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Distributions and Sampling\n=====================================\n\nInstallation\n-------------------------------------\n    npm install essy-distribution\n\nDescription\n-------------------------------------\nDefines multiple distributions with methods for random sampling and calculating\ndistribution properties. Sampling functions are largely ported from CERN's\n[cern.jet.random](https://dst.lbl.gov/ACSSoftware/colt/api/cern/jet/random/package-summary.html) Java package.\nSee the source code for details.\n\nThis package was created during the development of [Essy Tree](https://essytree.com) to\nsupport Monte Carlo simulations.\n\nBasic Usage (node.js)\n-------------------------------------\n\n    var dists  = require('essy-distribution');\n\n    var normal = new dists.Normal(0, 1);\n    var mean   = normal.mean();     // 0\n    var sample = normal.sample();   // eg, 0.2314311234\n\nBasic Usage (ES2015)\n-------------------------------------\n\n    import { Normal } from 'essy-distribution';\n\n    var normal = new Normal(0, 1);\n    var mean   = normal.mean();     // 0\n    var sample = normal.sample();   // eg, 0.2314311234\n\nOr you can load the entire package:\n\n    import * as dists from 'essy-distribution';\n\n    var normal = new dists.Normal(0, 1);\n    var mean   = normal.mean();     // 0\n    var sample = normal.sample();   // eg, 0.2314311234\n\nEach distribution defines the following methods:\n\n#### cdf(x {Number})\nCumulative distribution function.\n\n#### mean()\nReturns distribution mean.\n\n#### median()\nReturns distribution median.\n\n#### pdf(x {Number})\nProbability density function.\n\n#### sample([n {Number}] [,generator {Object}])\nSamples the distribution. If no arguments are provided or `n = 1` a single\nsampled value is returned. If `n` is greater than 1, an array of `n` sampled\nvalues is returned.\n\nThe method accepts an optional `generator` object that defines a method `random()`.\nIf no generator is provided a [mersenne-twister](https://www.npmjs.com/package/mersenne-twister) is used.\n\n#### variance()\nReturns variance.\n\nDistributions\n-------------------------------------\n\n#### Beta(alpha, beta)\nSee [documentation](https://essytree.com/docs#dist-beta).\n\n#### Binomial(samples, probability)\nSee [documentation](https://essytree.com/docs#dist-binomial).\n\n#### Cauchy(location, scale)\nSee [article](https://en.wikipedia.org/wiki/Cauchy_distribution).\n\n#### ChiSquared(degreesOfFreedom)\nSee [article](https://en.wikipedia.org/wiki/Chi-squared_distribution).\n\n#### Custom(values)\nA custom distribution. The `values` argument should be an array of numbers.\n\n#### Erlang(shape, rate)\nSee [documentation](https://essytree.com/docs#dist-erlang).\n\n#### Exponential(lambda)\nSee [documentation](https://essytree.com/docs#dist-exponential).\n\n#### F(degreesOfFreedom1, degreesOfFreedom2)\nSee [article](https://en.wikipedia.org/wiki/F-distribution).\n\n#### Gamma(shape, scale)\nSee [documentation](https://essytree.com/docs#dist-gamma).\n\n#### Hypergeometric(N, K, n)\nSee [article](https://en.wikipedia.org/wiki/Hypergeometric_distribution).\n\n#### Laplace(location, scale)\nSee [documentation](https://essytree.com/docs#dist-laplace).\n\n#### Levy(location, scale)\nSee [article](https://en.wikipedia.org/wiki/L%C3%A9vy_distribution).\n\n#### Logarithmic(probability)\nSee [documentation](https://essytree.com/docs#dist-logarithmic).\n\n#### Logistic(mean, scale)\nSee [documentation](https://essytree.com/docs#dist-logistic).\n\n#### LogLogistic(scale, shape)\nSee [documentation](https://essytree.com/docs#dist-loglogistic).\n\n#### LogNormal(mean, se)\nSee [documentation](https://essytree.com/docs#dist-lognormal).