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vega-statistics - npm Package Compare versions

Comparing version 1.2.1 to 1.2.2

.travis.yml

744

build/vega-statistics.js
(function (global, factory) {
typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports, require('d3-array')) :
typeof define === 'function' && define.amd ? define(['exports', 'd3-array'], factory) :
(factory((global.vega = global.vega || {}),global.d3));
typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports, require('d3-array')) :
typeof define === 'function' && define.amd ? define(['exports', 'd3-array'], factory) :
(factory((global.vega = {}),global.d3));
}(this, (function (exports,d3Array) { 'use strict';
var bin = function(_) {
// determine range
var maxb = _.maxbins || 20,
base = _.base || 10,
logb = Math.log(base),
div = _.divide || [5, 2],
min = _.extent[0],
max = _.extent[1],
span = max - min,
step, level, minstep, precision, v, i, n, eps;
function bin(_) {
// determine range
var maxb = _.maxbins || 20,
base = _.base || 10,
logb = Math.log(base),
div = _.divide || [5, 2],
min = _.extent[0],
max = _.extent[1],
span = max - min,
step, level, minstep, precision, v, i, n, eps;
if (_.step) {
// if step size is explicitly given, use that
step = _.step;
} else if (_.steps) {
// if provided, limit choice to acceptable step sizes
v = span / maxb;
for (i=0, n=_.steps.length; i < n && _.steps[i] < v; ++i);
step = _.steps[Math.max(0, i-1)];
} else {
// else use span to determine step size
level = Math.ceil(Math.log(maxb) / logb);
minstep = _.minstep || 0;
step = Math.max(
minstep,
Math.pow(base, Math.round(Math.log(span) / logb) - level)
);
if (_.step) {
// if step size is explicitly given, use that
step = _.step;
} else if (_.steps) {
// if provided, limit choice to acceptable step sizes
v = span / maxb;
for (i=0, n=_.steps.length; i < n && _.steps[i] < v; ++i);
step = _.steps[Math.max(0, i-1)];
} else {
// else use span to determine step size
level = Math.ceil(Math.log(maxb) / logb);
minstep = _.minstep || 0;
step = Math.max(
minstep,
Math.pow(base, Math.round(Math.log(span) / logb) - level)
);
// increase step size if too many bins
while (Math.ceil(span/step) > maxb) { step *= base; }
// increase step size if too many bins
while (Math.ceil(span/step) > maxb) { step *= base; }
// decrease step size if allowed
for (i=0, n=div.length; i<n; ++i) {
v = step / div[i];
if (v >= minstep && span / v <= maxb) step = v;
// decrease step size if allowed
for (i=0, n=div.length; i<n; ++i) {
v = step / div[i];
if (v >= minstep && span / v <= maxb) step = v;
}
}
}
// update precision, min and max
v = Math.log(step);
precision = v >= 0 ? 0 : ~~(-v / logb) + 1;
eps = Math.pow(base, -precision - 1);
if (_.nice || _.nice === undefined) {
v = Math.floor(min / step + eps) * step;
min = min < v ? v - step : v;
max = Math.ceil(max / step) * step;
// update precision, min and max
v = Math.log(step);
precision = v >= 0 ? 0 : ~~(-v / logb) + 1;
eps = Math.pow(base, -precision - 1);
if (_.nice || _.nice === undefined) {
v = Math.floor(min / step + eps) * step;
min = min < v ? v - step : v;
max = Math.ceil(max / step) * step;
}
return {
start: min,
stop: max,
step: step
};
}
return {
start: min,
stop: max,
step: step
};
};
function numbers(array, f) {
var numbers = [],
n = array.length,
i = -1, a;
var numbers = function(array, f) {
var numbers = [],
n = array.length,
i = -1, a;
if (f == null) {
while (++i < n) if (!isNaN(a = number(array[i]))) numbers.push(a);
