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<!doctype html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>All - jStat Documentation</title>
<link rel="stylesheet" href="assets/style.css" />
<link rel="stylesheet" href="assets/sh.css" />
</head>
<body>
<div id="container">
<header>
<h1>jStat v1.9.3 Documentation</h1>
<div id="gtoc">
<p><a href="index.html">Index</a> | <a href="all.html">View on single page</a></p>
</div>
<hr />
</header>
<div id="toc"><h2>Table Of Contents</h2><ul><li><a href="#overview">Overview</a><ul><li><a href="#description">Description</a></li><li><a href="#architecture">Architecture</a></li></ul></li><li><a href="#core_Functionality">Core Functionality</a><ul><li><a href="#jStat">jStat()</a></li><li><a href="#rows">rows()</a></li><li><a href="#rowa">rowa()</a></li><li><a href="#cols">cols()</a></li><li><a href="#cola">cola()</a></li><li><a href="#slice">slice()</a></li><li><a href="#sliceAssign">sliceAssign()</a></li><li><a href="#dimensions">dimensions()</a></li><li><a href="#row">row()</a></li><li><a href="#col">col()</a></li><li><a href="#diag">diag()</a></li><li><a href="#antidiag">antidiag()</a></li><li><a href="#diagonal">diagonal()</a></li><li><a href="#transpose">transpose()</a></li><li><a href="#map">map( func )</a></li><li><a href="#cumreduce">cumreduce( func )</a></li><li><a href="#alter">alter( func )</a></li><li><a href="#create">create()</a></li><li><a href="#zeros">zeros()</a></li><li><a href="#ones">ones()</a></li><li><a href="#rand">rand()</a></li><li><a href="#copy">copy()</a></li><li><a href="#identity">identity()</a></li><li><a href="#seq">seq()</a></li><li><a href="#arange">arange()</a></li><li><a href="#clear">clear()</a></li><li><a href="#symmetric">symmetric()</a></li></ul></li><li><a href="#jStat_Utility_Methods">jStat Utility Methods</a><ul><li><a href="#utils.calcRdx">utils.calcRdx( num0, num1 )</a></li><li><a href="#utils.isArray">utils.isArray( arg )</a></li><li><a href="#utils.isFunction">utils.isFunction( arg )</a></li><li><a href="#utils.isNumber">utils.isNumber( arg )</a></li></ul></li><li><a href="#vector_Functionality">Vector Functionality</a><ul><li><a href="#sum">sum()</a></li><li><a href="#sumsqrd">sumsqrd()</a></li><li><a href="#sumsqerr">sumsqerr()</a></li><li><a href="#sumrow">sumrow()</a></li><li><a href="#product">product()</a></li><li><a href="#min">min()</a></li><li><a href="#max">max()</a></li><li><a href="#mean">mean()</a></li><li><a href="#meansqerr">meansqerr()</a></li><li><a href="#geomean">geomean()</a></li><li><a href="#median">median()</a></li><li><a href="#cumsum">cumsum()</a></li><li><a href="#cumprod">cumprod()</a></li><li><a href="#diff">diff()</a></li><li><a href="#rank">rank()</a></li><li><a href="#mode">mode()</a></li><li><a href="#range">range()</a></li><li><a href="#variance">variance()</a></li><li><a href="#pooledvariance">pooledvariance()</a></li><li><a href="#deviation">deviation()</a></li><li><a href="#stdev">stdev()</a></li><li><a href="#pooledstdev">pooledstdev()</a></li><li><a href="#meandev">meandev()</a></li><li><a href="#meddev">meddev()</a></li><li><a href="#skewness">skewness()</a></li><li><a href="#kurtosis">kurtosis()</a></li><li><a href="#coeffvar">coeffvar()</a></li><li><a href="#quartiles">quartiles()</a></li><li><a href="#quantiles">quantiles()</a></li><li><a href="#percentile">percentile()</a></li><li><a href="#percentileOfScore">percentileOfScore()</a></li><li><a href="#histogram">histogram()</a></li><li><a href="#covariance">covariance()</a></li><li><a href="#corrcoeff">corrcoeff()</a></li></ul></li><li><a href="#distributions">Distributions</a><ul><li><a href="#jStat.beta">jStat.beta( alpha, beta )</a><ul><li><a href="#jStat.beta.pdf">jStat.beta.pdf( x, alpha, beta )</a></li><li><a href="#jStat.beta.cdf">jStat.beta.cdf( x, alpha, beta )</a></li><li><a href="#jStat.beta.inv">jStat.beta.inv( p, alpha, beta )</a></li><li><a href="#jStat.beta.mean">jStat.beta.mean( alpha, beta )</a></li><li><a href="#jStat.beta.median">jStat.beta.median( alpha, beta )</a></li><li><a href="#jStat.beta.mode">jStat.beta.mode( alpha, beta )</a></li><li><a href="#jStat.beta.sample">jStat.beta.sample( alpha, beta )</a></li><li><a href="#jStat.beta.variance">jStat.beta.variance( alpha, beta )</a></li></ul></li><li><a href="#jStat.centralF">jStat.centralF( df1, df2 )</a><ul><li><a href="#jStat.centralF.pdf">jStat.centralF.pdf( x, df1, df2 )</a></li><li><a href="#jStat.centralF.cdf">jStat.centralF.cdf( x, df1, df2 )</a></li><li><a href="#jStat.centralF.inv">jStat.centralF.inv( p, df1, df2 )</a></li><li><a href="#jStat.centralF.mean">jStat.centralF.mean( df1, df2 )</a></li><li><a href="#jStat.centralF.mode">jStat.centralF.mode( df1, df2 )</a></li><li><a href="#jStat.centralF.sample">jStat.centralF.sample( df1, df2 )</a></li><li><a href="#jStat.centralF.variance">jStat.centralF.variance( df1, df2 )</a></li></ul></li><li><a href="#jStat.cauchy">jStat.cauchy( local, scale )</a><ul><li><a href="#jStat.cauchy.pdf">jStat.cauchy.pdf( x, local, scale )</a></li><li><a href="#jStat.cauchy.cdf">jStat.cauchy.cdf( x, local, scale )</a></li><li><a href="#jStat.cauchy.inv">jStat.cauchy.inv( p, local, scale )</a></li><li><a href="#jStat.cauchy.median">jStat.cauchy.median( local, scale )</a></li><li><a href="#jStat.cauchy.mode">jStat.cauchy.mode( local, scale )</a></li><li><a href="#jStat.cauchy.sample">jStat.cauchy.sample( local, scale )</a></li><li><a href="#jStat.cauchy.variance">jStat.cauchy.variance( local, scale )</a></li></ul></li><li><a href="#jStat.chisquare">jStat.chisquare( dof )</a><ul><li><a href="#jStat.chisquare.pdf">jStat.chisquare.pdf( x, dof )</a></li><li><a href="#jStat.chisquare.cdf">jStat.chisquare.cdf( x, dof )</a></li><li><a href="#jStat.chisquare.inv">jStat.chisquare.inv( p, dof )</a></li><li><a href="#jStat.chisquare.mean">jStat.chisquare.mean( dof )</a></li><li><a href="#jStat.chisquare.median">jStat.chisquare.median( dof )</a></li><li><a href="#jStat.chisquare.mode">jStat.chisquare.mode( dof )</a></li><li><a href="#jStat.chisquare.sample">jStat.chisquare.sample( dof )</a></li><li><a href="#jStat.chisquare.variance">jStat.chisquare.variance( dof )</a></li></ul></li><li><a href="#jStat.exponential">jStat.exponential( rate )</a><ul><li><a href="#jStat.exponential.pdf">jStat.exponential.pdf( x, rate )</a></li><li><a href="#jStat.exponential.cdf">jStat.exponential.cdf( x, rate )</a></li><li><a href="#jStat.exponential.inv">jStat.exponential.inv( p, rate )</a></li><li><a href="#jStat.exponential.mean">jStat.exponential.mean( rate )</a></li><li><a href="#jStat.exponential.median">jStat.exponential.median( rate )</a></li><li><a href="#jStat.exponential.mode">jStat.exponential.mode( rate )</a></li><li><a href="#jStat.exponential.sample">jStat.exponential.sample( rate )</a></li><li><a href="#jStat.exponential.variance">jStat.exponential.variance( rate )</a></li></ul></li><li><a href="#jStat.gamma">jStat.gamma( shape, scale )</a><ul><li><a href="#jStat.gamma.pdf">jStat.gamma.pdf( x, shape, scale )</a></li><li><a href="#jStat.gamma.cdf">jStat.gamma.cdf( x, shape, scale )</a></li><li><a href="#jStat.gamma.inv">jStat.gamma.inv( p, shape, scale )</a></li><li><a href="#jStat.gamma.mean">jStat.gamma.mean( shape, scale )</a></li><li><a href="#jStat.gamma.mode">jStat.gamma.mode( shape, scale )</a></li><li><a href="#jStat.gamma.sample">jStat.gamma.sample( shape, scale )</a></li><li><a href="#jStat.gamma.variance">jStat.gamma.variance( shape, scale )</a></li></ul></li><li><a href="#jStat.invgamma">jStat.invgamma( shape, scale )</a><ul><li><a href="#jStat.invgamma.pdf">jStat.invgamma.pdf( x, shape, scale )</a></li><li><a href="#jStat.invgamma.cdf">jStat.invgamma.cdf( x, shape, scale )</a></li><li><a href="#jStat.invgamma.inv">jStat.invgamma.inv( p, shape, scale )</a></li><li><a href="#jStat.invgamma.mean">jStat.invgamma.mean( shape, scale )</a></li><li><a href="#jStat.invgamma.mode">jStat.invgamma.mode( shape, scale )</a></li><li><a href="#jStat.invgamma.sample">jStat.invgamma.sample( shape, scale )</a></li><li><a href="#jStat.invgamma.variance">jStat.invgamma.variance( shape, scale )</a></li></ul></li><li><a href="#jStat.kumaraswamy">jStat.kumaraswamy( alpha, beta )</a><ul><li><a href="#jStat.kumaraswamy.pdf">jStat.kumaraswamy.pdf( x, a, b )</a></li><li><a href="#jStat.kumaraswamy.cdf">jStat.kumaraswamy.cdf( x, alpha, beta )</a></li><li><a href="#jStat.kumaraswamy.inv">jStat.kumaraswamy.inv( p, alpha, beta )</a></li><li><a href="#jStat.kumaraswamy.mean">jStat.kumaraswamy.mean( alpha, beta )</a></li><li><a href="#jStat.kumaraswamy.median">jStat.kumaraswamy.median( alpha, beta )</a></li><li><a href="#jStat.kumaraswamy.mode">jStat.kumaraswamy.mode( alpha, beta )</a></li><li><a href="#jStat.kumaraswamy.variance">jStat.kumaraswamy.variance( alpha, beta )</a></li></ul></li><li><a href="#jStat.lognormal">jStat.lognormal( mu, sigma )</a><ul><li><a href="#jStat.lognormal.pdf">jStat.lognormal.pdf( x, mu, sigma )</a></li><li><a href="#jStat.lognormal.cdf">jStat.lognormal.cdf( x, mu, sigma )</a></li><li><a href="#jStat.lognormal.inv">jStat.lognormal.inv( p, mu, sigma )</a></li><li><a href="#jStat.lognormal.mean">jStat.lognormal.mean( mu, sigma )</a></li><li><a href="#jStat.lognormal.median">jStat.lognormal.median( mu, sigma )</a></li><li><a href="#jStat.lognormal.mode">jStat.lognormal.mode( mu, sigma )</a></li><li><a href="#jStat.lognormal.sample">jStat.lognormal.sample( mu, sigma )</a></li><li><a href="#jStat.lognormal.variance">jStat.lognormal.variance( mu, sigma )</a></li></ul></li><li><a href="#jStat.normal">jStat.normal( mean, std )</a><ul><li><a href="#jStat.normal.pdf">jStat.normal.pdf( x, mean, std )</a></li><li><a href="#jStat.normal.cdf">jStat.normal.cdf( x, mean, std )</a></li><li><a href="#jStat.normal.inv">jStat.normal.inv( p, mean, std )</a></li><li><a href="#jStat.normal.mean">jStat.normal.mean( mean, std )</a></li><li><a href="#jStat.normal.median">jStat.normal.median( mean, std )</a></li><li><a href="#jStat.normal.mode">jStat.normal.mode( mean, std )</a></li><li><a href="#jStat.normal.sample">jStat.normal.sample( mean, std )</a></li><li><a href="#jStat.normal.variance">jStat.normal.variance( mean, std )</a></li></ul></li><li><a href="#jStat.pareto">jStat.pareto( scale, shape )</a><ul><li><a href="#jStat.pareto.pdf">jStat.pareto.pdf( x, scale, shape )</a></li><li><a href="#jStat.pareto.inv">jStat.pareto.inv( p, scale, shape )</a></li><li><a href="#jStat.pareto.cdf">jStat.pareto.cdf( x, scale, shape )</a></li><li><a href="#jStat.pareto.mean">jStat.pareto.mean( scale, shape )</a></li><li><a href="#jStat.pareto.median">jStat.pareto.median( scale, shape )</a></li><li><a href="#jStat.pareto.mode">jStat.pareto.mode( scale, shape )</a></li><li><a href="#jStat.pareto.variance">jStat.pareto.variance( scale, shape )</a></li></ul></li><li><a href="#jStat.studentt">jStat.studentt( dof )</a><ul><li><a href="#jStat.studentt.pdf">jStat.studentt.pdf( x, dof )</a></li><li><a href="#jStat.studentt.cdf">jStat.studentt.cdf( x, dof )</a></li><li><a href="#jStat.studentt.inv">jStat.studentt.inv( p, dof )</a></li><li><a href="#jStat.studentt.mean">jStat.studentt.mean( dof )</a></li><li><a href="#jStat.studentt.median">jStat.studentt.median( dof )</a></li><li><a href="#jStat.studentt.mode">jStat.studentt.mode( dof )</a></li><li><a href="#jStat.studentt.sample">jStat.studentt.sample( dof )</a></li><li><a href="#jStat.studentt.variance">jStat.studentt.variance( dof )</a></li></ul></li><li><a href="#jStat.tukey">jStat.tukey( nmeans, dof )</a><ul><li><a href="#jStat.tukey.cdf">jStat.tukey.cdf( q, nmeans, dof )</a></li><li><a href="#jStat.tukey.inv">jStat.tukey.inv( p, nmeans, dof )</a></li></ul></li><li><a href="#jStat.weibull">jStat.weibull( scale, shape )</a><ul><li><a href="#jStat.weibull.pdf">jStat.weibull.pdf( x, scale, shape )</a></li><li><a href="#jStat.weibull.cdf">jStat.weibull.cdf( x, scale, shape )</a></li><li><a href="#jStat.weibull.inv">jStat.weibull.inv( p, scale, shape )</a></li><li><a href="#jStat.weibull.mean">jStat.weibull.mean( scale, shape )</a></li><li><a href="#jStat.weibull.median">jStat.weibull.median( scale, shape )</a></li><li><a href="#jStat.weibull.mode">jStat.weibull.mode( scale, shape )</a></li><li><a href="#jStat.weibull.sample">jStat.weibull.sample( scale, shape )</a></li><li><a href="#jStat.weibull.variance">jStat.weibull.variance( scale, shape )</a></li></ul></li><li><a href="#jStat.uniform">jStat.uniform( a, b )</a><ul><li><a href="#jStat.uniform.pdf">jStat.uniform.pdf( x, a, b )</a></li><li><a href="#jStat.uniform.cdf">jStat.uniform.cdf( x, a, b )</a></li><li><a href="#jStat.uniform.inv">jStat.uniform.inv( p, a, b)</a></li><li><a href="#jStat.uniform.mean">jStat.uniform.mean( a, b )</a></li><li><a