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Status](https://coveralls.io/repos/github/errcw/gaussian/badge.svg?branch=master)](https://coveralls.io/github/errcw/gaussian?branch=master)\n[![Downloads](https://img.shields.io/npm/dy/gaussian)](https://www.npmjs.com/package/gaussian)\n\n# gaussian\n\nA JavaScript model of the [Normal](http://en.wikipedia.org/wiki/Normal_distribution)\n(or Gaussian) distribution.\n\n## API\n\n### Creating a Distribution\n\n```javascript\nvar gaussian = require('gaussian');\nvar distribution = gaussian(mean, variance);\n// Take a random sample using inverse transform sampling method.\nvar sample = distribution.ppf(Math.random());\n```\n\n### Properties\n\n- `mean`: the mean (μ) of the distribution\n- `variance`: the variance (σ^2) of the distribution\n- `standardDeviation`: the standard deviation (σ) of the distribution\n\n### Probability Functions\n\n- `pdf(x)`: the probability density function, which describes the probability\n  of a random variable taking on the value _x_\n- `cdf(x)`: the cumulative distribution function, which describes the\n  probability of a random variable falling in the interval (−∞, _x_]\n- `ppf(x)`: the percent point function, the inverse of _cdf_\n\n### Combination Functions\n\n- `mul(d)`: returns the product distribution of this and the given distribution; equivalent to `scale(d)` when d is a constant\n- `div(d)`: returns the quotient distribution of this and the given distribution; equivalent to `scale(1/d)` when d is a constant\n- `add(d)`: returns the result of adding this and the given distribution's means and variances\n- `sub(d)`: returns the result of subtracting this and the given distribution's means and variances\n- `scale(c)`: returns the result of scaling this distribution by the given constant\n\n### Generation Function\n\n- `random(n)`: returns an array of generated `n` random samples correspoding to the Gaussian parameters.","users":{}}