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\n\nThis project provide a binding between the GNU Scientific Library (GSL) and NodeJS.\n\nAt the moment, the library is partially integrated.\n\nInstalling\n----------\n\nVia [npm](http://github.com/isaacs/npm):\n\n $ npm install gsl\n\nVia git (or downloaded tarball):\n\n $ git clone http://github.com/wdavidw/node-gsl.git\n $ node-waf configure && node-waf\n\nRandom API\n----------\n\nThe library takes two forms, functions and iterator objects. Both respect the same names with different conventions. For exemple, obtaining an uniform random name can be done as `random.get()` as well as `(new Random).get()`. If you wished to provide a seed, then you'll respectivelly call `random.get(seed)` and `(new Random(seed)).get()`\n\nUsing an iterator objects make sense when using seeds and when performance is a concern.\n\nSeeds are always optional and must be provided as unsigned integers. Deviations, used by gaussian functions, are float.\n\n-\t`gsl.Random([seed])` \n\tConstruct a new iterator, seel below for available random methods.\n\t\n-\t`gsl.random.get([seed])` \n\t`gsl.Random.get()` \n\tReturns a random integer. The minimum and maximum values depend on the algorithm used, but all integers in the range [min,max] are equally likely. The values of min and max can determined using the auxiliary functions `random.min()` and `random.max()`.\n\t\n-\t`gsl.random.min()` \n\t`gsl.Random.min()` \n\tReturns the smallest value that `random.get()` can return.\n\t\n-\t`gsl.random.max()` \n\t`gsl.Random.max()` \n\tReturns the largest value that `random.get()` can return.\n\t\n-\t`gsl.random.uniform([seed])` \n\t`gsl.Random.uniform()` \n\tReturns a double precision floating point number uniformly distributed in the range [0,1). The range includes 0.0 but excludes 1.0.\n\t\n-\t`gsl.random.gaussian([seed], deviation)` \n\t`gsl.Random.gaussian(deviation)` \n\tReturns a Gaussian random float with mean zero given a standart deviation as a float.\n\t\n-\t`gsl.random.gaussianZiggurat([seed], deviation)` \n\t`gsl.Random.gaussianZiggurat(deviation)` \n\tSame as `random.gaussian` but using the alternative Marsaglia-Tsang ziggurat method.\n\t\n-\t`gsl.random.gaussianRatioMethod([seed], deviation)` \n\t`gsl.Random.gaussianRatioMethod(deviation)` \n\tSame as `random.gaussian` but using the alternative Kinderman-Monahan-Leva ratio method.\n\t\n-\t`gsl.random.poisson([seed], mean)` \n\t`gsl.Random.poisson(mean)` \n\tReturns a random integer from the Poisson distribution given a provided mean as a float.\n\n### Exemple\n\n```javascript\nvar gsl = require('gsl'),\n\tseed = 50,\n\tdeviation = 0.5;\n\nconsole.log( gsl.random.gaussian(deviation) );\nconsole.log( gsl.random.gaussian(seed, deviation) );\n\nvar iterator = new gsl.Random(seed);\nconsole.log( iterator.gaussian(deviation) );\nconsole.log( iterator.gaussian(deviation) );\n```\n\n### Resources\n\n*\t[Sampling](http://www.gnu.org/software/gsl/manual/html_node/Sampling-from-a-random-number-generator.html)\n*\t[The Gaussian Distribution](http://www.gnu.org/software/gsl/manual/html_node/The-Gaussian-Distribution.html)\n*\t[The Poisson Distribution](http://www.gnu.org/software/gsl/manual/html_node/The-Poisson-Distribution.html)\n\nStatistics API\n--------------\n\nData are expected to be arrays of float numbers. Means are float numbers.\n\n-\t`gsl.statistics.mean(data)` \n\tReturns the arithmetic mean of data.\n\t\n-\t`gsl.statistics.variance(data, [mean])` \n\tReturns the estimated, or sample, variance of data.\n\t\n-\t`gsl.statistics.sd(data, [mean])` \n\tReturns the standard deviation defined as the square root of the variance defined above.\n\t\n-\t`gsl.statistics.tss(data, [mean])` \n\tReturn the total sum of squares (TSS) of data about the mean. If mean is not provided, it is computed the same way as above.\n\t\n-\t`gsl.statistics.varianceWithFixedMean(data, mean)` \n\tComputes an unbiased estimate of the variance of data when the population mean mean of the underlying distribution is known a priori.\n\t\n-\t`gsl.statistics.sdWithFixedMean(data, mean)` \n\tCalculates the standard deviation of data for a fixed population mean mean. The result is the square root of the corresponding variance function.\n\nRunning the tests\n-----------------\n\nTests are executed with expresso. To install it, simply issue `npm install expresso`.