href="#jStat.uniform.median">jStat.uniform.median( a, b )</a></li><li><a href="#jStat.uniform.mode">jStat.uniform.mode( a, b )</a></li><li><a href="#jStat.uniform.sample">jStat.uniform.sample( a, b )</a></li><li><a href="#jStat.uniform.variance">jStat.uniform.variance( a, b )</a></li></ul></li><li><a href="#jStat.binomial">jStat.binomial</a><ul><li><a href="#jStat.binomial.pdf">jStat.binomial.pdf( k, n, p )</a></li><li><a href="#jStat.binomial.cdf">jStat.binomial.cdf( k, n, p )</a></li></ul></li><li><a href="#jStat.negbin">jStat.negbin</a><ul><li><a href="#jStat.negbin.pdf">jStat.negbin.pdf( k, r, p )</a></li><li><a href="#jStat.negbin.cdf">jStat.negbin.cdf( x, r, p )</a></li></ul></li><li><a href="#jStat.hypgeom">jStat.hypgeom</a><ul><li><a href="#jStat.hypgeom.pdf">jStat.hypgeom.pdf( k, N, m, n )</a></li><li><a href="#jStat.hypgeom.cdf">jStat.hypgeom.cdf( x, N, m, n )</a></li></ul></li><li><a href="#jStat.poisson">jStat.poisson</a><ul><li><a href="#jStat.poisson.pdf">jStat.poisson.pdf( k, l )</a></li><li><a href="#jStat.poisson.cdf">jStat.poisson.cdf( x, l )</a></li><li><a href="#jStat.poisson.sample">jStat.poisson.sample( l )</a></li></ul></li><li><a href="#jStat.triangular">jStat.triangular</a><ul><li><a href="#jStat.triangular.pdf">jStat.triangular.pdf( x, a, b, c )</a></li><li><a href="#jStat.triangular.cdf">jStat.triangular.cdf( x, a, b, c )</a></li><li><a href="#jStat.triangular.mean">jStat.triangular.mean( a, b, c )</a></li><li><a href="#jStat.triangular.median">jStat.triangular.median( a, b, c )</a></li><li><a href="#jStat.triangular.mode">jStat.triangular.mode( a, b, c )</a></li><li><a href="#jStat.triangular.sample">jStat.triangular.sample( a, b, c )</a></li><li><a href="#jStat.triangular.variance">jStat.triangular.variance( a, b, c )</a></li></ul></li><li><a href="#jStat.arcsine">jStat.arcsine( a, b )</a><ul><li><a href="#jStat.arcsine.pdf">jStat.arcsine.pdf( x, a, b )</a></li><li><a href="#jStat.arcsine.cdf">jStat.arcsine.cdf( x, a, b )</a></li><li><a href="#jStat.arcsine.inv">jStat.arcsine.inv(p, a, b)</a></li><li><a href="#jStat.arcsine.mean">jStat.arcsine.mean( a, b )</a></li><li><a href="#jStat.arcsine.median">jStat.arcsine.median( a, b )</a></li><li><a href="#jStat.arcsine.mode">jStat.arcsine.mode( a, b )</a></li><li><a href="#jStat.arcsine.sample">jStat.arcsine.sample( a, b )</a></li><li><a href="#jStat.arcsine.variance">jStat.arcsine.variance( a, b )</a></li></ul></li></ul></li><li><a href="#special_Functions">Special Functions</a><ul><li><a href="#betafn">betafn( x, y )</a></li><li><a href="#betaln">betaln( x, y )</a></li><li><a href="#betacf">betacf( x, a, b )</a></li><li><a href="#ibetainv">ibetainv( p, a, b)</a></li><li><a href="#ibeta">ibeta( x, a, b)</a></li><li><a href="#gammafn">gammafn( x )</a></li><li><a href="#gammaln">gammaln( x )</a></li><li><a href="#gammap">gammap( a, x )</a></li><li><a href="#lowRegGamma">lowRegGamma(a, x)</a></li><li><a href="#gammapinv">gammapinv( p, a )</a></li><li><a href="#factorialln">factorialln( n )</a></li><li><a href="#factorial">factorial( n )</a></li><li><a href="#combination">combination( n, m )</a></li><li><a href="#permutation">permutation( n, m )</a></li><li><a href="#erf">erf( x )</a></li><li><a href="#erfc">erfc( x )</a></li><li><a href="#erfcinv">erfcinv( p )</a></li><li><a href="#randn">randn( n, m )</a></li><li><a href="#randg">randg( shape, n, m )</a></li></ul></li><li><a href="#linear_Algebra">Linear Algebra</a></li><li><a href="#instance_Functionality">Instance Functionality</a><ul><li><a href="#add">add( arg )</a></li><li><a href="#subtract">subtract( arg )</a></li><li><a href="#divide">divide( arg )</a></li><li><a href="#multiply">multiply( arg )</a></li><li><a href="#dot">dot( arg )</a></li><li><a href="#pow">pow( arg )</a></li><li><a href="#exp">exp()</a></li><li><a href="#log">log()</a></li><li><a href="#abs">abs()</a></li><li><a href="#norm">norm()</a></li><li><a href="#angle">angle( arg )</a></li></ul></li><li><a href="#static_Functionality">Static Functionality</a><ul><li><a href="#add">add( arr, arg )</a></li><li><a href="#subtract">subtract( arr, arg )</a></li><li><a href="#divide">divide( arr, arg )</a></li><li><a href="#multiply">multiply( arr, arg )</a></li><li><a href="#dot">dot( arr1, arr2 )</a></li><li><a href="#outer">outer( A, B )</a></li><li><a href="#pow">pow( arr, arg )</a></li><li><a href="#exp">exp( arr )</a></li><li><a href="#log">log( arr )</a></li><li><a href="#abs">abs( arr )</a></li><li><a href="#norm">norm( arr )</a></li><li><a href="#angle">angle( arr1, arr2 )</a></li><li><a href="#aug">aug( A, B )</a></li><li><a href="#det">det( A )</a></li><li><a href="#inv">inv( A )</a></li><li><a href="#gauss_elimination">gauss_elimination( A, B )</a></li><li><a href="#gauss_jordan">gauss_jordan( A, B )</a></li><li><a href="#lu">lu( A )</a></li><li><a href="#cholesky">cholesky( A )</a></li><li><a href="#gauss_jacobi">gauss_jacobi( A, b, x, r )</a></li><li><a href="#gauss_seidel">gauss_seidel( A, b, x, r )</a></li><li><a href="#sOR">SOR( A, b, x, r, w )</a></li><li><a href="#householder">householder( A )</a></li><li><a href="#qR">QR( A )</a></li><li><a href="#lstsq">lstsq( A, b )</a></li><li><a href="#jacobi">jacobi()</a></li><li><a href="#rungekutta">rungekutta()</a></li><li><a href="#romberg">romberg()</a></li><li><a href="#richardson">richardson()</a></li><li><a href="#simpson">simpson()</a></li><li><a href="#hermite">hermite()</a></li><li><a href="#lagrange">lagrange()</a></li><li><a href="#cubic_spline">cubic_spline()</a></li><li><a href="#gauss_quadrature">gauss_quadrature()</a></li><li><a href="#pCA">PCA()</a></li></ul></li><li><a href="#statistical_Tests">Statistical Tests</a></li><li><a href="#statistics_Instance_Functionality">Statistics Instance Functionality</a><ul><li><a href="#zscore">zscore( value[, flag] )</a></li><li><a href="#ztest">ztest( value, sides[, flag] )</a></li><li><a href="#tscore">tscore( value )</a></li><li><a href="#ttest">ttest( value, sides )</a></li><li><a href="#anovafscore">anovafscore()</a></li><li><a href="#anovaftest">anovaftest()</a></li></ul></li><li><a href="#static_Methods">Static Methods</a></li><li><a href="#z_Statistics">Z Statistics</a><ul><li><a href="#jStat.zscore">jStat.zscore( value, mean, sd )</a></li><li><a href="#jStat.zscore">jStat.zscore( value, array[, flag] )</a></li><li><a