedom for this test and look it up in the
+// `chiSquaredDistributionTable` in order to
+// accept or reject the goodness-of-fit of the hypothesized distribution.
+degreesOfFreedom = observedFrequencies.length - c - 1;
+return chiSquaredDistributionTable[degreesOfFreedom][significance] < chiSquared;
+}
+module.exports = chiSquaredGoodnessOfFit;
+},{"25":25,"6":6}],8:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* Split an array into chunks of a specified size. This function
+* has the same behavior as [PHP's array_chunk](http://php.net/manual/en/function.array-chunk.php)
+* function, and thus will insert smaller-sized chunks at the end if
+* the input size is not divisible by the chunk size.
+*
+* `sample` is expected to be an array, and `chunkSize` a number.
+* The `sample` array can contain any kind of data.
+*
+* @param {Array} sample any array of values
+* @param {number} chunkSize size of each output array
+* @returns {Array<Array>} a chunked array
+* @example
+* chunk([1, 2, 3, 4, 5, 6], 2);
+* // => [[1, 2], [3, 4], [5, 6]]
+*/
+function chunk(sample/*:Array<any>*/, chunkSize/*:number*/)/*:?Array<Array<any>>*/ {
+// a list of result chunks, as arrays in an array
+var output = [];
+// `chunkSize` must be zero or higher - otherwise the loop below,
+// in which we call `start += chunkSize`, will loop infinitely.
+// So, we'll detect and throw in that case to indicate
+// invalid input.
+if (chunkSize <= 0) {
+throw new Error('chunk size must be a positive integer');
+}
+// `start` is the index at which `.slice` will start selecting
+// new array elements
+for (var start = 0; start < sample.length; start += chunkSize) {
+// for each chunk, slice that part of the array and add it
+// to the output. The `.slice` function does not change
+// the original array.
+output.push(sample.slice(start, start + chunkSize));
+}
+return output;
+}
+module.exports = chunk;
+},{}],9:[function(require,module,exports){
+'use strict';
+/* @flow */
+var uniqueCountSorted = require(61),
+numericSort = require(34);
+/**
+* Create a new column x row matrix.
+*
+* @private
+* @param {number} columns
+* @param {number} rows
+* @return {Array<Array<number>>} matrix
+* @example
+* makeMatrix(10, 10);
+*/
+function makeMatrix(columns, rows) {
+var matrix = [];
+for (var i = 0; i < columns; i++) {
+var column = [];
+for (var j = 0; j < rows; j++) {
+column.push(0);
+}
+matrix.push(column);
+}
+return matrix;
+}
+/**
+* Generates incrementally computed values based on the sums and sums of
+* squares for the data array
+*
+* @private
+* @param {number} j
+* @param {number} i
+* @param {Array<number>} sums
+* @param {Array<number>} sumsOfSquares
+* @return {number}
+* @example
+* ssq(0, 1, [-1, 0, 2], [1, 1, 5]);
+*/
+function ssq(j, i, sums, sumsOfSquares) {
+var sji; // s(j, i)
+if (j > 0) {
+var muji = (sums[i] - sums[j - 1]) / (i - j + 1); // mu(j, i)
+sji = sumsOfSquares[i] - sumsOfSquares[j - 1] - (i - j + 1) * muji * muji;
+} else {
+sji = sumsOfSquares[i] - sums[i] * sums[i] / (i + 1);
+}
+if (sji < 0) {
+return 0;
+}
+return sji;
+}
+/**
+* Function that recursively divides and conquers computations
+* for cluster j
+*
+* @private
+* @param {number} iMin Minimum index in cluster to be computed
+* @param {number} iMax Maximum index in cluster to be computed
+* @param {number} cluster Index of the cluster currently being computed
+* @param {Array<Array<number>>} matrix
+* @param {Array<Array<number>>} backtrackMatrix
+* @param {Array<number>} sums
+* @param {Array<number>} sumsOfSquares
+*/
+function fillMatrixColumn(iMin, iMax, cluster, matrix, backtrackMatrix, sums, sumsOfSquares) {
+if (iMin > iMax) {
+return;
+}
+// Start at midpoint between iMin and iMax
+var i = Math.floor((iMin + iMax) / 2);
+matrix[cluster][i] = matrix[cluster - 1][i - 1];
+backtrackMatrix[cluster][i] = i;
+var jlow = cluster; // the lower end for j
+if (iMin > cluster) {
+jlow = Math.max(jlow, backtrackMatrix[cluster][iMin - 1] || 0);
+}
+jlow = Math.max(jlow, backtrackMatrix[cluster - 1][i] || 0);
+var jhigh = i - 1; // the upper end for j
+if (iMax < matrix.length - 1) {
+jhigh = Math.min(jhigh, backtrackMatrix[cluster][iMax + 1] || 0);
+}
+var sji;
+var sjlowi;
+var ssqjlow;
+var ssqj;
+for (var j = jhigh; j >= jlow; --j) {
+sji = ssq(j, i, sums, sumsOfSquares);
+if (sji + matrix[cluster - 1][jlow - 1] >= matrix[cluster][i]) {
+break;
+}
+// Examine the lower bound of the cluster border
+sjlowi = ssq(jlow, i, sums, sumsOfSquares);
+ssqjlow = sjlowi + matrix[cluster - 1][jlow - 1];
+if (ssqjlow < matrix[cluster][i]) {
+// Shrink the lower bound
+matrix[cluster][i] = ssqjlow;
+backtrackMatrix[cluster][i] = jlow;
+}
+jlow++;
+ssqj = sji + matrix[cluster - 1][j - 1];
+if (ssqj < matrix[cluster][i]) {
+matrix[cluster][i] = ssqj;
+backtrackMatrix[cluster][i] = j;
+}
+}
+fillMatrixColumn(iMin, i - 1, cluster, matrix, backtrackMatrix, sums, sumsOfSquares);
+fillMatrixColumn(i + 1, iMax, cluster, matrix, backtrackMatrix, sums, sumsOfSquares);
+}
+/**
+* Initializes the main matrices used in Ckmeans and kicks
+* off the divide and conquer cluster computation strategy
+*
+* @private
+* @param {Array<number>} data sorted array of values
+* @param {Array<Array<number>>} matrix
+* @param {Array<Array<number>>} backtrackMatrix
+*/
+function fillMatrices(data, matrix, backtrackMatrix) {
+var nValues = matrix[0].length;
+// Shift values by the median to improve numeric stability
+var shift = data[Math.floor(nValues / 2)];
+// Cumulative sum and cumulative sum of squares for all values in data array
+var sums = [];
+var sumsOfSquares = [];
+// Initialize first column in matrix & backtrackMatrix
+for (var i = 0, shiftedValue; i < nValues; ++i) {
+shiftedValue = data[i] - shift;
+if (i === 0) {
+sums.push(shiftedValue);
+sumsOfSquares.push(shiftedValue * shiftedValue);
+} else {
+sums.push(sums[i - 1] + shiftedValue);
+sumsOfSquares.push(sumsOfSquares[i - 1] + shiftedValue * shiftedValue);
+}
+// Initialize for cluster = 0
+matrix[0][i] = ssq(0, i, sums, sumsOfSquares);
+backtrackMatrix[0][i] = 0;
+}
+// Initialize the rest of the columns
+var iMin;
+for (var cluster = 1; cluster < matrix.length; ++cluster) {
+if (cluster < matrix.length - 1) {
+iMin = cluster;
+} else {
+// No need to compute matrix[K-1][0] ... matrix[K-1][N-2]
+iMin = nValues - 1;
+}
+fillMatrixColumn(iMin, nValues - 1, cluster, matrix, backtrackMatrix, sums, sumsOfSquares);
+}
+}
+/**
+* Ckmeans clustering is an improvement on heuristic-based clustering
+* approaches like Jenks. The algorithm was developed in
+* [Haizhou Wang and Mingzhou Song](http://journal.r-project.org/archive/2011-2/RJournal_2011-2_Wang+Song.pdf)
+* as a [dynamic programming](https://en.wikipedia.org/wiki/Dynamic_programming) approach
+* to the problem of clustering numeric data into groups with the least
+* within-group sum-of-squared-deviations.
+*
+* Minimizing the difference within groups - what Wang & Song refer to as
+* `withinss`, or within sum-of-squares, means that groups are optimally
+* homogenous within and the data is split into representative groups.
+* This is very useful for visualization, where you may want to represent
+* a continuous variable in discrete color or style groups. This function
+* can provide groups that emphasize differences between data.
+*
+* Being a dynamic approach, this algorithm is based on two matrices that
+* store incrementally-computed values for squared deviations and backtracking
+* indexes.
+*
+* This implementation is based on Ckmeans 3.4.6, which introduced a new divide
+* and conquer approach that improved runtime from O(kn^2) to O(kn log(n)).
+*
+* Unlike the [original implementation](https://cran.r-project.org/web/packages/Ckmeans.1d.dp/index.html),
+* this implementation does not include any code to automatically determine
+* the optimal number of clusters: this information needs to be explicitly
+* provided.
+*
+* ### References
+* _Ckmeans.1d.dp: Optimal k-means Clustering in One Dimension by Dynamic
+* Programming_ Haizhou Wang and Mingzhou Song ISSN 2073-4859
+*
+* from The R Journal Vol. 3/2, December 2011
+* @param {Array<number>} data input data, as an array of number values
+* @param {number} nClusters number of desired classes. This cannot be
+* greater than the number of values in the data array.
+* @returns {Array<Array<number>>} clustered input
+* @example
+* ckmeans([-1, 2, -1, 2, 4, 5, 6, -1, 2, -1], 3);
+* // The input, clustered into groups of similar numbers.
+* //= [[-1, -1, -1, -1], [2, 2, 2], [4, 5, 6]]);
+*/
+function ckmeans(data/*: Array<number> */, nClusters/*: number */)/*: Array<Array<number>> */ {
+if (nClusters > data.length) {
+throw new Error('Cannot generate more classes than there are data values');
+}
+var sorted = numericSort(data),
+// we'll use this as the maximum number of clusters
+uniqueCount = uniqueCountSorted(sorted);
+// if all of the input values are identical, there's one cluster
+// with all of the input in it.