} else {
while (++i < n) if (!isNaN(a = number(f(array[i], i, array)))) numbers.push(a);
}
return numbers;
}
if (f == null) {
while (++i < n) if (!isNaN(a = number(array[i]))) numbers.push(a);
} else {
while (++i < n) if (!isNaN(a = number(f(array[i], i, array)))) numbers.push(a);
function number(x) {
return x === null ? NaN : +x;
}
return numbers;
};
function number(x) {
return x === null ? NaN : +x;
}
exports.random = Math.random;
exports.random = Math.random;
function setRandom(r) {
exports.random = r;
}
function setRandom(r) {
exports.random = r;
}
function bootstrapCI(array, samples, alpha, f) {
if (!array.length) return [undefined, undefined];
var bootstrapCI = function(array, samples, alpha, f) {
var values = numbers(array, f),
n = values.length,
m = samples,
a, i, j, mu;
var values = numbers(array, f),
n = values.length,
m = samples,
a, i, j, mu;
for (j=0, mu=Array(m); j<m; ++j) {
for (a=0, i=0; i<n; ++i) {
a += values[~~(exports.random() * n)];
for (j=0, mu=Array(m); j<m; ++j) {
for (a=0, i=0; i<n; ++i) {
a += values[~~(exports.random() * n)];
}
mu[j] = a / n;
}
mu[j] = a / n;
return [
d3Array.quantile(mu.sort(d3Array.ascending), alpha/2),
d3Array.quantile(mu, 1-(alpha/2))
];
}
return [
d3Array.quantile(mu.sort(d3Array.ascending), alpha/2),
d3Array.quantile(mu, 1-(alpha/2))
];
};
function quartiles(array, f) {
var values = numbers(array, f);
var quartiles = function(array, f) {
var values = numbers(array, f);
return [
d3Array.quantile(values.sort(d3Array.ascending), 0.25),
d3Array.quantile(values, 0.50),
d3Array.quantile(values, 0.75)
];
};
var integer = function(min, max) {
if (max == null) {
max = min;
min = 0;
return [
d3Array.quantile(values.sort(d3Array.ascending), 0.25),
d3Array.quantile(values, 0.50),
d3Array.quantile(values, 0.75)
];
}
var dist = {},
a, b, d;
dist.min = function(_) {
if (arguments.length) {
a = _ || 0;
d = b - a;
return dist;
} else {
return a;
function integer(min, max) {
if (max == null) {
max = min;
min = 0;
}
};
dist.max = function(_) {
if (arguments.length) {
b = _ || 0;
d = b - a;
return dist;
} else {
return b;
}
};
var dist = {},
a, b, d;
dist.sample = function() {
return a + Math.floor(d * exports.random());
};
dist.min = function(_) {
if (arguments.length) {
a = _ || 0;
d = b - a;
return dist;
} else {
return a;
}
};
dist.pdf = function(x) {
return (x === Math.floor(x) && x >= a && x < b) ? 1 / d : 0;
};
dist.max = function(_) {
if (arguments.length) {
b = _ || 0;
d = b - a;
return dist;
} else {
return b;
}
};
dist.cdf = function(x) {
var v = Math.floor(x);
return v < a ? 0 : v >= b ? 1 : (v - a + 1) / d;
};
dist.sample = function() {
return a + Math.floor(d * exports.random());
};
dist.icdf = function(p) {
return (p >= 0 && p <= 1) ? a - 1 + Math.floor(p * d) : NaN;
};
dist.pdf = function(x) {
return (x === Math.floor(x) && x >= a && x < b) ? 1 / d : 0;
};
return dist.min(min).max(max);
};
dist.cdf = function(x) {
var v = Math.floor(x);
return v < a ? 0 : v >= b ? 1 : (v - a + 1) / d;
};
var gaussian = function(mean, stdev) {
var mu,
sigma,
next = NaN,
dist = {};
dist.icdf = function(p) {
return (p >= 0 && p <= 1) ? a - 1 + Math.floor(p * d) : NaN;
};
dist.mean = function(_) {
if (arguments.length) {
mu = _ || 0;
next = NaN;
return dist;
} else {
return mu;
}
};
return dist.min(min).max(max);
}
dist.stdev = function(_) {
if (arguments.length) {
sigma = _ == null ? 1 : _;
next = NaN;
return dist;
} else {
return sigma;
}
};
function gaussian(mean, stdev) {
var mu,
sigma,