href="#jStat.ztest">jStat.ztest( value, mean, sd, sides )</a></li><li><a href="#jStat.ztest">jStat.ztest( zscore, sides )</a></li><li><a href="#jStat.ztest">jStat.ztest( value, array, sides[, flag] )</a></li></ul></li><li><a href="#t_Statistics">T Statistics</a><ul><li><a href="#jStat.tscore">jStat.tscore( value, mean, sd, n )</a></li><li><a href="#jStat.tscore">jStat.tscore( value, array )</a></li><li><a href="#jStat.ttest">jStat.ttest( value, mean, sd, n, sides )</a></li><li><a href="#jStat.ttest">jStat.ttest( tscore, n, sides )</a></li><li><a href="#jStat.ttest">jStat.ttest( value, array, sides )</a></li></ul></li><li><a href="#f_Statistics">F Statistics</a><ul><li><a href="#jStat.anovafscore">jStat.anovafscore( array1, array2, ..., arrayn )</a></li><li><a href="#jStat.anovafscore">jStat.anovafscore( [array1,array2, ...,arrayn] )</a></li><li><a href="#jStat.anovaftest">jStat.anovaftest( array1, array2, ...., arrayn )</a></li><li><a href="#jStat.ftest">jStat.ftest( fscore, df1, df2)</a></li></ul></li><li><a href="#tukey_s_Range_Test">Tukey's Range Test</a><ul><li><a href="#jStat.qscore">jStat.qscore( mean1, mean2, n1, n2, sd )</a></li><li><a href="#jStat.qscore">jStat.qscore( array1, array2, sd )</a></li><li><a href="#jStat.qtest">jStat.qtest( qscore, n, k )</a></li><li><a href="#jStat.qtest">jStat.qtest( mean1, mean2, n1, n2, sd, n, k )</a></li><li><a href="#jStat.qtest">jStat.qtest( array1, array2, sd, n, k )</a></li><li><a href="#jStat.tukeyhsd">jStat.tukeyhsd( arrays )</a></li></ul></li><li><a href="#confidence_Intervals">Confidence Intervals</a><ul><li><a href="#jStat.normalci">jStat.normalci( value, alpha, sd, n )</a></li><li><a href="#jStat.normalci">jStat.normalci( value, alpha, array )</a></li><li><a href="#jStat.tci">jStat.tci( value, alpha, sd, n )</a></li><li><a href="#jStat.tci">jStat.tci( value, alpha, array )</a></li><li><a href="#jStat.fn.oneSidedDifferenceOfProportions">jStat.fn.oneSidedDifferenceOfProportions( p1, n1, p2, n2 )</a></li><li><a href="#jStat.fn.twoSidedDifferenceOfProportions">jStat.fn.twoSidedDifferenceOfProportions( p1, n1, p2, n2 )</a></li></ul></li></ul><hr /></div>
<h2 id="overview">Overview</h2>
<h3 id="description">Description</h3>
<p>jStat is a statistical library written in JavaScript that allows you to perform advanced statistical operations without the need of a dedicated statistical language (e.g. MATLAB or R). It is available for download on <a href="http://github.com/jstat/jstat">Github</a>.</p>
<h3 id="architecture">Architecture</h3>
<p>Calculations are done by <em>static methods</em>, while working with groups of numbers is handled by the <em>instance methods</em>.
Here is a pseudo example of what is happening in <code>core.js</code>:</p>
<pre><code>jStat.min = function( arr ) {
return Math.min.apply( null, arr );
}
jStat.prototype.min = function() {
var i = 0,
newval = [];
while( newval.push( jStat.min( this[i] )), ++i < this.length );
return newval;
}</code></pre>
<p><code>jStat.min</code> does the actual calculation on the array, while <code>jStat.prototype.min</code> is a wrapper to help work with the jStat object.
The reason for this approach is to allow for maxium flexibility to other developers who want to extend jStat, while allowing for easy creation of wrappers.
This way extending jStat requires minimal performance overhead and allows for more unique wrappers to be created.</p>
<p><strong>Remember: Static methods almost always return native JavaScript types. Instance methods always return a jStat object.</strong></p>
<p>Here is a simple example on the difference in usage between the static and instance methods:</p>
<pre><code>var myVect = [2,6,4,7,2,7,4],
jObj = jStat( myVect );
// calculate the sum of the the vector
jStat.sum( myVect ) === 32;
jObj.sum() === 32;</code></pre>
<p>Now say we want to do several operations on the vector (e.g. sum, min, max, and standard deviation).
This can be accomplished using the static methods, but each will need to be called separately.
By using the jStat object we can pass callback functions and chain the execution of each operation:</p>
<pre><code>jObj.sum( function( val ) {
// val === sum
}).min( function( val ) {
// val === min
}).max( function( val ) {
// val === max
}).stdev( function( val ) {
// val === st. dev.
});</code></pre>
<p>This method sets each calculation to be executed in an asynchronous queue.
Very useful method of preventing blocking when working with large data sets.</p>
<p>Let's look at a few chaining and shorthand examples:</p>
<pre><code>jStat( 0, 1, 11 ) === jStat( jStat.seq( 0, 1, 11 ));
jStat().rand( 4, 4 ) === jStat( jStat.rand( 4, 4 ));
jStat().create( 5, function( x, y ) {
return ( x + Math.random()) / ( y + Math.random());
}).min( true, function( x ) {
// do something with the min value
}).beta( 0.5, 0.5 ).pdf(); // generate and return the pdf
// of the beta function for all values</code></pre>
<h2 id="core_Functionality">Core Functionality</h2>
<p>Core functionality include methods that generate and analyse vectors or matrices.</p>
<h3 id="jStat">jStat()</h3>
<p>The jStat object can function in several capacities, as demonstrated below.
In all cases, jStat will always return an instance of itself.</p>
<p><strong>jStat( array[, fn] )</strong></p>
<p>Creates a new jStat object from either an existing array or jStat object.
For example, create a new jStat matrix by doing the following:</p>
<pre><code>var matrix = jStat([[ 1, 2, 3 ],[ 4, 5, 6 ],[ 7, 8, 9 ]]);</code></pre>
<p>If an existing jStat object is passed as an argument then it will be cloned into a new object:</p>
<pre><code>var stat1 = jStat([[ 1, 2 ],[ 3, 4 ]]),
stat2 = jStat( stat1 );</code></pre>
<p>To transform the data on creation, pass a function as the final argument:</p>
<pre><code>jStat([[ 1, 2 ],[ 3, 4 ]], function( x ) {
return x * 2;
});</code></pre>
<p><strong>jStat( start, stop, count[, fn ])</strong></p>
<p>To create a sequence then pass numeric values in the same form <code>jStat.seq()</code> would be used:</p>
<pre><code>var vector = jStat( 0, 1, 5 );
// vector === [[ 0, 0.25, 0.5, 0.75, 1 ]]</code></pre>
<p>By passing a function the sequence value can be manipulated:</p>
<pre><code>var vector = jStat( 0, 1, 5, function( x ) {
return x * 2;
});
// vector === [[ 0, 0.5, 1, 1.5, 2 ]];</code></pre>
<p>The second argument passed to the function is the count (starting from 0).