+if (uniqueCount === 1) {
+return [sorted];
+}
+// named 'S' originally
+var matrix = makeMatrix(nClusters, sorted.length),
+// named 'J' originally
+backtrackMatrix = makeMatrix(nClusters, sorted.length);
+// This is a dynamic programming way to solve the problem of minimizing
+// within-cluster sum of squares. It's similar to linear regression
+// in this way, and this calculation incrementally computes the
+// sum of squares that are later read.
+fillMatrices(sorted, matrix, backtrackMatrix);
+// The real work of Ckmeans clustering happens in the matrix generation:
+// the generated matrices encode all possible clustering combinations, and
+// once they're generated we can solve for the best clustering groups
+// very quickly.
+var clusters = [],
+clusterRight = backtrackMatrix[0].length - 1;
+// Backtrack the clusters from the dynamic programming matrix. This
+// starts at the bottom-right corner of the matrix (if the top-left is 0, 0),
+// and moves the cluster target with the loop.
+for (var cluster = backtrackMatrix.length - 1; cluster >= 0; cluster--) {
+var clusterLeft = backtrackMatrix[cluster][clusterRight];
+// fill the cluster from the sorted input by taking a slice of the
+// array. the backtrack matrix makes this easy - it stores the
+// indexes where the cluster should start and end.
+clusters[cluster] = sorted.slice(clusterLeft, clusterRight + 1);
+if (cluster > 0) {
+clusterRight = clusterLeft - 1;
+}
+}
+return clusters;
+}
+module.exports = ckmeans;
+},{"34":34,"61":61}],10:[function(require,module,exports){
+/* @flow */
+'use strict';
+/**
+* Implementation of Combinations
+* Combinations are unique subsets of a collection - in this case, k elements from a collection at a time.
+* https://en.wikipedia.org/wiki/Combination
+* @param {Array} elements any type of data
+* @param {int} k the number of objects in each group (without replacement)
+* @returns {Array<Array>} array of permutations
+* @example
+* combinations([1, 2, 3], 2); // => [[1,2], [1,3], [2,3]]
+*/
+function combinations(elements /*: Array<any> */, k/*: number */) {
+var i;
+var subI;
+var combinationList = [];
+var subsetCombinations;
+var next;
+for (i = 0; i < elements.length; i++) {
+if (k === 1) {
+combinationList.push([elements[i]])
+} else {
+subsetCombinations = combinations(elements.slice( i + 1, elements.length ), k - 1);
+for (subI = 0; subI < subsetCombinations.length; subI++) {
+next = subsetCombinations[subI];
+next.unshift(elements[i]);
+combinationList.push(next);
+}
+}
+}
+return combinationList;
+}
+module.exports = combinations;
+},{}],11:[function(require,module,exports){
+/* @flow */
+'use strict';
+/**
+* Implementation of [Combinations](https://en.wikipedia.org/wiki/Combination) with replacement
+* Combinations are unique subsets of a collection - in this case, k elements from a collection at a time.
+* 'With replacement' means that a given element can be chosen multiple times.
+* Unlike permutation, order doesn't matter for combinations.
+*
+* @param {Array} elements any type of data
+* @param {int} k the number of objects in each group (without replacement)
+* @returns {Array<Array>} array of permutations
+* @example
+* combinationsReplacement([1, 2], 2); // => [[1, 1], [1, 2], [2, 2]]
+*/
+function combinationsReplacement(
+elements /*: Array<any> */,
+k /*: number */) {
+var combinationList = [];
+for (var i = 0; i < elements.length; i++) {
+if (k === 1) {
+// If we're requested to find only one element, we don't need
+// to recurse: just push `elements[i]` onto the list of combinations.
+combinationList.push([elements[i]])
+} else {
+// Otherwise, recursively find combinations, given `k - 1`. Note that
+// we request `k - 1`, so if you were looking for k=3 combinations, we're
+// requesting k=2. This -1 gets reversed in the for loop right after this
+// code, since we concatenate `elements[i]` onto the selected combinations,
+// bringing `k` back up to your requested level.
+// This recursion may go many levels deep, since it only stops once
+// k=1.
+var subsetCombinations = combinationsReplacement(
+elements.slice(i, elements.length),
+k - 1);
+for (var j = 0; j < subsetCombinations.length; j++) {
+combinationList.push([elements[i]]
+.concat(subsetCombinations[j]));
+}
+}
+}
+return combinationList;
+}
+module.exports = combinationsReplacement;
+},{}],12:[function(require,module,exports){
+'use strict';
+/* @flow */
+var standardNormalTable = require(55);
+/**
+* **[Cumulative Standard Normal Probability](http://en.wikipedia.org/wiki/Standard_normal_table)**
+*
+* Since probability tables cannot be
+* printed for every normal distribution, as there are an infinite variety
+* of normal distributions, it is common practice to convert a normal to a
+* standard normal and then use the standard normal table to find probabilities.
+*
+* You can use `.5 + .5 * errorFunction(x / Math.sqrt(2))` to calculate the probability
+* instead of looking it up in a table.
+*
+* @param {number} z
+* @returns {number} cumulative standard normal probability
+*/
+function cumulativeStdNormalProbability(z /*:number */)/*:number */ {
+// Calculate the position of this value.
+var absZ = Math.abs(z),
+// Each row begins with a different
+// significant digit: 0.5, 0.6, 0.7, and so on. Each value in the table
+// corresponds to a range of 0.01 in the input values, so the value is
+// multiplied by 100.
+index = Math.min(Math.round(absZ * 100), standardNormalTable.length - 1);
+// The index we calculate must be in the table as a positive value,
+// but we still pay attention to whether the input is positive
+// or negative, and flip the output value as a last step.
+if (z >= 0) {
+return standardNormalTable[index];
+} else {
+// due to floating-point arithmetic, values in the table with
+// 4 significant figures can nevertheless end up as repeating
+// fractions when they're computed here.
+return +(1 - standardNormalTable[index]).toFixed(4);
+}
+}
+module.exports = cumulativeStdNormalProbability;
+},{"55":55}],13:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* We use `ε`, epsilon, as a stopping criterion when we want to iterate
+* until we're "close enough". Epsilon is a very small number: for
+* simple statistics, that number is **0.0001**
+*
+* This is used in calculations like the binomialDistribution, in which
+* the process of finding a value is [iterative](https://en.wikipedia.org/wiki/Iterative_method):
+* it progresses until it is close enough.
+*
+* Below is an example of using epsilon in [gradient descent](https://en.wikipedia.org/wiki/Gradient_descent),
+* where we're trying to find a local minimum of a function's derivative,
+* given by the `fDerivative` method.
+*
+* @example
+* // From calculation, we expect that the local minimum occurs at x=9/4
+* var x_old = 0;
+* // The algorithm starts at x=6
+* var x_new = 6;
+* var stepSize = 0.01;
+*
+* function fDerivative(x) {
+*   return 4 * Math.pow(x, 3) - 9 * Math.pow(x, 2);
+* }
+*
+* // The loop runs until the difference between the previous
+* // value and the current value is smaller than epsilon - a rough
+* // meaure of 'close enough'
+* while (Math.abs(x_new - x_old) > ss.epsilon) {
+*   x_old = x_new;
+*   x_new = x_old - stepSize * fDerivative(x_old);
+* }
+*
+* console.log('Local minimum occurs at', x_new);
+*/
+var epsilon = 0.0001;
+module.exports = epsilon;
+},{}],14:[function(require,module,exports){
+'use strict';
+/* @flow */
+var max = require(23),
+min = require(29);
+/**
+* Given an array of data, this will find the extent of the
+* data and return an array of breaks that can be used
+* to categorize the data into a number of classes. The
+* returned array will always be 1 longer than the number of
+* classes because it includes the minimum value.
+*
+* @param {Array<number>} data input data, as an array of number values
+* @param {number} nClasses number of desired classes
+* @returns {Array<number>} array of class break positions
+* @example
+* equalIntervalBreaks([1, 2, 3, 4, 5, 6], 4); //= [1, 2.25, 3.5, 4.75, 6]
+*/
+function equalIntervalBreaks(data/*: Array<number> */, nClasses/*:number*/)/*: Array<number> */ {
+if (data.length <= 1) {
+return data;
+}
+var theMin = min(data),
+theMax = max(data);
+// the first break will always be the minimum value
+// in the dataset
+var breaks = [theMin];
+// The size of each break is the full range of the data
+// divided by the number of classes requested
+var breakSize = (theMax - theMin) / nClasses;
+// In the case of nClasses = 1, this loop won't run
+// and the returned breaks will be [min, max]
+for (var i = 1; i < nClasses; i++) {
+breaks.push(breaks[0] + breakSize * i);
+}
+// the last break will always be the
+// maximum.
+breaks.push(theMax);
+return breaks;
+}
+module.exports = equalIntervalBreaks;
+},{"23":23,"29":29}],15:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* **[Gaussian error function](http://en.wikipedia.org/wiki/Error_function)**
+*
+* The `errorFunction(x/(sd * Math.sqrt(2)))` is the probability that a value in a
+* normal distribution with standard deviation sd is within x of the mean.
+*
+* This function returns a numerical approximation to the exact value.
+*
+* @param {number} x input
+* @return {number} error estimation
+* @example
+* errorFunction(1).toFixed(2); // => '0.84'
+*/
+function errorFunction(x/*: number */)/*: number */ {
+var t = 1 / (1 + 0.5 * Math.abs(x));
+var tau = t * Math.exp(-Math.pow(x, 2) -
+1.26551223 +
+1.00002368 * t +
+0.37409196 * Math.pow(t, 2) +
+0.09678418 * Math.pow(t, 3) -
+0.18628806 * Math.pow(t, 4) +
+0.27886807 * Math.pow(t, 5) -
+1.13520398 * Math.pow(t, 6) +
+1.48851587 * Math.pow(t, 7) -
+0.82215223 * Math.pow(t, 8) +
+0.17087277 * Math.pow(t, 9));
+if (x >= 0) {
+return 1 - tau;
+} else {
+return tau - 1;
+}
+}
+module.exports = errorFunction;
+},{}],16:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* A [Factorial](https://en.wikipedia.org/wiki/Factorial), usually written n!, is the product of all positive
+* integers less than or equal to n. Often factorial is implemented
+* recursively, but this iterative approach is significantly faster
+* and simpler.