next = NaN,
dist = {};
dist.sample = function() {
var x = 0, y = 0, rds, c;
if (next === next) {
x = next;
next = NaN;
return x;
}
do {
x = exports.random() * 2 - 1;
y = exports.random() * 2 - 1;
rds = x * x + y * y;
} while (rds === 0 || rds > 1);
c = Math.sqrt(-2 * Math.log(rds) / rds); // Box-Muller transform
next = mu + y * c * sigma;
return mu + x * c * sigma;
};
dist.mean = function(_) {
if (arguments.length) {
mu = _ || 0;
next = NaN;
return dist;
} else {
return mu;
}
};
dist.pdf = function(x) {
var exp = Math.exp(Math.pow(x-mu, 2) / (-2 * Math.pow(sigma, 2)));
return (1 / (sigma * Math.sqrt(2*Math.PI))) * exp;
};
// Approximation from West (2009)
// Better Approximations to Cumulative Normal Functions
dist.cdf = function(x) {
var cd,
z = (x - mu) / sigma,
Z = Math.abs(z);
if (Z > 37) {
cd = 0;
} else {
var sum, exp = Math.exp(-Z*Z/2);
if (Z < 7.07106781186547) {
sum = 3.52624965998911e-02 * Z + 0.700383064443688;
sum = sum * Z + 6.37396220353165;
sum = sum * Z + 33.912866078383;
sum = sum * Z + 112.079291497871;
sum = sum * Z + 221.213596169931;
sum = sum * Z + 220.206867912376;
cd = exp * sum;
sum = 8.83883476483184e-02 * Z + 1.75566716318264;
sum = sum * Z + 16.064177579207;
sum = sum * Z + 86.7807322029461;
sum = sum * Z + 296.564248779674;
sum = sum * Z + 637.333633378831;
sum = sum * Z + 793.826512519948;
sum = sum * Z + 440.413735824752;
cd = cd / sum;
dist.stdev = function(_) {
if (arguments.length) {
sigma = _ == null ? 1 : _;
next = NaN;
return dist;
} else {
sum = Z + 0.65;
sum = Z + 4 / sum;
sum = Z + 3 / sum;
sum = Z + 2 / sum;
sum = Z + 1 / sum;
cd = exp / sum / 2.506628274631;
return sigma;
}
}
return z > 0 ? 1 - cd : cd;
};
};
// Approximation of Probit function using inverse error function.
dist.icdf = function(p) {
if (p <= 0 || p >= 1) return NaN;
var x = 2*p - 1,
v = (8 * (Math.PI - 3)) / (3 * Math.PI * (4-Math.PI)),
a = (2 / (Math.PI*v)) + (Math.log(1 - Math.pow(x,2)) / 2),
b = Math.log(1 - (x*x)) / v,
s = (x > 0 ? 1 : -1) * Math.sqrt(Math.sqrt((a*a) - b) - a);
return mu + sigma * Math.SQRT2 * s;
};
dist.sample = function() {
var x = 0, y = 0, rds, c;
if (next === next) {
x = next;
next = NaN;
return x;
}
do {
x = exports.random() * 2 - 1;
y = exports.random() * 2 - 1;
rds = x * x + y * y;
} while (rds === 0 || rds > 1);
c = Math.sqrt(-2 * Math.log(rds) / rds); // Box-Muller transform
next = mu + y * c * sigma;
return mu + x * c * sigma;
};
return dist.mean(mean).stdev(stdev);
};
dist.pdf = function(x) {
var exp = Math.exp(Math.pow(x-mu, 2) / (-2 * Math.pow(sigma, 2)));
return (1 / (sigma * Math.sqrt(2*Math.PI))) * exp;
};
// TODO: support for additional kernels?
var kde = function(support, bandwidth) {
var kernel = gaussian(),
dist = {},
n = 0;
// Approximation from West (2009)
// Better Approximations to Cumulative Normal Functions
dist.cdf = function(x) {
var cd,
z = (x - mu) / sigma,
Z = Math.abs(z);
if (Z > 37) {
cd = 0;
} else {
var sum, exp = Math.exp(-Z*Z/2);
if (Z < 7.07106781186547) {
sum = 3.52624965998911e-02 * Z + 0.700383064443688;
sum = sum * Z + 6.37396220353165;
sum = sum * Z + 33.912866078383;
sum = sum * Z + 112.079291497871;
sum = sum * Z + 221.213596169931;
sum = sum * Z + 220.206867912376;
cd = exp * sum;
sum = 8.83883476483184e-02 * Z + 1.75566716318264;
sum = sum * Z + 16.064177579207;
sum = sum * Z + 86.7807322029461;
sum = sum * Z + 296.564248779674;
sum = sum * Z + 637.333633378831;
sum = sum * Z + 793.826512519948;
sum = sum * Z + 440.413735824752;
cd = cd / sum;