Using this we can create a multidimensional array (useful for plotting data):</p>
<pre><code>var betaGraph = jStat( 0, 1, 11, function( x, cnt ) {
return [ jStat.beta.pdf( x, alpha, beta ), cnt ];
});</code></pre>
<p><strong>jStat()</strong></p>
<p>A chainable shortcut in the API exists to allow for filling in the data after object creation.
So creating <code>jStat</code> objects from methods like <code>rand()</code> can be accomplished in one of the following ways:</p>
<pre><code>// pass the generated random 3x3 matrix to jStat
jStat( jStat.rand( 3 ));
// or create an empty instance that is filled in afterwards
jStat().rand( 3 );</code></pre>
<h3 id="rows">rows()</h3>
<p>Returns the count of rows in a matrix.</p>
<p><strong>rows( array )</strong></p>
<pre><code>var matrix = [[1,2,3],[4,5,6]];
jStat.rows( matrix ) === 2;</code></pre>
<p><strong>fn.rows( [callback] )</strong></p>
<pre><code>jStat( matrix ).rows() === 2;</code></pre>
<p>Or pass a callback to run the calculation asynchronously and pass on the calculation.
This allows for continued chaining of methods to the jStat object.
Also note <code>this</code> within the callback refers to the calling jStat object.</p>
<pre><code>jStat( matrix ).rows(function( d ) {
// d === 2
});</code></pre>
<h3 id="rowa">rowa()</h3>
<p>Returns a array from matrix row.</p>
<pre><code>rowa([[1,2],[3,4]]) === [1,2];</code></pre>
<h3 id="cols">cols()</h3>
<p>Returns the number of columns in a matrix.</p>
<p><strong>cols( array )</strong></p>
<pre><code>var matrix = [[1,2,3],[4,5,6]];
jStat.cols( matrix ) === 3;</code></pre>
<p><strong>fn.cols( [callback] )</strong></p>
<pre><code>jStat( matrix ).cols() === 3;</code></pre>
<p>Or pass a callback to run the calculation asynchronously and pass on the calculation.
This allows for continued chaining of methods to the jStat object.
Also note <code>this</code> within the callback refers to the calling jStat object.</p>
<pre><code>jStat( matrix ).cols(function( d ) {
// d === 3
});</code></pre>
<h3 id="cola">cola()</h3>
<p>Returns an array from matrix column (<code>col()</code> will return a matrix form instead of an array form).</p>
<pre><code>cola([[1,2],[3,4]]) === [1,3];</code></pre>
<h3 id="slice">slice()</h3>
<p>Slices matrix as numpy style.</p>
<pre><code>A=[[1,2,3],[4,5,6],[7,8,9]];
slice(A,{row:{end:2},col:{start:1}}) === [[2,3],[5,6]];
slice(A,1,{start:1}) === [5,6];</code></pre>
<h3 id="sliceAssign">sliceAssign()</h3>
<p>Do slice assign as numpy style.</p>
<pre><code>A = [[1,2,3],[4,5,6],[7,8,9]];
sliceAssign(A,{row : {start : 1}, col : {start : 1}},[[0,0],[0,0]]);
A = [[1,2,3],[4,0,0],[7,0,0]];</code></pre>
<h3 id="dimensions">dimensions()</h3>
<p>Returns an object with the dimensions of a matrix.</p>
<p><strong>dimensions( array )</strong></p>
<pre><code>var matrix = [[1,2,3],[4,5,6]];
jStat.dimensions( matrix ) === { cols: 3, rows: 2 };</code></pre>
<p><strong>fn.dimensions( [callback] )</strong></p>
<pre><code>jStat( matrix ).dimensions() === { cols: 3, rows: 2 };</code></pre>
<p>Or pass a callback to run the calculation asynchronously and pass on the calculation.
This allows for continued chaining of methods to the jStat object.
Also note <code>this</code> within the callback refers to the calling jStat object.</p>
<pre><code>jStat( matrix ).dimensions(function( d ) {
// d === { cols: 3, rows: 2 }
});</code></pre>
<h3 id="row">row()</h3>
<p>Returns a specified row of a matrix.</p>
<p><strong>row( array, index )</strong></p>
<pre><code>var matrix = [[1,2,3],[4,5,6],[7,8,9]];
jStat.row( matrix, 0 ) === [1,2,3];
jStat.row( matrix, [0,1] ) === [[1,2,3],[4,5,6]]</code></pre>
<p><strong>fn.row( index[, callback] )</strong></p>
<pre><code>jStat( matrix ).row( 0 ) === jStat([1,2,3]);</code></pre>
<p>Or pass a callback to run the calculation asynchronously and pass on the calculation.
This allows for continued chaining of methods to the jStat object.
Also note <code>this</code> within the callback refers to the calling jStat object.</p>
<pre><code>jStat( matrix ).row( 0, function( d ) {
// d === jStat([1,2,3])
});</code></pre>
<h3 id="col">col()</h3>
<p>Returns the specified column as a column vector.</p>
<p><strong>col( index )</strong></p>
<pre><code>var matrix = [[1,2,3],[4,5,6],[7,8,9]];
jStat.col( matrix, 0 ) === [[1],[4],[7]];
jStat.col( matrix,[0,1] ) === [[1,2],[4,5],[7,8]]</code></pre>
<p><strong>fn.col( index[, callback] )</strong></p>
<pre><code>jStat( matrix ).col( 0 ) === jStat([[1],[4],[7]]);</code></pre>
<p>Or pass a callback to run the calculation asynchronously and pass on the calculation.
This allows for continued chaining of methods to the jStat object.
Also note <code>this</code> within the callback refers to the calling jStat object.</p>
<pre><code>jStat( matrix ).col( 0, function( d ) {
// d === jStat([[1],[3]])
})</code></pre>
<h3 id="diag">diag()</h3>
<p>Returns the diagonal of a matrix.</p>
<p><strong>diag( array )</strong></p>
<pre><code>var matrix = [[1,2,3],[4,5,6],[7,8,9]];
jStat.diag( matrix ) === [[1],[5],[9]];</code></pre>
<p><strong>fn.diag( [callback] )</strong></p>
<pre><code>jStat( matrix ).diag() === jStat([[1],[5],[9]]);</code></pre>
<p>Or pass a callback to run the calculation asynchronously and pass on the calculation.
This allows for continued chaining of methods to the jStat object.
Also note <code>this</code> within the callback refers to the calling jStat object.</p>
<pre><code>jStat( matrix ).diag(function( d ) {
// d === jStat([[1],[5],[9]])
});</code></pre>
<h3 id="antidiag">antidiag()</h3>
<p>Returns the anti-diagonal of the matrix.</p>
<p><strong>antidiag( array )</strong></p>
<pre><code>var matrix = [[1,2,3],[4,5,6],[7,8,9]];
jStat.antidiag( matrix ) === [[3],[5],[7]];</code></pre>
<p><strong>fn.antidiag( [callback] )</strong></p>
<pre><code>jStat( matrix ).antidiag() === jStat([[3],[5],[7]]);</code></pre>
<p>Or pass a callback to run the calculation asynchronously and pass on the calculation.
This allows for continued chaining of methods to the jStat object.