+*
+* @param {number} n input
+* @returns {number} factorial: n!
+* @example
+* factorial(5); // => 120
+*/
+function factorial(n /*: number */)/*: number */ {
+// factorial is mathematically undefined for negative numbers
+if (n < 0) { return NaN; }
+// typically you'll expand the factorial function going down, like
+// 5! = 5 * 4 * 3 * 2 * 1. This is going in the opposite direction,
+// counting from 2 up to the number in question, and since anything
+// multiplied by 1 is itself, the loop only needs to start at 2.
+var accumulator = 1;
+for (var i = 2; i <= n; i++) {
+// for each number up to and including the number `n`, multiply
+// the accumulator my that number.
+accumulator *= i;
+}
+return accumulator;
+}
+module.exports = factorial;
+},{}],17:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* The [Geometric Mean](https://en.wikipedia.org/wiki/Geometric_mean) is
+* a mean function that is more useful for numbers in different
+* ranges.
+*
+* This is the nth root of the input numbers multiplied by each other.
+*
+* The geometric mean is often useful for
+* **[proportional growth](https://en.wikipedia.org/wiki/Geometric_mean#Proportional_growth)**: given
+* growth rates for multiple years, like _80%, 16.66% and 42.85%_, a simple
+* mean will incorrectly estimate an average growth rate, whereas a geometric
+* mean will correctly estimate a growth rate that, over those years,
+* will yield the same end value.
+*
+* This runs on `O(n)`, linear time in respect to the array
+*
+* @param {Array<number>} x input array
+* @returns {number} geometric mean
+* @example
+* var growthRates = [1.80, 1.166666, 1.428571];
+* var averageGrowth = geometricMean(growthRates);
+* var averageGrowthRates = [averageGrowth, averageGrowth, averageGrowth];
+* var startingValue = 10;
+* var startingValueMean = 10;
+* growthRates.forEach(function(rate) {
+*   startingValue *= rate;
+* });
+* averageGrowthRates.forEach(function(rate) {
+*   startingValueMean *= rate;
+* });
+* startingValueMean === startingValue;
+*/
+function geometricMean(x /*: Array<number> */) {
+// The mean of no numbers is null
+if (x.length === 0) { return undefined; }
+// the starting value.
+var value = 1;
+for (var i = 0; i < x.length; i++) {
+// the geometric mean is only valid for positive numbers
+if (x[i] <= 0) { return undefined; }
+// repeatedly multiply the value by each number
+value *= x[i];
+}
+return Math.pow(value, 1 / x.length);
+}
+module.exports = geometricMean;
+},{}],18:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* The [Harmonic Mean](https://en.wikipedia.org/wiki/Harmonic_mean) is
+* a mean function typically used to find the average of rates.
+* This mean is calculated by taking the reciprocal of the arithmetic mean
+* of the reciprocals of the input numbers.
+*
+* This is a [measure of central tendency](https://en.wikipedia.org/wiki/Central_tendency):
+* a method of finding a typical or central value of a set of numbers.
+*
+* This runs on `O(n)`, linear time in respect to the array.
+*
+* @param {Array<number>} x input
+* @returns {number} harmonic mean
+* @example
+* harmonicMean([2, 3]).toFixed(2) // => '2.40'
+*/
+function harmonicMean(x /*: Array<number> */) {
+// The mean of no numbers is null
+if (x.length === 0) { return undefined; }
+var reciprocalSum = 0;
+for (var i = 0; i < x.length; i++) {
+// the harmonic mean is only valid for positive numbers
+if (x[i] <= 0) { return undefined; }
+reciprocalSum += 1 / x[i];
+}
+// divide n by the the reciprocal sum
+return x.length / reciprocalSum;
+}
+module.exports = harmonicMean;
+},{}],19:[function(require,module,exports){
+'use strict';
+/* @flow */
+var quantile = require(40);
+/**
+* The [Interquartile range](http://en.wikipedia.org/wiki/Interquartile_range) is
+* a measure of statistical dispersion, or how scattered, spread, or
+* concentrated a distribution is. It's computed as the difference between
+* the third quartile and first quartile.
+*
+* @param {Array<number>} sample
+* @returns {number} interquartile range: the span between lower and upper quartile,
+* 0.25 and 0.75
+* @example
+* interquartileRange([0, 1, 2, 3]); // => 2
+*/
+function interquartileRange(sample/*: Array<number> */) {
+// Interquartile range is the span between the upper quartile,
+// at `0.75`, and lower quartile, `0.25`
+var q1 = quantile(sample, 0.75),
+q2 = quantile(sample, 0.25);
+if (typeof q1 === 'number' && typeof q2 === 'number') {
+return q1 - q2;
+}
+}
+module.exports = interquartileRange;
+},{"40":40}],20:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* The Inverse [Gaussian error function](http://en.wikipedia.org/wiki/Error_function)
+* returns a numerical approximation to the value that would have caused
+* `errorFunction()` to return x.
+*
+* @param {number} x value of error function
+* @returns {number} estimated inverted value
+*/
+function inverseErrorFunction(x/*: number */)/*: number */ {
+var a = (8 * (Math.PI - 3)) / (3 * Math.PI * (4 - Math.PI));
+var inv = Math.sqrt(Math.sqrt(
+Math.pow(2 / (Math.PI * a) + Math.log(1 - x * x) / 2, 2) -
+Math.log(1 - x * x) / a) -
+(2 / (Math.PI * a) + Math.log(1 - x * x) / 2));
+if (x >= 0) {
+return inv;
+} else {
+return -inv;
+}
+}
+module.exports = inverseErrorFunction;
+},{}],21:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* [Simple linear regression](http://en.wikipedia.org/wiki/Simple_linear_regression)
+* is a simple way to find a fitted line
+* between a set of coordinates. This algorithm finds the slope and y-intercept of a regression line
+* using the least sum of squares.
+*
+* @param {Array<Array<number>>} data an array of two-element of arrays,
+* like `[[0, 1], [2, 3]]`
+* @returns {Object} object containing slope and intersect of regression line
+* @example
+* linearRegression([[0, 0], [1, 1]]); // => { m: 1, b: 0 }
+*/
+function linearRegression(data/*: Array<Array<number>> */)/*: { m: number, b: number } */ {
+var m, b;
+// Store data length in a local variable to reduce
+// repeated object property lookups
+var dataLength = data.length;
+//if there's only one point, arbitrarily choose a slope of 0
+//and a y-intercept of whatever the y of the initial point is
+if (dataLength === 1) {
+m = 0;
+b = data[0][1];
+} else {
+// Initialize our sums and scope the `m` and `b`
+// variables that define the line.
+var sumX = 0, sumY = 0,
+sumXX = 0, sumXY = 0;
+// Use local variables to grab point values
+// with minimal object property lookups
+var point, x, y;
+// Gather the sum of all x values, the sum of all
+// y values, and the sum of x^2 and (x*y) for each
+// value.
+//
+// In math notation, these would be SS_x, SS_y, SS_xx, and SS_xy
+for (var i = 0; i < dataLength; i++) {
+point = data[i];
+x = point[0];
+y = point[1];
+sumX += x;
+sumY += y;
+sumXX += x * x;
+sumXY += x * y;
+}
+// `m` is the slope of the regression line
+m = ((dataLength * sumXY) - (sumX * sumY)) /
+((dataLength * sumXX) - (sumX * sumX));
+// `b` is the y-intercept of the line.
+b = (sumY / dataLength) - ((m * sumX) / dataLength);
+}
+// Return both values as an object.
+return {
+m: m,
+b: b
+};
+}
+module.exports = linearRegression;
+},{}],22:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* Given the output of `linearRegression`: an object
+* with `m` and `b` values indicating slope and intercept,
+* respectively, generate a line function that translates
+* x values into y values.
+*
+* @param {Object} mb object with `m` and `b` members, representing
+* slope and intersect of desired line
+* @returns {Function} method that computes y-value at any given
+* x-value on the line.
+* @example
+* var l = linearRegressionLine(linearRegression([[0, 0], [1, 1]]));
+* l(0) // = 0
+* l(2) // = 2
+* linearRegressionLine({ b: 0, m: 1 })(1); // => 1
+* linearRegressionLine({ b: 1, m: 1 })(1); // => 2
+*/
+function linearRegressionLine(mb/*: { b: number, m: number }*/)/*: Function */ {
+// Return a function that computes a `y` value for each
+// x value it is given, based on the values of `b` and `a`
+// that we just computed.
+return function(x) {
+return mb.b + (mb.m * x);
+};
+}
+module.exports = linearRegressionLine;
+},{}],23:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* This computes the maximum number in an array.
+*
+* This runs on `O(n)`, linear time in respect to the array
+*
+* @param {Array<number>} x input
+* @returns {number} maximum value
+* @example
+* max([1, 2, 3, 4]);
+* // => 4
+*/
+function max(x /*: Array<number> */) /*:number*/ {
+var value;
+for (var i = 0; i < x.length; i++) {
+// On the first iteration of this loop, max is
+// NaN and is thus made the maximum element in the array
+if (value === undefined || x[i] > value) {
+value = x[i];
+}
+}
+if (value === undefined) {
+return NaN;
+}
+return value;
+}
+module.exports = max;
+},{}],24:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* The maximum is the highest number in the array. With a sorted array,
+* the last element in the array is always the largest, so this calculation
+* can be done in one step, or constant time.
+*
+* @param {Array<number>} x input
+* @returns {number} maximum value
+* @example
+* maxSorted([-100, -10, 1, 2, 5]); // => 5
+*/
+function maxSorted(x /*: Array<number> */)/*:number*/ {
+return x[x.length - 1];
+}
+module.exports = maxSorted;
+},{}],25:[function(require,module,exports){
+'use strict';
+/* @flow */
+var sum = require(56);
+/**
+* The mean, _also known as average_,
+* is the sum of all values over the number of values.
+* This is a [measure of central tendency](https://en.wikipedia.org/wiki/Central_tendency):
+* a method of finding a typical or central value of a set of numbers.
