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\n \n\n# Stats\n\n[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url] \n\n> Statistical functions.\n\n\n\n## Usage\n\n```javascript\nvar statistics = require( '@stdlib/stats' );\n```\n\n#### statistics\n\nNamespace containing statistical functions.\n\n```javascript\nvar stats = statistics;\n// returns {...}\n```\n\nThe namespace exposes the following statistical tests:\n\n\n\n\n\n- [`anova1( x, factor[, opts] )`][@stdlib/stats/anova1]: perform a one-way analysis of variance.\n- [`bartlettTest( a[,b,...,k][, opts] )`][@stdlib/stats/bartlett-test]: compute Bartlett’s test for equal variances.\n- [`binomialTest( x[, n][, opts] )`][@stdlib/stats/binomial-test]: exact test for the success probability in a Bernoulli experiment.\n- [`chi2gof( x, y[, ...args][, options] )`][@stdlib/stats/chi2gof]: perform a chi-square goodness-of-fit test.\n- [`chi2test( x[, options] )`][@stdlib/stats/chi2test]: perform a chi-square independence test.\n- [`flignerTest( a[,b,...,k][, opts] )`][@stdlib/stats/fligner-test]: compute the Fligner-Killeen test for equal variances.\n- [`kruskalTest( a[,b,...,k][, opts] )`][@stdlib/stats/kruskal-test]: compute the Kruskal-Wallis test for equal medians.\n- [`kstest( x, y[, ...params][, opts] )`][@stdlib/stats/kstest]: one-sample Kolmogorov-Smirnov goodness-of-fit test.\n- [`leveneTest( x[, y, ..., z][, opts] )`][@stdlib/stats/levene-test]: compute Levene's test for equal variances.\n- [`pcorrtest( x, y[, opts] )`][@stdlib/stats/pcorrtest]: compute a Pearson product-moment correlation test between paired samples.\n- [`ttest( x[, y][, opts] )`][@stdlib/stats/ttest]: one-sample and paired Student's t-Test.\n- [`ttest2( x, y[, opts] )`][@stdlib/stats/ttest2]: two-sample Student's t-Test.\n- [`vartest( x, y[, opts] )`][@stdlib/stats/vartest]: two-sample F-test for equal variances.\n- [`wilcoxon( x[, y][, opts] )`][@stdlib/stats/wilcoxon]: one-sample and paired Wilcoxon signed rank test.\n- [`ztest( x, sigma[, opts] )`][@stdlib/stats/ztest]: one-sample z-Test.\n- [`ztest2( x, y, sigmax, sigmay[, opts] )`][@stdlib/stats/ztest2]: two-sample z-Test.\n\n
\n\n\n\nIn addition, it contains an assortment of functions for computing statistics incrementally as part of the `incr` sub-namespace and functions for computing statistics over iterators in the `iterators` namespace.\n\n\n\n\n\n- [`incr`][@stdlib/stats/incr]: incremental statistics.\n- [`iterators`][@stdlib/stats/iter]: statistical function iterators.\n\n
\n\n\n\nThe namespace further contains functions for computing statistics on arrays as part of the `array` sub-namespace and functions for computing statistics on strided arrays in the `strided` namespace.\n\n\n\n\n\n- [`array`][@stdlib/stats/array]: statistical functions for arrays.\n- [`strided`][@stdlib/stats/strided]: statistical operations for strided arrays.\n\n
\n\n\n\nThe `base` sub-namespace contains lower-level statistical functions, including a `dists` namespace containing functions related to a wide assortment of probability distributions.\n\n\n\n\n\n- [`base`][@stdlib/stats/base]: base (i.e., lower-level) statistical functions.\n\n
\n\n\n\nOther statistical functions included are:\n\n\n\n\n\n- [`cumax( x[, options] )`][@stdlib/stats/cumax]: compute the cumulative maximum value along one or more ndarray dimensions.\n- [`cumin( x[, options] )`][@stdlib/stats/cumin]: compute the cumulative minimum value along one or more ndarray dimensions.\n- [`kde2d()`][@stdlib/stats/kde2d]: two-dimensional kernel density estimation.\n- [`lowess( x, y[, opts] )`][@stdlib/stats/lowess]: locally-weighted polynomial regression via the LOWESS algorithm.\n- [`maxBy( x[, options], clbk[, thisArg] )`][@stdlib/stats/max-by]: compute the maximum value along one or more ndarray dimensions according to a callback function.