Also note <code>this</code> within the callback refers to the calling jStat object.</p>
<pre><code>jStat( matrix ).antidiag(function( d ) {
// d === jStat([[3],[5],[7]])
});</code></pre>
<h3 id="diagonal">diagonal()</h3>
<p>Creates a new diagonal matrix by given 1d diag array.</p>
<pre><code>jStat.diagonal([1,2,3]) === [[1,0,0],[0,2,0],[0,0,3]];</code></pre>
<h3 id="transpose">transpose()</h3>
<p>Transposes a matrix.</p>
<p><strong>transpose( array )</strong></p>
<pre><code>var matrix = [[1,2],[3,4]];
jStat.transpose( matrix ) === [[1,3],[2,4]];</code></pre>
<p><strong>fn.transpose( [callback] )</strong></p>
<pre><code>jStat( matrix ).transpose() === [[1,3],[2,4]];</code></pre>
<p>Or pass a callback to run the calculation asynchronously and pass on the calculation.
This allows for continued chaining of methods to the jStat object.
Also note <code>this</code> within the callback refers to the calling jStat object.</p>
<pre><code>jStat( matrix ).transpose(function( d ) {
// d === jStat([[1,3],[2,4]])
})</code></pre>
<h3 id="map">map( func )</h3>
<p>Maps a function to all values and return a new object.</p>
<p><strong>map( array, fn )</strong></p>
<pre><code>var matrix = [[1,2],[3,4]];
jStat.map( matrix, function( x ) {
return x * 2;
});
// returns [[2,4],[6,8]]</code></pre>
<p><strong>fn.map( fn )</strong></p>
<pre><code>jStat( matrix ).map(function( x ) {
return x * 2;
});</code></pre>
<h3 id="cumreduce">cumreduce( func )</h3>
<p>Cumulatively reduces values using a function and return a new object.</p>
<p><strong>cumreduce( array, fn )</strong></p>
<pre><code>var matrix = [[1,2],[3,4]];
jStat.cumreduce( matrix, function( a, b ) {
return a + b;
});
// returns [[1,3],[3,7]]</code></pre>
<p><strong>fn.cumreduce( fn )</strong></p>
<pre><code>jStat( matrix ).cumreduce(function( a, b ) {
return a + b;
});</code></pre>
<h3 id="alter">alter( func )</h3>
<p>Destructively maps to an array.</p>
<p><strong>alter( array, fn )</strong></p>
<pre><code>var matrix = [[1,2],[3,4]];
jStat.alter( matrix, function( x ) {
return x * 2;
});
// matrix === [[2,4],[6,8]]</code></pre>
<p><strong>fn.alter( fn )</strong></p>
<pre><code>var matrix = [[1,2],[3,4]];
jStat( matrix ).alter( function( x ) {
return x * 2;
});</code></pre>
<h3 id="create">create()</h3>
<p>Creates a row by col matrix using the supplied function.
If <code>col</code> is omitted then it will default to value <code>row</code>.</p>
<p><strong>create( row[, col], fn )</strong></p>
<pre><code>jStat.create( 2, function( row, col ) {
return row + col;
});
// returns [[0,1],[1,2]]</code></pre>
<p><strong>fn.create( row[, col], fn )</strong></p>
<p>Use this technique to create matrices in jStat instances.</p>
<pre><code>jStat().create( 2, function( row, col ) {
return row + col;
});
// returns jStat([[0,1],[1,2]])</code></pre>
<h3 id="zeros">zeros()</h3>
<p>Creates a row by col matrix of all zeros.
If <code>col</code> is omitted then it will default to value <code>row</code>.</p>
<p><strong>zeros( row[, col] )</strong></p>
<pre><code>jStat.zeros( 2 );
// returns [[0,0],[0,0]]</code></pre>
<p><strong>fn.zeros( row[, col] )</strong></p>
<p>Use this technique to create matrices in jStat instances.</p>
<pre><code>jStat().zeros( 2 );
// returns jStat([[0,0],[0,0]])</code></pre>
<h3 id="ones">ones()</h3>
<p>Creates a row by col matrix of all ones.
If <code>col</code> is omitted then it will default to value <code>row</code>.</p>
<p><strong>ones( row[, col] )</strong></p>
<pre><code>jStat.zeros( 2 );
// returns [[0,0],[0,0]]</code></pre>
<p><strong>fn.ones( row[, col] )</strong></p>
<p>Use this technique to create matrices in jStat instances.</p>
<pre><code>jStat().ones( 2 );
// returns jStat([[0,0],[0,0]])</code></pre>
<h3 id="rand">rand()</h3>
<p>Creates a matrix of normally distributed random numbers.
If <code>col</code> is omitted then it will default to value <code>row</code>.</p>
<p><strong>rand( row[, col] )</strong></p>
<pre><code>jStat.rand( 3 );</code></pre>
<p><strong>fn.rand( row[, col] )</strong></p>
<p>Use this technique to create matrices in jStat instances.</p>
<pre><code>jStat().rand( 3 );</code></pre>
<h3 id="copy">copy()</h3>
<p>Returns a copy of given matrix.</p>
<h3 id="identity">identity()</h3>
<p>Creates an identity matrix of row by col.
If <code>col</code> is omitted then it will default to value <code>row</code>.</p>
<p><strong>identity( row[, col] )</strong></p>
<pre><code>jStat.identity( 2 );
// returns [[1,0],[0,1]]</code></pre>
<p><strong>fn.identity( row[, col] )</strong></p>
<p>Use this technique to create matrices in jStat instances.</p>
<pre><code>jStat().identity( 2 );</code></pre>
<h3 id="seq">seq()</h3>
<p>Creates an arithmetic sequence by given length.</p>
<pre><code>jStat.seq(1,5,9) === [1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5];</code></pre>
<h3 id="arange">arange()</h3>
<p>Creates an arithmetic sequence by given step.</p>
<pre><code>arange(5) === [0,1,2,3,4]
arange(1,5) === [1,2,3,4]
arange(5,1,-1) === [5,4,3,2]</code></pre>
<h3 id="clear">clear()</h3>
<p>Sets all values in the vector or matrix to zero.</p>
<p><strong>clear( array )</strong></p>
<pre><code>var tmp = [1,2,3];
jStat.clear( tmp );
// tmp === [0,0,0]</code></pre>
<p><strong>fn.clear( [callback] )</strong></p>
<pre><code>jStat( 0, 1, 3 ).clear();
// returns [[0,0,0]]</code></pre>
<p>If a callback is passed then the original object is not altered.</p>
<pre><code>var obj = jStat( 0, 1, 3 );
obj.clear(function() {
// this === [ 0, 0, 0 ]
});
// obj === [ 0, 0.5, 1 ]</code></pre>
<h3 id="symmetric">symmetric()</h3>
<p>Tests if a matrix is symmetric.</p>
<p><strong>symmetric( array )</strong></p>
<pre><code>jStat.symmetric([[1,2],[2,1]]) === true</code></pre>
<p><strong>fn.symmetric( [callback] )</strong></p>
<pre><code>jStat([[1,2],[2,1]]).symmetric() === true</code></pre>
<p>Can pass a callback to maintain chainability.</p>
<pre><code>jStat([[1,2],[2,1]]).symmetric(function( result ) {
// result === true
});</code></pre>
<h2 id="jStat_Utility_Methods">jStat Utility Methods</h2>
<p>Utilities that are used throughout the jStat library.</p>
<h3 id="utils.calcRdx">utils.calcRdx( num0, num1 )</h3>
<p>Calculates the decimal shift for the IEEE 754 floating point calculation correction.</p>
<h3 id="utils.isArray">utils.isArray( arg )</h3>