} else {
sum = Z + 0.65;
sum = Z + 4 / sum;
sum = Z + 3 / sum;
sum = Z + 2 / sum;
sum = Z + 1 / sum;
cd = exp / sum / 2.506628274631;
}
}
return z > 0 ? 1 - cd : cd;
};
dist.data = function(_) {
if (arguments.length) {
support = _;
n = _ ? _.length : 0;
return dist.bandwidth(bandwidth);
} else {
return support;
}
};
// Approximation of Probit function using inverse error function.
dist.icdf = function(p) {
if (p <= 0 || p >= 1) return NaN;
var x = 2*p - 1,
v = (8 * (Math.PI - 3)) / (3 * Math.PI * (4-Math.PI)),
a = (2 / (Math.PI*v)) + (Math.log(1 - Math.pow(x,2)) / 2),
b = Math.log(1 - (x*x)) / v,
s = (x > 0 ? 1 : -1) * Math.sqrt(Math.sqrt((a*a) - b) - a);
return mu + sigma * Math.SQRT2 * s;
};
dist.bandwidth = function(_) {
if (!arguments.length) return bandwidth;
bandwidth = _;
if (!bandwidth && support) bandwidth = estimateBandwidth(support);
return dist;
};
return dist.mean(mean).stdev(stdev);
}
dist.sample = function() {
return support[~~(exports.random() * n)] + bandwidth * kernel.sample();
};
// TODO: support for additional kernels?
function kde(support, bandwidth) {
var kernel = gaussian(),
dist = {},
n = 0;
dist.pdf = function(x) {
for (var y=0, i=0; i<n; ++i) {
y += kernel.pdf((x - support[i]) / bandwidth);
}
return y / bandwidth / n;
};
dist.data = function(_) {
if (arguments.length) {
support = _;
n = _ ? _.length : 0;
return dist.bandwidth(bandwidth);
} else {
return support;
}
};
dist.cdf = function(x) {
for (var y=0, i=0; i<n; ++i) {
y += kernel.cdf((x - support[i]) / bandwidth);
}
return y / n;
};
dist.bandwidth = function(_) {
if (!arguments.length) return bandwidth;
bandwidth = _;
if (!bandwidth && support) bandwidth = estimateBandwidth(support);
return dist;
};
dist.icdf = function() {
throw Error('KDE icdf not supported.');
};
dist.sample = function() {
return support[~~(exports.random() * n)] + bandwidth * kernel.sample();
};
return dist.data(support);
};
dist.pdf = function(x) {
for (var y=0, i=0; i<n; ++i) {
y += kernel.pdf((x - support[i]) / bandwidth);
}
return y / bandwidth / n;
};
// Scott, D. W. (1992) Multivariate Density Estimation:
// Theory, Practice, and Visualization. Wiley.
function estimateBandwidth(array) {
var n = array.length,
q = quartiles(array),
h = (q[2] - q[0]) / 1.34;
return 1.06 * Math.min(Math.sqrt(d3Array.variance(array)), h) * Math.pow(n, -0.2);
}
dist.cdf = function(x) {
for (var y=0, i=0; i<n; ++i) {
y += kernel.cdf((x - support[i]) / bandwidth);
}
return y / n;
};
var mixture = function(dists, weights) {
var dist = {}, m = 0, w;
dist.icdf = function() {
throw Error('KDE icdf not supported.');
};
function normalize(x) {
var w = [], sum = 0, i;
for (i=0; i<m; ++i) { sum += (w[i] = (x[i]==null ? 1 : +x[i])); }
for (i=0; i<m; ++i) { w[i] /= sum; }
return w;
return dist.data(support);
}
dist.weights = function(_) {
if (arguments.length) {
w = normalize(weights = (_ || []));
return dist;
// Scott, D. W. (1992) Multivariate Density Estimation:
// Theory, Practice, and Visualization. Wiley.
function estimateBandwidth(array) {
var n = array.length,
q = quartiles(array),
h = (q[2] - q[0]) / 1.34;
return 1.06 * Math.min(Math.sqrt(d3Array.variance(array)), h) * Math.pow(n, -0.2);
}
function mixture(dists, weights) {
var dist = {}, m = 0, w;
function normalize(x) {
var w = [], sum = 0, i;
for (i=0; i<m; ++i) { sum += (w[i] = (x[i]==null ? 1 : +x[i])); }
for (i=0; i<m; ++i) { w[i] /= sum; }
return w;
}
return weights;
};
dist.distributions = function(_) {
if (arguments.length) {