\n\n#### Normal(mean, se)\nSee [documentation](https://essytree.com/docs#dist-normal).\n\n#### Pareto(scale, shape)\nSee [article](https://en.wikipedia.org/wiki/Pareto_distribution).\n\n#### Poisson(lambda)\nSee [documentation](https://essytree.com/docs#dist-poisson).\n\n#### Rayleigh(scale)\nSee [documentation](https://essytree.com/docs#dist-rayleigh).\n\n#### StudentT(degreesOfFreedom)\nSee [article](https://en.wikipedia.org/wiki/Student%27s_t-distribution).\n\n#### Triangular(min, mode, max)\nSee [documentation](https://essytree.com/docs#dist-triangular).\n\n#### Uniform(min, max)\nSee [documentation](https://essytree.com/docs#dist-uniform).\n\n#### Weibull(shape, scale)\nSee [documentation](https://essytree.com/docs#dist-weibull).\n","maintainers":[{"name":"scherry","email":"greatmailbox2000-zml@yahoo.com"}],"time":{"modified":"2024-01-09T03:22:31.501Z","created":"2017-11-27T18:39:15.490Z","1.0.0":"2017-11-27T18:39:15.490Z","1.0.1":"2017-11-27T18:43:29.680Z","1.0.2":"2017-11-27T18:46:56.825Z","1.0.3":"2017-11-27T19:22:39.846Z","1.0.4":"2017-11-27T19:59:16.248Z","1.0.5":"2017-11-27T20:18:27.820Z","1.0.6":"2017-11-27T22:15:18.153Z","1.0.7":"2017-11-27T22:18:48.419Z","1.0.8":"2017-11-27T22:51:03.763Z","1.0.9":"2017-11-29T01:19:56.778Z","1.0.10":"2017-11-29T16:16:36.041Z","1.0.12":"2017-11-29T18:10:26.365Z","1.0.13":"2017-11-29T19:58:38.651Z","1.0.14":"2017-11-29T22:55:57.341Z","1.0.15":"2017-11-29T23:04:34.741Z","1.0.16":"2017-11-30T02:23:08.940Z","1.0.17":"2017-12-17T01:40:42.224Z","1.0.18":"2017-12-17T04:11:27.892Z","1.0.19":"2017-12-17T18:12:29.719Z","1.0.20":"2017-12-17T18:38:09.254Z","1.0.21":"2017-12-18T20:57:34.997Z","1.0.22":"2017-12-19T20:57:30.537Z","1.0.23":"2017-12-20T05:56:36.365Z","1.0.24":"2017-12-20T18:40:34.878Z","1.0.25":"2017-12-21T17:38:58.990Z","1.0.26":"2017-12-21T17:43:21.835Z","1.0.27":"2017-12-21T17:48:17.400Z","1.0.271":"2017-12-21T17:53:53.403Z","1.0.272":"2017-12-21T21:22:01.825Z","1.0.273":"2017-12-21T22:04:35.916Z","1.0.274":"2017-12-21T22:37:01.131Z","1.0.275":"2017-12-22T03:15:49.195Z","1.0.276":"2017-12-23T18:20:05.287Z","1.0.277":"2017-12-24T23:13:04.476Z","1.0.278":"2017-12-25T00:55:04.118Z","1.0.279":"2017-12-27T01:36:23.724Z","1.0.28":"2017-12-28T03:48:13.134Z","1.1.0":"2017-12-28T03:54:52.064Z","1.1.1":"2018-01-08T19:56:05.825Z","1.1.2":"2018-01-09T03:33:02.442Z","1.1.3":"2018-01-09T03:37:13.953Z","1.1.4":"2018-01-09T03:39:12.620Z","1.1.5":"2018-01-09T15:39:55.942Z","1.1.6":"2018-01-09T18:52:59.751Z","1.1.7":"2018-01-09T23:46:26.896Z","1.1.8":"2018-01-09T23:50:37.235Z","1.1.9":"2018-01-10T17:07:35.146Z","1.1.10":"2018-01-10T17:19:42.752Z","1.1.11":"2018-01-10T17:41:42.078Z","1.1.12":"2018-01-10T17:47:09.708Z","1.1.13":"2018-01-10T18:00:43.810Z","1.1.14":"2018-01-11T02:36:23.653Z","1.1.15":"2018-01-11T03:28:05.580Z","1.1.16":"2018-01-11T03:56:40.448Z","1.1.17":"2018-01-11T04:04:03.355Z","1.1.18":"2018-01-14T16:14:10.167Z","1.1.19":"2018-01-17T04:06:47.955Z","1.1.20":"2018-01-17T04:23:38.482Z","1.1.21":"2018-01-17T11:53:44.641Z","1.1.22":"2018-01-17T12:22:41.698Z","1.1.23":"2023-09-18T02:24:50.270Z","1.1.24":"2023-09-18T02:41:55.633Z","1.1.25":"2023-09-18T03:08:58.056Z","1.1.30":"2023-09-18T03:18:52.696Z","1.1.31":"2023-09-18T03:54:54.023Z","1.1.32":"2024-01-09T02:52:00.405Z","1.1.33":"2024-01-09T03:16:30.721Z","1.1.34":"2024-01-09T03:22:31.329Z"},"homepage":"https://github.com/essy2017/essy-distribution.git#readme","keywords":["statistics","distribution","probability","sampling","math"],"repository":{"type":"git","url":"git+https://github.com/essy2017/essy-distribution.git"},"author":{"name":"Spencer Cherry"},"bugs":{"url":"https://github.com/essy2017/essy-distribution/issues"},"license":"MIT","readmeFilename":"README.md"}