<p>Tests if <code>arg</code> is an array.</p>
<h3 id="utils.isFunction">utils.isFunction( arg )</h3>
<p>Tests if <code>arg</code> is a function.</p>
<h3 id="utils.isNumber">utils.isNumber( arg )</h3>
<p>Tests if <code>arg</code> is a number and not <code>NaN</code>.</p>
<h2 id="vector_Functionality">Vector Functionality</h2>
<h3 id="sum">sum()</h3>
<p><strong>sum( array )</strong></p>
<p>Returns the sum of the <code>array</code> vector.</p>
<pre><code>jStat.sum([1,2,3]) === 6</code></pre>
<p><strong>fn.sum( [bool][, callback] )</strong></p>
<p>Returns the sum of a vector or matrix columns.</p>
<pre><code>jStat( 1, 5, 5 ).sum() === 15
jStat([[1,2],[3,4]]).sum() === [ 4, 6 ]</code></pre>
<p>If callback is passed then will pass result as first argument.</p>
<pre><code>jStat( 1, 5, 5 ).sum(function( result ) {
// result === 15
});</code></pre>
<p>If pass boolean true as first argument, then return sum of entire matrix.</p>
<pre><code>jStat([[1,2],[3,4]]).sum( true ) === 10</code></pre>
<p>And the two can be combined.</p>
<pre><code>jStat([[1,2],[3,4]]).sum(true, function( result ) {
// result === 10
});</code></pre>
<h3 id="sumsqrd">sumsqrd()</h3>
<p><strong>sumsqrd( array )</strong></p>
<p>Returns the sum squared of the <code>array</code> vector.</p>
<pre><code>jStat.sumsqrd([1,2,3]) === 14</code></pre>
<p><strong>fn.sumsqrd( [bool][, callback] )</strong></p>
<p>Returns the sum squared of a vector or matrix columns.</p>
<pre><code>jStat( 1, 5, 5 ).sumsqrd() === 55
jStat([[1,2],[3,4]]).sumsqrd() === [ 10, 20 ]</code></pre>
<p>If callback is passed then will pass result as first argument.</p>
<pre><code>jStat( 1, 5, 5 ).sumsqrd(function( result ) {
// result === 55
});</code></pre>
<p>If pass boolean true as first argument, then return sum squared of entire matrix.</p>
<pre><code>jStat([[1,2],[3,4]]).sumsqrd( true ) === 650</code></pre>
<p>And the two can be combined.</p>
<pre><code>jStat([[1,2],[3,4]]).sumsqrd(true,function( result ) {
// result === 650
});</code></pre>
<h3 id="sumsqerr">sumsqerr()</h3>
<p><strong>sumsqerr( array )</strong></p>
<p>Returns the sum of squared errors of prediction of the <code>array</code> vector.</p>
<pre><code>jStat.sumsqerr([1,2,3]) === 2</code></pre>
<p><strong>fn.sumsqerr( [bool][, callback] )</strong></p>
<p>Returns the sum of squared errors of prediction of a vector or matrix columns.</p>
<pre><code>jStat( 1, 5, 5 ).sumsqerr() === 10
jStat([[1,2],[3,4]]).sumsqerr() === [ 2, 2 ]</code></pre>
<p>If callback is passed then will pass result as first argument.</p>
<pre><code>jStat( 1, 5, 5 ).sumsqerr(function( result ) {
// result === 55
});</code></pre>
<p>If pass boolean true as first argument, then return sum of squared errors of entire matrix.</p>
<pre><code>jStat([[1,2],[3,4]]).sumsqerr( true ) === 0</code></pre>
<p>And the two can be combined.</p>
<pre><code>jStat([[1,2],[3,4]]).sumsqerr(true,function( result ) {
// result === 0
});</code></pre>
<h3 id="sumrow">sumrow()</h3>
<p><strong>sumrow( array )</strong></p>
<p>Returns the sum of the <code>array</code> vector in row-based order.</p>
<pre><code>jStat.sumrow([1,2,3]) === 6</code></pre>
<p><strong>fn.sumrow( [bool][, callback] )</strong></p>
<p>Returns the sum of a vector or matrix rows.</p>
<pre><code>jStat( 1, 5, 5 ).sumrow() === 15
jStat([[1,2],[3,4]]).sumrow() === [ 3, 7 ]</code></pre>
<p>If callback is passed then will pass result as first argument.</p>
<pre><code>jStat( 1, 5, 5 ).sumrow(function( result ) {
// result === 15
});</code></pre>
<p>If pass boolean true as first argument, then return sum of entire matrix.</p>
<pre><code>jStat([[1,2],[3,4]]).sumrow( true ) === 10</code></pre>
<p>And the two can be combined.</p>
<pre><code>jStat([[1,2],[3,4]]).sumrow(true,function( result ) {
// result === 10
});</code></pre>
<h3 id="product">product()</h3>
<p><strong>product( array )</strong></p>
<p>Returns the product of the <code>array</code> vector.</p>
<pre><code>jStat.product([1,2,3]) === 6</code></pre>
<p><strong>fn.product( [bool][, callback] )</strong></p>
<p>Returns the product of a vector or matrix columns.</p>
<pre><code>jStat( 1, 5, 5 ).product() === 120
jStat([[1,2],[3,4]]).product() === [ 3, 8 ]</code></pre>
<p>If callback is passed then will pass result as first argument.</p>
<pre><code>jStat( 1, 5, 5 ).product(function( result ) {
// result === 120
});</code></pre>
<p>If pass boolean true as first argument, then return sumsqerr of entire matrix.</p>
<pre><code>jStat([[1,2],[3,4]]).product( true ) === 24</code></pre>
<p>And the two can be combined.</p>
<pre><code>jStat([[1,2],[3,4]]).product(true,function( result ) {
// result === 24
});</code></pre>
<h3 id="min">min()</h3>
<p><strong>min( array )</strong></p>
<p>Returns the minimum value of the <code>array</code> vector.</p>
<pre><code>jStat.min([1,2,3]) === 1</code></pre>
<p><strong>fn.min( [bool][, callback] )</strong></p>
<p>Returns the minimum value of a vector or matrix columns.</p>
<pre><code>jStat( 1, 5, 5 ).min() === 1
jStat([[1,2],[3,4]]).min() === [ 1, 2 ]</code></pre>
<p>If callback is passed then will pass result as first argument.</p>
<pre><code>jStat( 1, 5, 5 ).min(function( result ) {
// result === 1
});</code></pre>
<p>If pass boolean true as first argument, then return minimum of entire matrix.</p>
<pre><code>jStat([[1,2],[3,4]]).min( true ) === 1</code></pre>
<p>And the two can be combined.</p>
<pre><code>jStat([[1,2],[3,4]]).min(true,function( result ) {
// result === 1
});</code></pre>
<h3 id="max">max()</h3>
<p><strong>max( array )</strong></p>
<p>Returns the maximum value of the <code>array</code> vector.</p>
<pre><code>jStat.max([1,2,3]) === 3</code></pre>
<p><strong>fn.max( [bool][, callback] )</strong></p>
<p>Returns the maximum value of a vector or matrix columns.</p>
<pre><code>jStat( 1, 5, 5 ).max() === 5
jStat([[1,2],[3,4]]).max() === [ 3, 4 ]</code></pre>
<p>If callback is passed then will pass result as first argument.</p>
<pre><code>jStat( 1, 5, 5 ).max(function( result ) {
// result === 5
});</code></pre>
<p>If pass boolean true as first argument, then return maximum of entire matrix.</p>
<pre><code>jStat([[1,2],[3,4]]).max( true ) === 4</code></pre>
<p>And the two can be combined.</p>
<pre><code>jStat([[1,2],[3,4]]).max(true,function( result ) {