if (_) {
m = _.length;
dists = _;
} else {
m = 0;
dists = [];
dist.weights = function(_) {
if (arguments.length) {
w = normalize(weights = (_ || []));
return dist;
}
return dist.weights(weights);
}
return dists;
};
return weights;
};
dist.sample = function() {
var r = exports.random(),
d = dists[m-1],
v = w[0],
i = 0;
dist.distributions = function(_) {
if (arguments.length) {
if (_) {
m = _.length;
dists = _;
} else {
m = 0;
dists = [];
}
return dist.weights(weights);
}
return dists;
};
// first select distribution
for (; i<m-1; v += w[++i]) {
if (r < v) { d = dists[i]; break; }
}
// then sample from it
return d.sample();
};
dist.sample = function() {
var r = exports.random(),
d = dists[m-1],
v = w[0],
i = 0;
dist.pdf = function(x) {
for (var p=0, i=0; i<m; ++i) {
p += w[i] * dists[i].pdf(x);
}
return p;
};
// first select distribution
for (; i<m-1; v += w[++i]) {
if (r < v) { d = dists[i]; break; }
}
// then sample from it
return d.sample();
};
dist.cdf = function(x) {
for (var p=0, i=0; i<m; ++i) {
p += w[i] * dists[i].cdf(x);
}
return p;
};
dist.pdf = function(x) {
for (var p=0, i=0; i<m; ++i) {
p += w[i] * dists[i].pdf(x);
}
return p;
};
dist.icdf = function() {
throw Error('Mixture icdf not supported.');
};
dist.cdf = function(x) {
for (var p=0, i=0; i<m; ++i) {
p += w[i] * dists[i].cdf(x);
}
return p;
};
return dist.distributions(dists).weights(weights);
};
dist.icdf = function() {
throw Error('Mixture icdf not supported.');
};
var uniform = function(min, max) {
if (max == null) {
max = (min == null ? 1 : min);
min = 0;
return dist.distributions(dists).weights(weights);
}
var dist = {},
a, b, d;
dist.min = function(_) {
if (arguments.length) {
a = _ || 0;
d = b - a;
return dist;
} else {
return a;
function uniform(min, max) {
if (max == null) {
max = (min == null ? 1 : min);
min = 0;
}
};
dist.max = function(_) {
if (arguments.length) {
b = _ || 0;
d = b - a;
return dist;
} else {
return b;
}
};
var dist = {},
a, b, d;
dist.sample = function() {
return a + d * exports.random();
};
dist.min = function(_) {
if (arguments.length) {
a = _ || 0;
d = b - a;
return dist;
} else {
return a;
}
};
dist.pdf = function(x) {
return (x >= a && x <= b) ? 1 / d : 0;
};
dist.max = function(_) {
if (arguments.length) {
b = _ || 0;
d = b - a;
return dist;
} else {
return b;
}
};
dist.cdf = function(x) {
return x < a ? 0 : x > b ? 1 : (x - a) / d;
};
dist.sample = function() {
return a + d * exports.random();
};
dist.icdf = function(p) {
return (p >= 0 && p <= 1) ? a + p * d : NaN;
};
dist.pdf = function(x) {
return (x >= a && x <= b) ? 1 / d : 0;
};
return dist.min(min).max(max);
};
dist.cdf = function(x) {
return x < a ? 0 : x > b ? 1 : (x - a) / d;
};
exports.bin = bin;
exports.bootstrapCI = bootstrapCI;
exports.quartiles = quartiles;
exports.setRandom = setRandom;
exports.randomInteger = integer;
exports.randomKDE = kde;
exports.randomMixture = mixture;
exports.randomNormal = gaussian;
exports.randomUniform = uniform;
dist.icdf = function(p) {
return (p >= 0 && p <= 1) ? a + p * d : NaN;
};
Object.defineProperty(exports, '__esModule', { value: true });
return dist.min(min).max(max);
}
exports.bin = bin;
exports.bootstrapCI = bootstrapCI;
exports.quartiles = quartiles;
exports.setRandom = setRandom;
exports.randomInteger = integer;
exports.randomKDE = kde;
exports.randomMixture = mixture;
exports.randomNormal = gaussian;
exports.randomUniform = uniform;
Object.defineProperty(exports, '__esModule', { value: true });
})));