\n\nTo run the tests\n\texpresso\n\nContributors\n------------\n\n*\tDavid Worms : \n*\tAlzennyr Gomes da Silva : \n*\tLeeley Daio-Pires-Dos-Santos : \n\n\n","maintainers":[{"name":"david","email":"david@adaltas.com"}]},"0.0.9":{"name":"gsl","version":"0.0.9","description":"GNU Scientific Library for NodeJS.","author":{"name":"David Worms","email":"david@adaltas.com"},"contributors":[{"name":"David Worms","email":"david@adaltas.com"},{"name":"Alzennyr Gomes da Silva","email":"alzennyr@gmail.com"},{"name":"Leeley Daio-Pires-Dos-Santos","email":"leeley.daio-pires-dos-santos@edf.fr"}],"engines":{"node":">= 0.1.90"},"dependencies":{},"devDependencies":{"coffee-script":"latest","mocha":"latest","should":"latest"},"keywords":["library","gsl","mathematics","random","gaussian"],"repository":{"type":"git","url":"https://github.com/wdavidw/node-gsl.git"},"scripts":{"install":"node-waf clean ; node-waf configure build"},"readme":"\n _ \n | |\n __ _ ___| |\n / _` / __| |\n| (_| \\__ \\ |\n \\__, |___/_|\n __/ | \n |___/ \n\n
\n\nThis project provide a binding between the GNU Scientific Library (GSL) and NodeJS.\n\nAt the moment, the library is partially integrated.\n\nInstalling\n----------\n\nVia [npm](http://github.com/isaacs/npm):\n\n $ npm install gsl\n\nVia git (or downloaded tarball):\n\n $ git clone http://github.com/wdavidw/node-gsl.git\n $ node-waf configure && node-waf\n\nRandom API\n----------\n\nThe library takes two forms, functions and iterator objects. Both respect the same names with different conventions. For exemple, obtaining an uniform random name can be done as `random.get()` as well as `(new Random).get()`. If you wished to provide a seed, then you'll respectivelly call `random.get(seed)` and `(new Random(seed)).get()`\n\nUsing an iterator objects make sense when using seeds and when performance is a concern.\n\nSeeds are always optional and must be provided as unsigned integers. 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The range includes 0.0 but excludes 1.0.\n\t\n-\t`gsl.random.gaussian([seed], deviation)` \n\t`gsl.Random.gaussian(deviation)` \n\tReturns a Gaussian random float with mean zero given a standart deviation as a float.\n\t\n-\t`gsl.random.gaussianZiggurat([seed], deviation)` \n\t`gsl.Random.gaussianZiggurat(deviation)` \n\tSame as `random.gaussian` but using the alternative Marsaglia-Tsang ziggurat method.\n\t\n-\t`gsl.random.gaussianRatioMethod([seed], deviation)` \n\t`gsl.Random.gaussianRatioMethod(deviation)` \n\tSame as `random.gaussian` but using the alternative Kinderman-Monahan-Leva ratio method.\n\t\n-\t`gsl.random.poisson([seed], mean)` \n\t`gsl.Random.poisson(mean)` \n\tReturns a random integer from the Poisson distribution given a provided mean as a float.\n\n### Exemple\n\n```javascript\nvar gsl = require('gsl'),\n\tseed = 50,\n\tdeviation = 0.5;\n\nconsole.log( gsl.random.gaussian(deviation) );\nconsole.log( gsl.random.gaussian(seed, deviation) );\n\nvar iterator = new gsl.Random(seed);\nconsole.log( iterator.gaussian(deviation) );\nconsole.log( iterator.gaussian(deviation) );\n```\n\n### Resources\n\n*\t[Sampling](http://www.gnu.org/software/gsl/manual/html_node/Sampling-from-a-random-number-generator.html)\n*\t[The Gaussian Distribution](http://www.gnu.org/software/gsl/manual/html_node/The-Gaussian-Distribution.html)\n*\t[The Poisson Distribution](http://www.gnu.org/software/gsl/manual/html_node/The-Poisson-Distribution.html)\n\nStatistics API\n--------------\n\nData are expected to be arrays of float numbers. Means are float numbers.\n\n-\t`gsl.statistics.mean(data)` \n\tReturns the arithmetic mean of data.\n\t\n-\t`gsl.statistics.variance(data, [mean])` \n\tReturns the estimated, or sample, variance of data.\n\t\n-\t`gsl.statistics.sd(data, [mean])` \n\tReturns the standard deviation defined as the square root of the variance defined above.\n\t\n-\t`gsl.statistics.tss(data, [mean])` \n\tReturn the total sum of squares (TSS) of data about the mean. If mean is not provided, it is computed the same way as above.\n\t\n-\t`gsl.statistics.varianceWithFixedMean(data, mean)` \n\tComputes an unbiased estimate of the variance of data when the population mean mean of the underlying distribution is known a priori.\n\t\n-\t`gsl.statistics.sdWithFixedMean(data, mean)` \n\tCalculates the standard deviation of data for a fixed population mean mean. The result is the square root of the corresponding variance function.\n\nRunning the tests\n-----------------\n\nTests are executed with expresso. 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