+*
+* This runs on `O(n)`, linear time in respect to the array
+*
+* @param {Array<number>} x input values
+* @returns {number} mean
+* @example
+* mean([0, 10]); // => 5
+*/
+function mean(x /*: Array<number> */)/*:number*/ {
+// The mean of no numbers is null
+if (x.length === 0) { return NaN; }
+return sum(x) / x.length;
+}
+module.exports = mean;
+},{"56":56}],26:[function(require,module,exports){
+'use strict';
+/* @flow */
+var quantile = require(40);
+/**
+* The [median](http://en.wikipedia.org/wiki/Median) is
+* the middle number of a list. This is often a good indicator of 'the middle'
+* when there are outliers that skew the `mean()` value.
+* This is a [measure of central tendency](https://en.wikipedia.org/wiki/Central_tendency):
+* a method of finding a typical or central value of a set of numbers.
+*
+* The median isn't necessarily one of the elements in the list: the value
+* can be the average of two elements if the list has an even length
+* and the two central values are different.
+*
+* @param {Array<number>} x input
+* @returns {number} median value
+* @example
+* median([10, 2, 5, 100, 2, 1]); // => 3.5
+*/
+function median(x /*: Array<number> */)/*:number*/ {
+return +quantile(x, 0.5);
+}
+module.exports = median;
+},{"40":40}],27:[function(require,module,exports){
+'use strict';
+/* @flow */
+var median = require(26);
+/**
+* The [Median Absolute Deviation](http://en.wikipedia.org/wiki/Median_absolute_deviation) is
+* a robust measure of statistical
+* dispersion. It is more resilient to outliers than the standard deviation.
+*
+* @param {Array<number>} x input array
+* @returns {number} median absolute deviation
+* @example
+* medianAbsoluteDeviation([1, 1, 2, 2, 4, 6, 9]); // => 1
+*/
+function medianAbsoluteDeviation(x /*: Array<number> */) {
+// The mad of nothing is null
+var medianValue = median(x),
+medianAbsoluteDeviations = [];
+// Make a list of absolute deviations from the median
+for (var i = 0; i < x.length; i++) {
+medianAbsoluteDeviations.push(Math.abs(x[i] - medianValue));
+}
+// Find the median value of that list
+return median(medianAbsoluteDeviations);
+}
+module.exports = medianAbsoluteDeviation;
+},{"26":26}],28:[function(require,module,exports){
+'use strict';
+/* @flow */
+var quantileSorted = require(41);
+/**
+* The [median](http://en.wikipedia.org/wiki/Median) is
+* the middle number of a list. This is often a good indicator of 'the middle'
+* when there are outliers that skew the `mean()` value.
+* This is a [measure of central tendency](https://en.wikipedia.org/wiki/Central_tendency):
+* a method of finding a typical or central value of a set of numbers.
+*
+* The median isn't necessarily one of the elements in the list: the value
+* can be the average of two elements if the list has an even length
+* and the two central values are different.
+*
+* @param {Array<number>} sorted input
+* @returns {number} median value
+* @example
+* medianSorted([10, 2, 5, 100, 2, 1]); // => 52.5
+*/
+function medianSorted(sorted /*: Array<number> */)/*:number*/ {
+return quantileSorted(sorted, 0.5);
+}
+module.exports = medianSorted;
+},{"41":41}],29:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* The min is the lowest number in the array. This runs on `O(n)`, linear time in respect to the array
+*
+* @param {Array<number>} x input
+* @returns {number} minimum value
+* @example
+* min([1, 5, -10, 100, 2]); // => -10
+*/
+function min(x /*: Array<number> */)/*:number*/ {
+var value;
+for (var i = 0; i < x.length; i++) {
+// On the first iteration of this loop, min is
+// NaN and is thus made the minimum element in the array
+if (value === undefined || x[i] < value) {
+value = x[i];
+}
+}
+if (value === undefined) {
+return NaN;
+}
+return value;
+}
+module.exports = min;
+},{}],30:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* The minimum is the lowest number in the array. With a sorted array,
+* the first element in the array is always the smallest, so this calculation
+* can be done in one step, or constant time.
+*
+* @param {Array<number>} x input
+* @returns {number} minimum value
+* @example
+* minSorted([-100, -10, 1, 2, 5]); // => -100
+*/
+function minSorted(x /*: Array<number> */)/*:number*/ {
+return x[0];
+}
+module.exports = minSorted;
+},{}],31:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* **Mixin** simple_statistics to a single Array instance if provided
+* or the Array native object if not. This is an optional
+* feature that lets you treat simple_statistics as a native feature
+* of Javascript.
+*
+* @param {Object} ss simple statistics
+* @param {Array} [array=] a single array instance which will be augmented
+* with the extra methods. If omitted, mixin will apply to all arrays
+* by changing the global `Array.prototype`.
+* @returns {*} the extended Array, or Array.prototype if no object
+* is given.
+*
+* @example
+* var myNumbers = [1, 2, 3];
+* mixin(ss, myNumbers);
+* console.log(myNumbers.sum()); // 6
+*/
+function mixin(ss /*: Object */, array /*: ?Array<any> */)/*: any */ {
+var support = !!(Object.defineProperty && Object.defineProperties);
+// Coverage testing will never test this error.
+/* istanbul ignore next */
+if (!support) {
+throw new Error('without defineProperty, simple-statistics cannot be mixed in');
+}
+// only methods which work on basic arrays in a single step
+// are supported
+var arrayMethods = ['median', 'standardDeviation', 'sum', 'product',
+'sampleSkewness',
+'mean', 'min', 'max', 'quantile', 'geometricMean',
+'harmonicMean', 'root_mean_square'];
+// create a closure with a method name so that a reference
+// like `arrayMethods[i]` doesn't follow the loop increment
+function wrap(method) {
+return function() {
+// cast any arguments into an array, since they're
+// natively objects
+var args = Array.prototype.slice.apply(arguments);
+// make the first argument the array itself
+args.unshift(this);
+// return the result of the ss method
+return ss[method].apply(ss, args);
+};
+}
+// select object to extend
+var extending;
+if (array) {
+// create a shallow copy of the array so that our internal
+// operations do not change it by reference
+extending = array.slice();
+} else {
+extending = Array.prototype;
+}
+// for each array function, define a function that gets
+// the array as the first argument.
+// We use [defineProperty](https://developer.mozilla.org/en-US/docs/JavaScript/Reference/Global_Objects/Object/defineProperty)
+// because it allows these properties to be non-enumerable:
+// `for (var in x)` loops will not run into problems with this
+// implementation.
+for (var i = 0; i < arrayMethods.length; i++) {
+Object.defineProperty(extending, arrayMethods[i], {
+value: wrap(arrayMethods[i]),
+configurable: true,
+enumerable: false,
+writable: true
+});
+}
+return extending;
+}
+module.exports = mixin;
+},{}],32:[function(require,module,exports){
+'use strict';
+/* @flow */
+var numericSort = require(34),
+modeSorted = require(33);
+/**
+* The [mode](http://bit.ly/W5K4Yt) is the number that appears in a list the highest number of times.
+* There can be multiple modes in a list: in the event of a tie, this
+* algorithm will return the most recently seen mode.
+*
+* This is a [measure of central tendency](https://en.wikipedia.org/wiki/Central_tendency):
+* a method of finding a typical or central value of a set of numbers.
+*
+* This runs on `O(nlog(n))` because it needs to sort the array internally
+* before running an `O(n)` search to find the mode.
+*
+* @param {Array<number>} x input
+* @returns {number} mode
+* @example
+* mode([0, 0, 1]); // => 0
+*/
+function mode(x /*: Array<number> */)/*:number*/ {
+// Sorting the array lets us iterate through it below and be sure
+// that every time we see a new number it's new and we'll never
+// see the same number twice
+return modeSorted(numericSort(x));
+}
+module.exports = mode;
+},{"33":33,"34":34}],33:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* The [mode](http://bit.ly/W5K4Yt) is the number that appears in a list the highest number of times.
+* There can be multiple modes in a list: in the event of a tie, this
+* algorithm will return the most recently seen mode.
+*
+* This is a [measure of central tendency](https://en.wikipedia.org/wiki/Central_tendency):
+* a method of finding a typical or central value of a set of numbers.
+*
+* This runs in `O(n)` because the input is sorted.
+*
+* @param {Array<number>} sorted input
+* @returns {number} mode
+* @example
+* modeSorted([0, 0, 1]); // => 0
+*/
+function modeSorted(sorted /*: Array<number> */)/*:number*/ {
+// Handle edge cases:
+// The mode of an empty list is NaN
+if (sorted.length === 0) { return NaN; }
+else if (sorted.length === 1) { return sorted[0]; }
+// This assumes it is dealing with an array of size > 1, since size
+// 0 and 1 are handled immediately. Hence it starts at index 1 in the
+// array.
+var last = sorted[0],
+// store the mode as we find new modes
+value = NaN,
+// store how many times we've seen the mode
+maxSeen = 0,
+// how many times the current candidate for the mode
+// has been seen
+seenThis = 1;
+// end at sorted.length + 1 to fix the case in which the mode is
+// the highest number that occurs in the sequence. the last iteration
+// compares sorted[i], which is undefined, to the highest number
+// in the series
+for (var i = 1; i < sorted.length + 1; i++) {
+// we're seeing a new number pass by
+if (sorted[i] !== last) {
+// the last number is the new mode since we saw it more
+// often than the old one
+if (seenThis > maxSeen) {
+maxSeen = seenThis;
+value = last;
+}
+seenThis = 1;
+last = sorted[i];
+// if this isn't a new number, it's one more occurrence of
+// the potential mode
+} else { seenThis++; }
+}
+return value;
+}
+module.exports = modeSorted;
+},{}],34:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* Sort an array of numbers by their numeric value, ensuring that the
+* array is not changed in place.