2

build/vega-statistics.min.js

@@ -1,1 +0,1 @@

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!function(n,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports,require("d3-array")):"function"==typeof define&&define.amd?define(["exports","d3-array"],t):t(n.vega={},n.d3)}(this,function(h,l){"use strict";function s(n,t){var r,e=[],a=n.length,o=-1;if(null==t)for(;++o<a;)isNaN(r=u(n[o]))||e.push(r);else for(;++o<a;)isNaN(r=u(t(n[o],o,n)))||e.push(r);return e}function u(n){return null===n?NaN:+n}function f(n,t){var r=s(n,t);return[l.quantile(r.sort(l.ascending),.25),l.quantile(r,.5),l.quantile(r,.75)]}function n(n,t){var u,i,a=NaN,r={mean:function(n){return arguments.length?(u=n||0,a=NaN,r):u},stdev:function(n){return arguments.length?(i=null==n?1:n,a=NaN,r):i},sample:function(){var n,t,r=0,e=0;if(a==a)return r=a,a=NaN,r;for(;0===(n=(r=2*h.random()-1)*r+(e=2*h.random()-1)*e)||1<n;);return t=Math.sqrt(-2*Math.log(n)/n),a=u+e*t*i,u+r*t*i},pdf:function(n){var t=Math.exp(Math.pow(n-u,2)/(-2*Math.pow(i,2)));return 1/(i*Math.sqrt(2*Math.PI))*t},cdf:function(n){var t,r=(n-u)/i,e=Math.abs(r);if(37<e)t=0;else{var a=Math.exp(-e*e/2);e<7.07106781186547?(t=a*((((((.0352624965998911*e+.700383064443688)*e+6.37396220353165)*e+33.912866078383)*e+112.079291497871)*e+221.213596169931)*e+220.206867912376),t/=((((((.0883883476483184*e+1.75566716318264)*e+16.064177579207)*e+86.7807322029461)*e+296.564248779674)*e+637.333633378831)*e+793.826512519948)*e+440.413735824752):t=a/(e+1/(e+2/(e+3/(e+4/(e+.65)))))/2.506628274631}return 0<r?1-t:t},icdf:function(n){if(n<=0||1<=n)return NaN;var t=2*n-1,r=8*(Math.PI-3)/(3*Math.PI*(4-Math.PI)),e=2/(Math.PI*r)+Math.log(1-Math.pow(t,2))/2,a=Math.log(1-t*t)/r,o=(0<t?1:-1)*Math.sqrt(Math.sqrt(e*e-a)-e);return u+i*Math.SQRT2*o}};return r.mean(n).stdev(t)}h.random=Math.random,h.bin=function(n){var t,r,e,a,o,u,i,f,c=n.maxbins||20,d=n.base||10,h=Math.log(d),l=n.divide||[5,2],s=n.extent[0],p=n.extent[1],M=p-s;if(n.step)t=n.step;else if(n.steps){for(o=M/c,u=0,i=n.steps.length;u<i&&n.steps[u]<o;++u);t=n.steps[Math.max(0,u-1)]}else{for(r=Math.ceil(Math.log(c)/h),e=n.minstep||0,t=Math.max(e,Math.pow(d,Math.round(Math.log(M)/h)-r));Math.ceil(M/t)>c;)t*=d;for(u=0,i=l.length;u<i;++u)e<=(o=t/l[u])&&M/o<=c&&(t=o)}return a=0<=(o=Math.log(t))?0:1+~~(-o/h),f=Math.pow(d,-a-1),(n.nice||void 0===n.nice)&&(s=s<(o=Math.floor(s/t+f)*t)?o-t:o,p=Math.ceil(p/t)*t),{start:s,stop:p,step:t}},h.bootstrapCI=function(n,t,r,e){if(!n.length)return[void 0,void 0];var a,o,u,i,f=s(n,e),c=f.length,d=t;for(u=0,i=Array(d);u<d;++u){for(o=a=0;o<c;++o)a+=f[~~(h.random()*c)];i[u]=a/c}return[l.quantile(i.sort(l.ascending),r/2),l.quantile(i,1-r/2)]},h.quartiles=f,h.setRandom=function(n){h.random=n},h.randomInteger=function(n,t){null==t&&(t=n,n=0);var r,e,a,o={};return o.min=function(n){return arguments.length?(a=e-(r=n||0),o):r},o.max=function(n){return arguments.length?