// result === 4
});</code></pre>
<h3 id="mean">mean()</h3>
<p><strong>mean( array )</strong></p>
<p>Returns the mean of the <code>array</code> vector.</p>
<pre><code>jStat.mean([1,2,3]) === 2</code></pre>
<p><strong>fn.max( [bool,][callback] )</strong></p>
<p>Returns the max of a vector or matrix columns.</p>
<pre><code>jStat( 1, 5, 5 ).mean() === 3
jStat([[1,2],[3,4]]).mean() === [ 2, 3 ]</code></pre>
<p>If callback is passed then will pass result as first argument.</p>
<pre><code>jStat( 1, 5, 5 ).mean(function( result ) {
// result === 3
});</code></pre>
<p>If pass boolean true as first argument, then return mean of entire matrix.</p>
<pre><code>jStat([[1,2],[3,4]]).mean( true ) === 2.5</code></pre>
<p>And the two can be combined.</p>
<pre><code>jStat([[1,2],[3,4]]).mean(true,function( result ) {
// result === 2.5
});</code></pre>
<h3 id="meansqerr">meansqerr()</h3>
<p><strong>meansqerr( array )</strong></p>
<p>Returns the mean squared error of the <code>array</code> vector.</p>
<pre><code>jStat.meansqerr([1,2,3]) === 0.66666...</code></pre>
<p><strong>fn.meansqerr( [bool][, callback] )</strong></p>
<p>Returns the mean squared error of a vector or matrix columns.</p>
<pre><code>jStat( 1, 5, 5 ).meansqerr() === 2
jStat([[1,2],[3,4]]).meansqerr() === [ 1, 1 ]</code></pre>
<p>If callback is passed then will pass result as first argument.</p>
<pre><code>jStat( 1, 5, 5 ).meansqerr(function( result ) {
// result === 2
});</code></pre>
<p>If pass boolean true as first argument, then return mean squared error of entire matrix.</p>
<pre><code>jStat([[1,2],[3,4]]).meansqerr( true ) === 0</code></pre>
<p>And the two can be combined.</p>
<pre><code>jStat([[1,2],[3,4]]).meansqerr(true,function( result ) {
// result === 0
});</code></pre>
<h3 id="geomean">geomean()</h3>
<p><strong>geomean( array )</strong></p>
<p>Returns the geometric mean of the <code>array</code> vector.</p>
<pre><code>jStat.geomean([4,1,1/32]) === 0.5</code></pre>
<p><strong>fn.geomean( [bool][, callback] )</strong></p>
<p>Returns the geometric mean of a vector or matrix columns.</p>
<pre><code>jStat([4,1,1\32]).geomean() === 0.5
jStat([[1,2],[3,4]]).geomean() === [ 1.732..., 2.828... ]</code></pre>
<p>If callback is passed then will pass result as first argument.</p>
<pre><code>jStat([4,1,1\32]).geomean(function( result ) {
// result === 0.5
});</code></pre>
<p>If pass boolean true as first argument, then return geometric mean of entire matrix.</p>
<pre><code>jStat([[1,2],[3,4]]).geomean( true ) === 2.213...</code></pre>
<p>And the two can be combined.</p>
<pre><code>jStat([[1,2],[3,4]]).geomean(true,function( result ) {
// result === 2.213...
});</code></pre>
<h3 id="median">median()</h3>
<p><strong>median( array )</strong></p>
<p>Returns the median of the <code>array</code> vector.</p>
<pre><code>jStat.median([1,2,3]) === 2</code></pre>
<p><strong>fn.median( [bool][, callback] )</strong></p>
<p>Returns the median of a vector or matrix columns.</p>
<pre><code>jStat( 1, 5, 5 ).median() === 3
jStat([[1,2],[3,4]]).median() === [ 2, 3 ]</code></pre>
<p>If callback is passed then will pass result as first argument.</p>
<pre><code>jStat( 1, 5, 5 ).median(function( result ) {
// result === 3
});</code></pre>
<p>If pass boolean true as first argument, then return median of entire matrix.</p>
<pre><code>jStat([[1,2],[3,4]]).median( true ) === 2.5</code></pre>
<p>And the two can be combined.</p>
<pre><code>jStat([[1,2],[3,4]]).median(true,function( result ) {
// result === 2.5
});</code></pre>
<h3 id="cumsum">cumsum()</h3>
<p><strong>cumsum( array )</strong></p>
<p>Returns an array of partial sums in the sequence.</p>
<pre><code>jStat.cumsum([1,2,3]) === [1,3,6]</code></pre>
<p><strong>fn.cumsum( [bool][, callback] )</strong></p>
<p>Returns an array of partial sums for a vector or matrix columns.</p>
<pre><code>jStat( 1, 5, 5 ).cumsum() === [1,3,6,10,15]
jStat([[1,2],[3,4]]).cumsum() === [[1,4],[2,6]]</code></pre>
<p>If callback is passed then will pass result as first argument.</p>
<pre><code>jStat( 1, 5, 5 ).cumsum(function( result ) {
// result === [1,3,6,10,15]
});</code></pre>
<p>If pass boolean true as first argument, then return cumulative sums of the matrix.</p>
<pre><code>jStat([[1,2],[3,4]]).cumsum( true ) === [[1,3],[3,7]]</code></pre>
<p>And the two can be combined.</p>
<pre><code>jStat([[1,2],[3,4]]).cumsum(true,function( result ) {
// result === ...
});</code></pre>
<h3 id="cumprod">cumprod()</h3>
<p><strong>cumprod( array )</strong></p>
<p>Returns an array of partial products in the sequence.</p>
<pre><code>jStat.cumprod([2,3,4]) === [2,6,24]</code></pre>
<p><strong>fn.cumprod( [bool][, callback] )</strong></p>
<p>Returns an array of partial products for a vector or matrix columns.</p>
<pre><code>jStat( 1, 5, 5 ).cumprod() === [1,2,6,24,120]
jStat([[1,2],[3,4]]).cumprod() === [[1,3],[2,8]]</code></pre>
<p>If callback is passed then will pass result as first argument.</p>
<pre><code>jStat( 1, 5, 5 ).cumprod(function( result ) {
// result === [1,2,6,24,120]
});</code></pre>
<p>If pass boolean true as first argument, then return cumulative products of the matrix.</p>
<pre><code>jStat([[1,2],[3,4]]).cumprod( true ) === [[1,2],[3,12]]</code></pre>
<p>And the two can be combined.</p>
<pre><code>jStat([[1,2],[3,4]]).cumprod(true,function( result ) {
// result === ...
});</code></pre>
<h3 id="diff">diff()</h3>
<p><strong>diff( array )</strong></p>
<p>Returns an array of the successive differences of the array.</p>
<pre><code>jStat.diff([1,2,2,3]) === [1,0,1]</code></pre>
<p><strong>fn.diff( [bool][, callback] )</strong></p>
<p>Returns an array of successive differences for a vector or matrix columns.</p>
<pre><code>jStat([1,2,2,3]).diff() === [1,0,1]
jStat([[1,2],[3,4],[1,4]]).diff() === [[2,-2],[2,0]]</code></pre>
<p>If callback is passed then will pass result as first argument.</p>
<pre><code>jStat([[1,2],[3,4],[1,4]]).diff(function( result ) {
// result === [[2,-2],[2,0]]
});</code></pre>
<p>If pass boolean true as first argument, then return successive difference for the whole matrix.</p>
<pre><code>jStat([[1,2],[3,4],[1,4]]).diff(true) === [0,2]</code></pre>
<p>And the two can be combined.</p>
<pre><code>jStat([[1,2],[3,4],[1,4]]).diff(true,function( result ) {
// result === [0,2]