+*
+* This is necessary because the default behavior of .sort
+* in JavaScript is to sort arrays as string values
+*
+*     [1, 10, 12, 102, 20].sort()
+*     // output
+*     [1, 10, 102, 12, 20]
+*
+* @param {Array<number>} array input array
+* @return {Array<number>} sorted array
+* @private
+* @example
+* numericSort([3, 2, 1]) // => [1, 2, 3]
+*/
+function numericSort(array /*: Array<number> */) /*: Array<number> */ {
+return array
+// ensure the array is not changed in-place
+.slice()
+// comparator function that treats input as numeric
+.sort(function(a, b) {
+return a - b;
+});
+}
+module.exports = numericSort;
+},{}],35:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* This is a single-layer [Perceptron Classifier](http://en.wikipedia.org/wiki/Perceptron) that takes
+* arrays of numbers and predicts whether they should be classified
+* as either 0 or 1 (negative or positive examples).
+* @class
+* @example
+* // Create the model
+* var p = new PerceptronModel();
+* // Train the model with input with a diagonal boundary.
+* for (var i = 0; i < 5; i++) {
+*     p.train([1, 1], 1);
+*     p.train([0, 1], 0);
+*     p.train([1, 0], 0);
+*     p.train([0, 0], 0);
+* }
+* p.predict([0, 0]); // 0
+* p.predict([0, 1]); // 0
+* p.predict([1, 0]); // 0
+* p.predict([1, 1]); // 1
+*/
+function PerceptronModel() {
+// The weights, or coefficients of the model;
+// weights are only populated when training with data.
+this.weights = [];
+// The bias term, or intercept; it is also a weight but
+// it's stored separately for convenience as it is always
+// multiplied by one.
+this.bias = 0;
+}
+/**
+* **Predict**: Use an array of features with the weight array and bias
+* to predict whether an example is labeled 0 or 1.
+*
+* @param {Array<number>} features an array of features as numbers
+* @returns {number} 1 if the score is over 0, otherwise 0
+*/
+PerceptronModel.prototype.predict = function(features) {
+// Only predict if previously trained
+// on the same size feature array(s).
+if (features.length !== this.weights.length) { return null; }
+// Calculate the sum of features times weights,
+// with the bias added (implicitly times one).
+var score = 0;
+for (var i = 0; i < this.weights.length; i++) {
+score += this.weights[i] * features[i];
+}
+score += this.bias;
+// Classify as 1 if the score is over 0, otherwise 0.
+if (score > 0) {
+return 1;
+} else {
+return 0;
+}
+};
+/**
+* **Train** the classifier with a new example, which is
+* a numeric array of features and a 0 or 1 label.
+*
+* @param {Array<number>} features an array of features as numbers
+* @param {number} label either 0 or 1
+* @returns {PerceptronModel} this
+*/
+PerceptronModel.prototype.train = function(features, label) {
+// Require that only labels of 0 or 1 are considered.
+if (label !== 0 && label !== 1) { return null; }
+// The length of the feature array determines
+// the length of the weight array.
+// The perceptron will continue learning as long as
+// it keeps seeing feature arrays of the same length.
+// When it sees a new data shape, it initializes.
+if (features.length !== this.weights.length) {
+this.weights = features;
+this.bias = 1;
+}
+// Make a prediction based on current weights.
+var prediction = this.predict(features);
+// Update the weights if the prediction is wrong.
+if (prediction !== label) {
+var gradient = label - prediction;
+for (var i = 0; i < this.weights.length; i++) {
+this.weights[i] += gradient * features[i];
+}
+this.bias += gradient;
+}
+return this;
+};
+module.exports = PerceptronModel;
+},{}],36:[function(require,module,exports){
+/* @flow */
+'use strict';
+/**
+* Implementation of [Heap's Algorithm](https://en.wikipedia.org/wiki/Heap%27s_algorithm)
+* for generating permutations.
+*
+* @param {Array} elements any type of data
+* @returns {Array<Array>} array of permutations
+*/
+function permutationsHeap/*:: <T> */(elements /*: Array<T> */)/*: Array<Array<T>> */ {
+var indexes = new Array(elements.length);
+var permutations = [elements.slice()];
+for (var i = 0; i < elements.length; i++) {
+indexes[i] = 0;
+}
+for (i = 0; i < elements.length;) {
+if (indexes[i] < i) {
+// At odd indexes, swap from indexes[i] instead
+// of from the beginning of the array
+var swapFrom = 0;
+if (i % 2 !== 0) {
+swapFrom = indexes[i];
+}
+// swap between swapFrom and i, using
+// a temporary variable as storage.
+var temp = elements[swapFrom];
+elements[swapFrom] = elements[i];
+elements[i] = temp;
+permutations.push(elements.slice());
+indexes[i]++;
+i = 0;
+} else {
+indexes[i] = 0;
+i++;
+}
+}
+return permutations;
+}
+module.exports = permutationsHeap;
+},{}],37:[function(require,module,exports){
+'use strict';
+/* @flow */
+var epsilon = require(13);
+var factorial = require(16);
+/**
+* The [Poisson Distribution](http://en.wikipedia.org/wiki/Poisson_distribution)
+* is a discrete probability distribution that expresses the probability
+* of a given number of events occurring in a fixed interval of time
+* and/or space if these events occur with a known average rate and
+* independently of the time since the last event.
+*
+* The Poisson Distribution is characterized by the strictly positive
+* mean arrival or occurrence rate, `λ`.
+*
+* @param {number} lambda location poisson distribution
+* @returns {number} value of poisson distribution at that point
+*/
+function poissonDistribution(lambda/*: number */) {
+// Check that lambda is strictly positive
+if (lambda <= 0) { return undefined; }
+// our current place in the distribution
+var x = 0,
+// and we keep track of the current cumulative probability, in
+// order to know when to stop calculating chances.
+cumulativeProbability = 0,
+// the calculated cells to be returned
+cells = {};
+// This algorithm iterates through each potential outcome,
+// until the `cumulativeProbability` is very close to 1, at
+// which point we've defined the vast majority of outcomes
+do {
+// a [probability mass function](https://en.wikipedia.org/wiki/Probability_mass_function)
+cells[x] = (Math.pow(Math.E, -lambda) * Math.pow(lambda, x)) / factorial(x);
+cumulativeProbability += cells[x];
+x++;
+// when the cumulativeProbability is nearly 1, we've calculated
+// the useful range of this distribution
+} while (cumulativeProbability < 1 - epsilon);
+return cells;
+}
+module.exports = poissonDistribution;
+},{"13":13,"16":16}],38:[function(require,module,exports){
+'use strict';
+/* @flow */
+var epsilon = require(13);
+var inverseErrorFunction = require(20);
+/**
+* The [Probit](http://en.wikipedia.org/wiki/Probit)
+* is the inverse of cumulativeStdNormalProbability(),
+* and is also known as the normal quantile function.
+*
+* It returns the number of standard deviations from the mean
+* where the p'th quantile of values can be found in a normal distribution.
+* So, for example, probit(0.5 + 0.6827/2) ≈ 1 because 68.27% of values are
+* normally found within 1 standard deviation above or below the mean.
+*
+* @param {number} p
+* @returns {number} probit
+*/
+function probit(p /*: number */)/*: number */ {
+if (p === 0) {
+p = epsilon;
+} else if (p >= 1) {
+p = 1 - epsilon;
+}
+return Math.sqrt(2) * inverseErrorFunction(2 * p - 1);
+}
+module.exports = probit;
+},{"13":13,"20":20}],39:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* The [product](https://en.wikipedia.org/wiki/Product_(mathematics)) of an array
+* is the result of multiplying all numbers together, starting using one as the multiplicative identity.
+*
+* This runs on `O(n)`, linear time in respect to the array
+*
+* @param {Array<number>} x input
+* @return {number} product of all input numbers
+* @example
+* product([1, 2, 3, 4]); // => 24
+*/
+function product(x/*: Array<number> */)/*: number */ {
+var value = 1;
+for (var i = 0; i < x.length; i++) {
+value *= x[i];
+}
+return value;
+}
+module.exports = product;
+},{}],40:[function(require,module,exports){
+'use strict';
+/* @flow */
+var quantileSorted = require(41);
+var quickselect = require(42);
+/**
+* The [quantile](https://en.wikipedia.org/wiki/Quantile):
+* this is a population quantile, since we assume to know the entire
+* dataset in this library. This is an implementation of the
+* [Quantiles of a Population](http://en.wikipedia.org/wiki/Quantile#Quantiles_of_a_population)
+* algorithm from wikipedia.
+*
+* Sample is a one-dimensional array of numbers,
+* and p is either a decimal number from 0 to 1 or an array of decimal
+* numbers from 0 to 1.
+* In terms of a k/q quantile, p = k/q - it's just dealing with fractions or dealing
+* with decimal values.
+* When p is an array, the result of the function is also an array containing the appropriate
+* quantiles in input order
+*
+* @param {Array<number>} sample a sample from the population
+* @param {number} p the desired quantile, as a number between 0 and 1
+* @returns {number} quantile
+* @example
+* quantile([3, 6, 7, 8, 8, 9, 10, 13, 15, 16, 20], 0.5); // => 9
+*/
+function quantile(sample /*: Array<number> */, p /*: Array<number> | number */) {
+var copy = sample.slice();
+if (Array.isArray(p)) {
+// rearrange elements so that each element corresponding to a requested
+// quantile is on a place it would be if the array was fully sorted
+multiQuantileSelect(copy, p);
+// Initialize the result array
+var results = [];
+// For each requested quantile
+for (var i = 0; i < p.length; i++) {
+results[i] = quantileSorted(copy, p[i]);
+}
+return results;
+} else {
+var idx = quantileIndex(copy.length, p);
+quantileSelect(copy, idx, 0, copy.length - 1);
+return quantileSorted(copy, p);
+}
+}
+function quantileSelect(arr, k, left, right) {
+if (k % 1 === 0) {
+quickselect(arr, k, left, right);
+} else {
+k = Math.floor(k);
+quickselect(arr, k, left, right);
+quickselect(arr, k + 1, k + 1, right);
+}
+}
+function multiQuantileSelect(arr, p) {
+var indices = [0];
+for (var i = 0; i < p.length; i++) {
+indices.push(quantileIndex(arr.length, p[i]));
+}
+indices.push(arr.length - 1);
+indices.sort(compare);
+var stack = [0, indices.length - 1];
+while (stack.length) {
+var r = Math.ceil(stack.pop());
+var l = Math.floor(stack.pop());
+if (r - l <= 1) continue;
+var m = Math.floor((l + r) / 2);
+quantileSelect(arr, indices[m], indices[l], indices[r]);
+stack.push(l, m, m, r);
+}
+}
+function compare(a, b) {
+return a - b;
+}
+function quantileIndex(len /*: number */, p /*: number */)/*:number*/ {
+var idx = len * p;
+if (p === 1) {
+// If p is 1, directly return the last index
+return len - 1;
+} else if (p === 0) {
+// If p is 0, directly return the first index
+return 0;
+} else if (idx % 1 !== 0) {
+// If index is not integer, return the next index in array
+return Math.ceil(idx) - 1;
+} else if (len % 2 === 0) {
+// If the list has even-length, we'll return the middle of two indices
+// around quantile to indicate that we need an average value of the two
+return idx - 0.5;
+} else {
+// Finally, in the simple case of an integer index
+// with an odd-length list, return the index
+return idx;
+}
+}
+module.exports = quantile;
+},{"41":41,"42":42}],41:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* This is the internal implementation of quantiles: when you know
+* that the order is sorted, you don't need to re-sort it, and the computations
+* are faster.
