Random number generator (included): Difference between revisions
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(R random numbers) |
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=={{header|Ruby}}== |
=={{header|Ruby}}== |
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Ruby's <code>rand</code> function currently uses the [[wp:Mersenne twister|Mersenne twister]] algorithm, as described in [http://www.ruby-doc.org/core/classes/Kernel.html#M005974 its documentation]. |
Ruby's <code>rand</code> function currently uses the [[wp:Mersenne twister|Mersenne twister]] algorithm, as described in [http://www.ruby-doc.org/core/classes/Kernel.html#M005974 its documentation]. |
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=={{header|R}}== |
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For uniform random numbers, R may use Wichmann-Hill, Marsaglia-multicarry, Super-Duper, Mersenne-Twister, or Knuth-TAOCP |
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(both 1997 and 2002 versions), or a user-defined method. The default is Mersenne Twister. |
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R is able to generate random numbers from a variety of distributions, e.g. |
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# Beta |
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# Binomial |
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# Cauchy |
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# Chi-Squared |
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# Exponential |
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# F |
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# Gamma |
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# Geometric |
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# Hypergeometric |
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# Logistic |
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# Log Normal |
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# Multinomial |
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# Negative Binomial |
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# Normal |
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# Poisson |
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# Student t |
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# Uniform |
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# Weibull |
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See R help on [http://pbil.univ-lyon1.fr/library/base/html/Random.html Random number generation], or in the R system type |
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<lang R> |
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?RNG |
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help.search("Distribution", package="stats") |
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</lang> |
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=={{header|Ursala}}== |
=={{header|Ursala}}== |
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