(a=(e=n||0)-r,o):e},o.sample=function(){return r+Math.floor(a*h.random())},o.pdf=function(n){return n===Math.floor(n)&&r<=n&&n<e?1/a:0},o.cdf=function(n){var t=Math.floor(n);return t<r?0:e<=t?1:(t-r+1)/a},o.icdf=function(n){return 0<=n&&n<=1?r-1+Math.floor(n*a):NaN},o.min(n).max(t)},h.randomKDE=function(o,u){var e=n(),i={},a=0;return i.data=function(n){return arguments.length?(a=(o=n)?n.length:0,i.bandwidth(u)):o},i.bandwidth=function(n){return arguments.length?(!(u=n)&&o&&(r=(t=o).length,e=f(t),a=(e[2]-e[0])/1.34,u=1.06*Math.min(Math.sqrt(l.variance(t)),a)*Math.pow(r,-.2)),i):u;var t,r,e,a},i.sample=function(){return o[~~(h.random()*a)]+u*e.sample()},i.pdf=function(n){for(var t=0,r=0;r<a;++r)t+=e.pdf((n-o[r])/u);return t/u/a},i.cdf=function(n){for(var t=0,r=0;r<a;++r)t+=e.cdf((n-o[r])/u);return t/a},i.icdf=function(){throw Error("KDE icdf not supported.")},i.data(o)},h.randomMixture=function(a,t){var o,r={},u=0;return r.weights=function(n){return arguments.length?(o=function(n){var t,r=[],e=0;for(t=0;t<u;++t)e+=r[t]=null==n[t]?1:+n[t];for(t=0;t<u;++t)r[t]/=e;return r}(t=n||[]),r):t},r.distributions=function(n){return arguments.length?(a=n?(u=n.length,n):(u=0,[]),r.weights(t)):a},r.sample=function(){for(var n=h.random(),t=a[u-1],r=o[0],e=0;e<u-1;r+=o[++e])if(n<r){t=a[e];break}return t.sample()},r.pdf=function(n){for(var t=0,r=0;r<u;++r)t+=o[r]*a[r].pdf(n);return t},r.cdf=function(n){for(var t=0,r=0;r<u;++r)t+=o[r]*a[r].cdf(n);return t},r.icdf=function(){throw Error("Mixture icdf not supported.")},r.distributions(a).weights(t)},h.randomNormal=n,h.randomUniform=function(n,t){null==t&&(t=null==n?1:n,n=0);var r,e,a,o={};return o.min=function(n){return arguments.length?(a=e-(r=n||0),o):r},o.max=function(n){return arguments.length?(a=(e=n||0)-r,o):e},o.sample=function(){return r+a*h.random()},o.pdf=function(n){return r<=n&&n<=e?1/a:0},o.cdf=function(n){return n<r?0:e<n?1:(n-r)/a},o.icdf=function(n){return 0<=n&&n<=1?r+n*a:NaN},o.min(n).max(t)},Object.defineProperty(h,"__esModule",{value:!0})});

10

package.json
{
"name": "vega-statistics",
"version": "1.2.1",
"version": "1.2.2",
"description": "Statistical routines and probability distributions.",

@@ -27,11 +27,11 @@ "keywords": [

"test": "tape 'test/**/*-test.js' && eslint index.js src test",
"prepublish": "npm run build",
"prepublishOnly": "npm run build",
"postpublish": "git push && git push --tags && zip -j build/vega-statistics.zip -- LICENSE README.md build/vega-statistics.js build/vega-statistics.min.js"
},
"dependencies": {
"d3-array": "1"
"d3-array": "^1.2.4"
},
"devDependencies": {
"eslint": "4",
"rollup": "0.43",
"eslint": "5",
"rollup": "0.66.6",
"tape": "4",

@@ -38,0 +38,0 @@ "uglify-js": "3"

2

src/bootstrapCI.js

@@ -6,2 +6,4 @@ import numbers from './numbers';

export default function(array, samples, alpha, f) {
if (!array.length) return [undefined, undefined];
var values = numbers(array, f),

@@ -8,0 +10,0 @@ n = values.length,

.npmignore
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