+*
+* @param {Array<number>} sample input data
+* @param {number} p desired quantile: a number between 0 to 1, inclusive
+* @returns {number} quantile value
+* @example
+* quantileSorted([3, 6, 7, 8, 8, 9, 10, 13, 15, 16, 20], 0.5); // => 9
+*/
+function quantileSorted(sample /*: Array<number> */, p /*: number */)/*:number*/ {
+var idx = sample.length * p;
+if (p < 0 || p > 1) {
+return NaN;
+} else if (p === 1) {
+// If p is 1, directly return the last element
+return sample[sample.length - 1];
+} else if (p === 0) {
+// If p is 0, directly return the first element
+return sample[0];
+} else if (idx % 1 !== 0) {
+// If p is not integer, return the next element in array
+return sample[Math.ceil(idx) - 1];
+} else if (sample.length % 2 === 0) {
+// If the list has even-length, we'll take the average of this number
+// and the next value, if there is one
+return (sample[idx - 1] + sample[idx]) / 2;
+} else {
+// Finally, in the simple case of an integer value
+// with an odd-length list, return the sample value at the index.
+return sample[idx];
+}
+}
+module.exports = quantileSorted;
+},{}],42:[function(require,module,exports){
+'use strict';
+/* @flow */
+module.exports = quickselect;
+/**
+* Rearrange items in `arr` so that all items in `[left, k]` range are the smallest.
+* The `k`-th element will have the `(k - left + 1)`-th smallest value in `[left, right]`.
+*
+* Implements Floyd-Rivest selection algorithm https://en.wikipedia.org/wiki/Floyd-Rivest_algorithm
+*
+* @private
+* @param {Array<number>} arr input array
+* @param {number} k pivot index
+* @param {number} left left index
+* @param {number} right right index
+* @returns {undefined}
+* @example
+* var arr = [65, 28, 59, 33, 21, 56, 22, 95, 50, 12, 90, 53, 28, 77, 39];
+* quickselect(arr, 8);
+* // = [39, 28, 28, 33, 21, 12, 22, 50, 53, 56, 59, 65, 90, 77, 95]
+*/
+function quickselect(arr /*: Array<number> */, k /*: number */, left /*: number */, right /*: number */) {
+left = left || 0;
+right = right || (arr.length - 1);
+while (right > left) {
+// 600 and 0.5 are arbitrary constants chosen in the original paper to minimize execution time
+if (right - left > 600) {
+var n = right - left + 1;
+var m = k - left + 1;
+var z = Math.log(n);
+var s = 0.5 * Math.exp(2 * z / 3);
+var sd = 0.5 * Math.sqrt(z * s * (n - s) / n);
+if (m - n / 2 < 0) sd *= -1;
+var newLeft = Math.max(left, Math.floor(k - m * s / n + sd));
+var newRight = Math.min(right, Math.floor(k + (n - m) * s / n + sd));
+quickselect(arr, k, newLeft, newRight);
+}
+var t = arr[k];
+var i = left;
+var j = right;
+swap(arr, left, k);
+if (arr[right] > t) swap(arr, left, right);
+while (i < j) {
+swap(arr, i, j);
+i++;
+j--;
+while (arr[i] < t) i++;
+while (arr[j] > t) j--;
+}
+if (arr[left] === t) swap(arr, left, j);
+else {
+j++;
+swap(arr, j, right);
+}
+if (j <= k) left = j + 1;
+if (k <= j) right = j - 1;
+}
+}
+function swap(arr, i, j) {
+var tmp = arr[i];
+arr[i] = arr[j];
+arr[j] = tmp;
+}
+},{}],43:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* The [R Squared](http://en.wikipedia.org/wiki/Coefficient_of_determination)
+* value of data compared with a function `f`
+* is the sum of the squared differences between the prediction
+* and the actual value.
+*
+* @param {Array<Array<number>>} data input data: this should be doubly-nested
+* @param {Function} func function called on `[i][0]` values within the dataset
+* @returns {number} r-squared value
+* @example
+* var samples = [[0, 0], [1, 1]];
+* var regressionLine = linearRegressionLine(linearRegression(samples));
+* rSquared(samples, regressionLine); // = 1 this line is a perfect fit
+*/
+function rSquared(data /*: Array<Array<number>> */, func /*: Function */) /*: number */ {
+if (data.length < 2) { return 1; }
+// Compute the average y value for the actual
+// data set in order to compute the
+// _total sum of squares_
+var sum = 0, average;
+for (var i = 0; i < data.length; i++) {
+sum += data[i][1];
+}
+average = sum / data.length;
+// Compute the total sum of squares - the
+// squared difference between each point
+// and the average of all points.
+var sumOfSquares = 0;
+for (var j = 0; j < data.length; j++) {
+sumOfSquares += Math.pow(average - data[j][1], 2);
+}
+// Finally estimate the error: the squared
+// difference between the estimate and the actual data
+// value at each point.
+var err = 0;
+for (var k = 0; k < data.length; k++) {
+err += Math.pow(data[k][1] - func(data[k][0]), 2);
+}
+// As the error grows larger, its ratio to the
+// sum of squares increases and the r squared
+// value grows lower.
+return 1 - err / sumOfSquares;
+}
+module.exports = rSquared;
+},{}],44:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* The Root Mean Square (RMS) is
+* a mean function used as a measure of the magnitude of a set
+* of numbers, regardless of their sign.
+* This is the square root of the mean of the squares of the
+* input numbers.
+* This runs on `O(n)`, linear time in respect to the array
+*
+* @param {Array<number>} x input
+* @returns {number} root mean square
+* @example
+* rootMeanSquare([-1, 1, -1, 1]); // => 1
+*/
+function rootMeanSquare(x /*: Array<number> */)/*:number*/ {
+if (x.length === 0) { return NaN; }
+var sumOfSquares = 0;
+for (var i = 0; i < x.length; i++) {
+sumOfSquares += Math.pow(x[i], 2);
+}
+return Math.sqrt(sumOfSquares / x.length);
+}
+module.exports = rootMeanSquare;
+},{}],45:[function(require,module,exports){
+'use strict';
+/* @flow */
+var shuffle = require(51);
+/**
+* Create a [simple random sample](http://en.wikipedia.org/wiki/Simple_random_sample)
+* from a given array of `n` elements.
+*
+* The sampled values will be in any order, not necessarily the order
+* they appear in the input.
+*
+* @param {Array} array input array. can contain any type
+* @param {number} n count of how many elements to take
+* @param {Function} [randomSource=Math.random] an optional source of entropy
+* instead of Math.random
+* @return {Array} subset of n elements in original array
+* @example
+* var values = [1, 2, 4, 5, 6, 7, 8, 9];
+* sample(values, 3); // returns 3 random values, like [2, 5, 8];
+*/
+function sample/*:: <T> */(
+array /*: Array<T> */,
+n /*: number */,
+randomSource /*: Function */) /*: Array<T> */ {
+// shuffle the original array using a fisher-yates shuffle
+var shuffled = shuffle(array, randomSource);
+// and then return a subset of it - the first `n` elements.
+return shuffled.slice(0, n);
+}
+module.exports = sample;
+},{"51":51}],46:[function(require,module,exports){
+'use strict';
+/* @flow */
+var sampleCovariance = require(47);
+var sampleStandardDeviation = require(49);
+/**
+* The [correlation](http://en.wikipedia.org/wiki/Correlation_and_dependence) is
+* a measure of how correlated two datasets are, between -1 and 1
+*
+* @param {Array<number>} x first input
+* @param {Array<number>} y second input
+* @returns {number} sample correlation
+* @example
+* sampleCorrelation([1, 2, 3, 4, 5, 6], [2, 2, 3, 4, 5, 60]).toFixed(2);
+* // => '0.69'
+*/
+function sampleCorrelation(x/*: Array<number> */, y/*: Array<number> */)/*:number*/ {
+var cov = sampleCovariance(x, y),
+xstd = sampleStandardDeviation(x),
+ystd = sampleStandardDeviation(y);
+return cov / xstd / ystd;
+}
+module.exports = sampleCorrelation;
+},{"47":47,"49":49}],47:[function(require,module,exports){
+'use strict';
+/* @flow */
+var mean = require(25);
+/**
+* [Sample covariance](https://en.wikipedia.org/wiki/Sample_mean_and_sampleCovariance) of two datasets:
+* how much do the two datasets move together?
+* x and y are two datasets, represented as arrays of numbers.
+*
+* @param {Array<number>} x first input
+* @param {Array<number>} y second input
+* @returns {number} sample covariance
+* @example
+* sampleCovariance([1, 2, 3, 4, 5, 6], [6, 5, 4, 3, 2, 1]); // => -3.5
+*/
+function sampleCovariance(x /*:Array<number>*/, y /*:Array<number>*/)/*:number*/ {
+// The two datasets must have the same length which must be more than 1
+if (x.length <= 1 || x.length !== y.length) {
+return NaN;
+}
+// determine the mean of each dataset so that we can judge each
+// value of the dataset fairly as the difference from the mean. this
+// way, if one dataset is [1, 2, 3] and [2, 3, 4], their covariance
+// does not suffer because of the difference in absolute values
+var xmean = mean(x),
+ymean = mean(y),
+sum = 0;
+// for each pair of values, the covariance increases when their
+// difference from the mean is associated - if both are well above
+// or if both are well below
+// the mean, the covariance increases significantly.
+for (var i = 0; i < x.length; i++) {
+sum += (x[i] - xmean) * (y[i] - ymean);
+}
+// this is Bessels' Correction: an adjustment made to sample statistics
+// that allows for the reduced degree of freedom entailed in calculating
+// values from samples rather than complete populations.
+var besselsCorrection = x.length - 1;
+// the covariance is weighted by the length of the datasets.
+return sum / besselsCorrection;
+}
+module.exports = sampleCovariance;
+},{"25":25}],48:[function(require,module,exports){
+'use strict';
+/* @flow */
+var sumNthPowerDeviations = require(57);
+var sampleStandardDeviation = require(49);
+/**
+* [Skewness](http://en.wikipedia.org/wiki/Skewness) is
+* a measure of the extent to which a probability distribution of a
+* real-valued random variable "leans" to one side of the mean.
+* The skewness value can be positive or negative, or even undefined.
+*
+* Implementation is based on the adjusted Fisher-Pearson standardized
+* moment coefficient, which is the version found in Excel and several
+* statistical packages including Minitab, SAS and SPSS.
+*
+* @param {Array<number>} x input
+* @returns {number} sample skewness
+* @example
+* sampleSkewness([2, 4, 6, 3, 1]); // => 0.590128656384365
+*/
+function sampleSkewness(x /*: Array<number> */)/*:number*/ {
+// The skewness of less than three arguments is null
+var theSampleStandardDeviation = sampleStandardDeviation(x);
+if (isNaN(theSampleStandardDeviation) || x.length < 3) {
+return NaN;
+}
+var n = x.length,
+cubedS = Math.pow(theSampleStandardDeviation, 3),
+sumCubedDeviations = sumNthPowerDeviations(x, 3);
+return n * sumCubedDeviations / ((n - 1) * (n - 2) * cubedS);
+}
+module.exports = sampleSkewness;
+},{"49":49,"57":57}],49:[function(require,module,exports){
+'use strict';
+/* @flow */
+var sampleVariance = require(50);
+/**
+* The [standard deviation](http://en.wikipedia.org/wiki/Standard_deviation)
+* is the square root of the variance.
+*
+* @param {Array<number>} x input array
+* @returns {number} sample standard deviation
+* @example
+* sampleStandardDeviation([2, 4, 4, 4, 5, 5, 7, 9]).toFixed(2);
+* // => '2.14'
+*/
+function sampleStandardDeviation(x/*:Array<number>*/)/*:number*/ {
+// The standard deviation of no numbers is null
+var sampleVarianceX = sampleVariance(x);
+if (isNaN(sampleVarianceX)) { return NaN; }
+return Math.sqrt(sampleVarianceX);
+}
+module.exports = sampleStandardDeviation;
+},{"50":50}],50:[function(require,module,exports){
+'use strict';
+/* @flow */
+var sumNthPowerDeviations = require(57);
+/*
+* The [sample variance](https://en.wikipedia.org/wiki/Variance#Sample_variance)
+* is the sum of squared deviations from the mean. The sample variance
+* is distinguished from the variance by the usage of [Bessel's Correction](https://en.wikipedia.org/wiki/Bessel's_correction):
+* instead of dividing the sum of squared deviations by the length of the input,
+* it is divided by the length minus one. This corrects the bias in estimating
+* a value from a set that you don't know if full.
+*
+* References:
+* * [Wolfram MathWorld on Sample Variance](http://mathworld.wolfram.com/SampleVariance.html)
+*
+* @param {Array<number>} x input array
+* @return {number} sample variance
+* @example
+* sampleVariance([1, 2, 3, 4, 5]); // => 2.5
+*/
+function sampleVariance(x /*: Array<number> */)/*:number*/ {
+// The variance of no numbers is null
+if (x.length <= 1) { return NaN; }
+var sumSquaredDeviationsValue = sumNthPowerDeviations(x, 2);
+// this is Bessels' Correction: an adjustment made to sample statistics
+// that allows for the reduced degree of freedom entailed in calculating
+// values from samples rather than complete populations.
+var besselsCorrection = x.length - 1;
+// Find the mean value of that list
+return sumSquaredDeviationsValue / besselsCorrection;
+}
+module.exports = sampleVariance;
+},{"57":57}],51:[function(require,module,exports){
+'use strict';
+/* @flow */
+var shuffleInPlace = require(52);
+/*
+* A [Fisher-Yates shuffle](http://en.wikipedia.org/wiki/Fisher%E2%80%93Yates_shuffle)
+* is a fast way to create a random permutation of a finite set. This is
+* a function around `shuffle_in_place` that adds the guarantee that
+* it will not modify its input.
+*
+* @param {Array} sample an array of any kind of element
+* @param {Function} [randomSource=Math.random] an optional entropy source
+* @return {Array} shuffled version of input
+* @example
+* var shuffled = shuffle([1, 2, 3, 4]);
+* shuffled; // = [2, 3, 1, 4] or any other random permutation
+*/
+function shuffle/*::<T>*/(sample/*:Array<T>*/, randomSource/*:Function*/) {
+// slice the original array so that it is not modified
+sample = sample.slice();
+// and then shuffle that shallow-copied array, in place
+return shuffleInPlace(sample.slice(), randomSource);
+}
+module.exports = shuffle;
+},{"52":52}],52:[function(require,module,exports){
+'use strict';
+/* @flow */
+/*
+* A [Fisher-Yates shuffle](http://en.wikipedia.org/wiki/Fisher%E2%80%93Yates_shuffle)
+* in-place - which means that it **will change the order of the original
+* array by reference**.
+*
+* This is an algorithm that generates a random [permutation](https://en.wikipedia.org/wiki/Permutation)
+* of a set.
+*
+* @param {Array} sample input array
+* @param {Function} [randomSource=Math.random] an optional source of entropy
+* @returns {Array} sample
+* @example
+* var sample = [1, 2, 3, 4];
+* shuffleInPlace(sample);
+* // sample is shuffled to a value like [2, 1, 4, 3]
+*/
+function shuffleInPlace(sample/*:Array<any>*/, randomSource/*:Function*/)/*:Array<any>*/ {
+// a custom random number source can be provided if you want to use
+// a fixed seed or another random number generator, like
+// [random-js](https://www.npmjs.org/package/random-js)
+randomSource = randomSource || Math.random;
+// store the current length of the sample to determine
+// when no elements remain to shuffle.
+var length = sample.length;
+// temporary is used to hold an item when it is being
+// swapped between indices.
+var temporary;
+// The index to swap at each stage.
+var index;
+// While there are still items to shuffle
+while (length > 0) {
+// chose a random index within the subset of the array
+// that is not yet shuffled
+index = Math.floor(randomSource() * length--);
+// store the value that we'll move temporarily
+temporary = sample[length];
+// swap the value at `sample[length]` with `sample[index]`
+sample[length] = sample[index];
+sample[index] = temporary;
+}
+return sample;
+}
+module.exports = shuffleInPlace;
+},{}],53:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* [Sign](https://en.wikipedia.org/wiki/Sign_function) is a function
+* that extracts the sign of a real number
+*
+* @param {Number} x input value
+* @returns {Number} sign value either 1, 0 or -1
+* @throws {TypeError} if the input argument x is not a number
+* @private
+*
+* @example
+* sign(2); // => 1
+*/
+function sign(x/*: number */)/*: number */ {
+if (typeof x === 'number') {
+if (x < 0) {
+return -1;
+} else if (x === 0) {
+return 0
+} else {
+return 1;
+}
+} else {
+throw new TypeError('not a number');
+}
+}
+module.exports = sign;
+},{}],54:[function(require,module,exports){
+'use strict';
+/* @flow */
+var variance = require(62);
+/**
+* The [standard deviation](http://en.wikipedia.org/wiki/Standard_deviation)
+* is the square root of the variance. It's useful for measuring the amount
+* of variation or dispersion in a set of values.
+*
+* Standard deviation is only appropriate for full-population knowledge: for
+* samples of a population, {@link sampleStandardDeviation} is
+* more appropriate.
+*
+* @param {Array<number>} x input
+* @returns {number} standard deviation
+* @example
+* variance([2, 4, 4, 4, 5, 5, 7, 9]); // => 4
+* standardDeviation([2, 4, 4, 4, 5, 5, 7, 9]); // => 2
+*/
+function standardDeviation(x /*: Array<number> */)/*:number*/ {
+// The standard deviation of no numbers is null
+var v = variance(x);
+if (isNaN(v)) { return 0; }
+return Math.sqrt(v);
+}
+module.exports = standardDeviation;
+},{"62":62}],55:[function(require,module,exports){
+'use strict';
+/* @flow */
+var SQRT_2PI = Math.sqrt(2 * Math.PI);
+function cumulativeDistribution(z) {
+var sum = z,
+tmp = z;
+// 15 iterations are enough for 4-digit precision
+for (var i = 1; i < 15; i++) {
+tmp *= z * z / (2 * i + 1);
+sum += tmp;
+}
+return Math.round((0.5 + (sum / SQRT_2PI) * Math.exp(-z * z / 2)) * 1e4) / 1e4;
+}
+/**
+* A standard normal table, also called the unit normal table or Z table,
+* is a mathematical table for the values of Φ (phi), which are the values of
+* the cumulative distribution function of the normal distribution.
+* It is used to find the probability that a statistic is observed below,
+* above, or between values on the standard normal distribution, and by
+* extension, any normal distribution.
+*
+* The probabilities are calculated using the
+* [Cumulative distribution function](https://en.wikipedia.org/wiki/Normal_distribution#Cumulative_distribution_function).
+* The table used is the cumulative, and not cumulative from 0 to mean
+* (even though the latter has 5 digits precision, instead of 4).
+*/
+var standardNormalTable/*: Array<number> */ = [];
+for (var z = 0; z <= 3.09; z += 0.01) {
+standardNormalTable.push(cumulativeDistribution(z));
+}
+module.exports = standardNormalTable;
+},{}],56:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* Our default sum is the [Kahan summation algorithm](https://en.wikipedia.org/wiki/Kahan_summation_algorithm) is
+* a method for computing the sum of a list of numbers while correcting
+* for floating-point errors. Traditionally, sums are calculated as many
+* successive additions, each one with its own floating-point roundoff. These
+* losses in precision add up as the number of numbers increases. This alternative
+* algorithm is more accurate than the simple way of calculating sums by simple
+* addition.
+*
+* This runs on `O(n)`, linear time in respect to the array
+*
+* @param {Array<number>} x input
+* @return {number} sum of all input numbers
+* @example
+* sum([1, 2, 3]); // => 6
+*/
+function sum(x/*: Array<number> */)/*: number */ {
+// like the traditional sum algorithm, we keep a running
+// count of the current sum.
+var sum = 0;
+// but we also keep three extra variables as bookkeeping:
+// most importantly, an error correction value. This will be a very
+// small number that is the opposite of the floating point precision loss.
+var errorCompensation = 0;
+// this will be each number in the list corrected with the compensation value.
+var correctedCurrentValue;
+// and this will be the next sum
+var nextSum;
+for (var i = 0; i < x.length; i++) {
+// first correct the value that we're going to add to the sum
+correctedCurrentValue = x[i] - errorCompensation;
+// compute the next sum. sum is likely a much larger number
+// than correctedCurrentValue, so we'll lose precision here,
+// and measure how much precision is lost in the next step
+nextSum = sum + correctedCurrentValue;
+// we intentionally didn't assign sum immediately, but stored
+// it for now so we can figure out this: is (sum + nextValue) - nextValue
+// not equal to 0? ideally it would be, but in practice it won't:
+// it will be some very small number. that's what we record
+// as errorCompensation.
+errorCompensation = nextSum - sum - correctedCurrentValue;
+// now that we've computed how much we'll correct for in the next
+// loop, start treating the nextSum as the current sum.
+sum = nextSum;
+}
+return sum;
+}
+module.exports = sum;
+},{}],57:[function(require,module,exports){
+'use strict';
+/* @flow */
+var mean = require(25);
+/**
+* The sum of deviations to the Nth power.
+* When n=2 it's the sum of squared deviations.
+* When n=3 it's the sum of cubed deviations.
+*
+* @param {Array<number>} x
+* @param {number} n power
+* @returns {number} sum of nth power deviations
+* @example
+* var input = [1, 2, 3];
+* // since the variance of a set is the mean squared
+* // deviations, we can calculate that with sumNthPowerDeviations:
+* var variance = sumNthPowerDeviations(input) / input.length;
+*/
+function sumNthPowerDeviations(x/*: Array<number> */, n/*: number */)/*:number*/ {
+var meanValue = mean(x),
+sum = 0;
+for (var i = 0; i < x.length; i++) {
+sum += Math.pow(x[i] - meanValue, n);
+}
+return sum;
+}
+module.exports = sumNthPowerDeviations;
+},{"25":25}],58:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* The simple [sum](https://en.wikipedia.org/wiki/Summation) of an array
+* is the result of adding all numbers together, starting from zero.
+*
+* This runs on `O(n)`, linear time in respect to the array
+*
+* @param {Array<number>} x input
+* @return {number} sum of all input numbers
+* @example
+* sumSimple([1, 2, 3]); // => 6
+*/
+function sumSimple(x/*: Array<number> */)/*: number */ {
+var value = 0;
+for (var i = 0; i < x.length; i++) {
+value += x[i];
+}
+return value;
+}
+module.exports = sumSimple;
+},{}],59:[function(require,module,exports){
+'use strict';
+/* @flow */
+var standardDeviation = require(54);
+var mean = require(25);
+/**
+* This is to compute [a one-sample t-test](https://en.wikipedia.org/wiki/Student%27s_t-test#One-sample_t-test), comparing the mean
+* of a sample to a known value, x.
+*
+* in this case, we're trying to determine whether the
+* population mean is equal to the value that we know, which is `x`
+* here. usually the results here are used to look up a
+* [p-value](http://en.wikipedia.org/wiki/P-value), which, for
+* a certain level of significance, will let you determine that the
+* null hypothesis can or cannot be rejected.
+*
+* @param {Array<number>} sample an array of numbers as input
+* @param {number} x expected value of the population mean
+* @returns {number} value
+* @example
+* tTest([1, 2, 3, 4, 5, 6], 3.385).toFixed(2); // => '0.16'
+*/
+function tTest(sample/*: Array<number> */, x/*: number */)/*:number*/ {
+// The mean of the sample
+var sampleMean = mean(sample);
+// The standard deviation of the sample
+var sd = standardDeviation(sample);
+// Square root the length of the sample
+var rootN = Math.sqrt(sample.length);
+// returning the t value
+return (sampleMean - x) / (sd / rootN);
+}
+module.exports = tTest;
+},{"25":25,"54":54}],60:[function(require,module,exports){
+'use strict';
+/* @flow */
+var mean = require(25);
+var sampleVariance = require(50);
+/**
+* This is to compute [two sample t-test](http://en.wikipedia.org/wiki/Student's_t-test).
+* Tests whether "mean(X)-mean(Y) = difference", (
+* in the most common case, we often have `difference == 0` to test if two samples
+* are likely to be taken from populations with the same mean value) with
+* no prior knowledge on standard deviations of both samples
+* other than the fact that they have the same standard deviation.
+*
+* Usually the results here are used to look up a
+* [p-value](http://en.wikipedia.org/wiki/P-value), which, for
+* a certain level of significance, will let you determine that the
+* null hypothesis can or cannot be rejected.
+*
+* `diff` can be omitted if it equals 0.
+*
+* [This is used to confirm or deny](http://www.monarchlab.org/Lab/Research/Stats/2SampleT.aspx)
+* a null hypothesis that the two populations that have been sampled into
+* `sampleX` and `sampleY` are equal to each other.
+*
+* @param {Array<number>} sampleX a sample as an array of numbers
+* @param {Array<number>} sampleY a sample as an array of numbers
+* @param {number} [difference=0]
+* @returns {number} test result
+* @example
+* ss.tTestTwoSample([1, 2, 3, 4], [3, 4, 5, 6], 0); //= -2.1908902300206643
+*/
+function tTestTwoSample(
+sampleX/*: Array<number> */,
+sampleY/*: Array<number> */,
+difference/*: number */) {
+var n = sampleX.length,
+m = sampleY.length;
+// If either sample doesn't actually have any values, we can't
+// compute this at all, so we return `null`.
+if (!n || !m) { return null; }
+// default difference (mu) is zero
+if (!difference) {
+difference = 0;
+}
+var meanX = mean(sampleX),
+meanY = mean(sampleY),
+sampleVarianceX = sampleVariance(sampleX),
+sampleVarianceY = sampleVariance(sampleY);
+if (typeof meanX === 'number' &&
+typeof meanY === 'number' &&
+typeof sampleVarianceX === 'number' &&
+typeof sampleVarianceY === 'number') {
+var weightedVariance = ((n - 1) * sampleVarianceX +
+(m - 1) * sampleVarianceY) / (n + m - 2);
+return (meanX - meanY - difference) /
+Math.sqrt(weightedVariance * (1 / n + 1 / m));
+}
+}
+module.exports = tTestTwoSample;
+},{"25":25,"50":50}],61:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* For a sorted input, counting the number of unique values
+* is possible in constant time and constant memory. This is
+* a simple implementation of the algorithm.
+*
+* Values are compared with `===`, so objects and non-primitive objects
+* are not handled in any special way.
+*
+* @param {Array} input an array of primitive values.
+* @returns {number} count of unique values
+* @example
+* uniqueCountSorted([1, 2, 3]); // => 3
+* uniqueCountSorted([1, 1, 1]); // => 1
+*/
+function uniqueCountSorted(input/*: Array<any>*/)/*: number */ {
+var uniqueValueCount = 0,
+lastSeenValue;
+for (var i = 0; i < input.length; i++) {
+if (i === 0 || input[i] !== lastSeenValue) {
+lastSeenValue = input[i];
+uniqueValueCount++;
+}
+}
+return uniqueValueCount;
+}
+module.exports = uniqueCountSorted;
+},{}],62:[function(require,module,exports){
+'use strict';
+/* @flow */
+var sumNthPowerDeviations = require(57);
+/**
+* The [variance](http://en.wikipedia.org/wiki/Variance)
+* is the sum of squared deviations from the mean.
+*
+* This is an implementation of variance, not sample variance:
+* see the `sampleVariance` method if you want a sample measure.
+*
+* @param {Array<number>} x a population
+* @returns {number} variance: a value greater than or equal to zero.
+* zero indicates that all values are identical.
+* @example
+* variance([1, 2, 3, 4, 5, 6]); // => 2.9166666666666665
+*/
+function variance(x/*: Array<number> */)/*:number*/ {
+// The variance of no numbers is null
+if (x.length === 0) { return NaN; }
+// Find the mean of squared deviations between the
+// mean value and each value.
+return sumNthPowerDeviations(x, 2) / x.length;
+}
+module.exports = variance;
+},{"57":57}],63:[function(require,module,exports){
+'use strict';
+/* @flow */
+/**
+* The [Z-Score, or Standard Score](http://en.wikipedia.org/wiki/Standard_score).
+*
+* The standard score is the number of standard deviations an observation
+* or datum is above or below the mean. Thus, a positive standard score
+* represents a datum above the mean, while a negative standard score
+* represents a datum below the mean. It is a dimensionless quantity
+* obtained by subtracting the population mean from an individual raw
+* score and then dividing the difference by the population standard
+* deviation.
+*
+* The z-score is only defined if one knows the population parameters;
+* if one only has a sample set, then the analogous computation with
+* sample mean and sample standard deviation yields the
+* Student's t-statistic.
+*
+* @param {number} x
+* @param {number} mean
+* @param {number} standardDeviation
+* @return {number} z score
+* @example
+* zScore(78, 80, 5); // => -0.4
+*/
+function zScore(x/*:number*/, mean/*:number*/, standardDeviation/*:number*/)/*:number*/ {
+return (x - mean) / standardDeviation;
+}
+module.exports = zScore;
+},{}]},{},[1])(1)
+});
+//# sourceMappingURL=simple-statistics.js.map