\n- [`max( x[, options] )`][@stdlib/stats/max]: compute the maximum value along one or more ndarray dimensions.\n- [`maxabs( x[, options] )`][@stdlib/stats/maxabs]: compute the maximum absolute value along one or more ndarray dimensions.\n- [`maxsorted( x[, options] )`][@stdlib/stats/maxsorted]: compute the maximum value along one or more sorted ndarray dimensions.\n- [`mean( x[, options] )`][@stdlib/stats/mean]: compute the arithmetic mean along one or more ndarray dimensions.\n- [`meankbn( x[, options] )`][@stdlib/stats/meankbn]: compute the arithmetic mean along one or more ndarray dimensions using an improved Kahan–Babuška algorithm.\n- [`meankbn2( x[, options] )`][@stdlib/stats/meankbn2]: compute the arithmetic mean along one or more ndarray dimensions using a second-order iterative Kahan–Babuška algorithm.\n- [`meanors( x[, options] )`][@stdlib/stats/meanors]: compute the arithmetic mean along one or more ndarray dimensions using ordinary recursive summation.\n- [`meanpn( x[, options] )`][@stdlib/stats/meanpn]: compute the arithmetic mean along one or more ndarray dimensions using a two-pass error correction algorithm.\n- [`meanpw( x[, options] )`][@stdlib/stats/meanpw]: compute the arithmetic mean along one or more ndarray dimensions using pairwise summation.\n- [`meanwd( x[, options] )`][@stdlib/stats/meanwd]: compute the arithmetic mean along one or more ndarray dimensions using Welford's algorithm.\n- [`mediansorted( x[, options] )`][@stdlib/stats/mediansorted]: compute the median value along one or more sorted ndarray dimensions.\n- [`midrangeBy( x[, options], clbk[, thisArg] )`][@stdlib/stats/midrange-by]: compute the mid-range along one or more ndarray dimensions according to a callback function.\n- [`midrange( x[, options] )`][@stdlib/stats/midrange]: compute the mid-range along one or more ndarray dimensions.\n- [`minBy( x[, options], clbk[, thisArg] )`][@stdlib/stats/min-by]: compute the minimum value along one or more ndarray dimensions according to a callback function.\n- [`min( x[, options] )`][@stdlib/stats/min]: compute the minimum value along one or more ndarray dimensions.\n- [`minabs( x[, options] )`][@stdlib/stats/minabs]: compute the minimum absolute value along one or more ndarray dimensions.\n- [`minsorted( x[, options] )`][@stdlib/stats/minsorted]: compute the minimum value along one or more sorted ndarray dimensions.\n- [`nanmaxBy( x[, options], clbk[, thisArg] )`][@stdlib/stats/nanmax-by]: compute the maximum value along one or more ndarray dimensions according to a callback function, ignoring `NaN` values.\n- [`nanmax( x[, options] )`][@stdlib/stats/nanmax]: compute the maximum value along one or more ndarray dimensions, ignoring `NaN` values.\n- [`nanmaxabs( x[, options] )`][@stdlib/stats/nanmaxabs]: compute the maximum absolute value along one or more ndarray dimensions, ignoring `NaN` values.\n- [`nanmean( x[, options] )`][@stdlib/stats/nanmean]: compute the arithmetic mean along one or more ndarray dimensions, ignoring `NaN` values.\n- [`nanmeanors( x[, options] )`][@stdlib/stats/nanmeanors]: compute the arithmetic mean along one or more ndarray dimensions, ignoring `NaN` values and using ordinary recursive summation.\n- [`nanmeanpn( x[, options] )`][@stdlib/stats/nanmeanpn]: compute the arithmetic mean along one or more ndarray dimensions, ignoring `NaN` values and using a two-pass error correction algorithm.\n- [`nanmeanwd( x[, options] )`][@stdlib/stats/nanmeanwd]: compute the arithmetic mean along one or more ndarray dimensions, ignoring `NaN` values and using Welford's algorithm.\n- [`nanmidrangeBy( x[, options], clbk[, thisArg] )`][@stdlib/stats/nanmidrange-by]: compute the **mid-range** along one or more ndarray dimensions according to a callback function, ignoring `NaN` values.\n- [`nanmidrange( x[, options] )`][@stdlib/stats/nanmidrange]: compute the **mid-range** along one or more ndarray dimensions, ignoring `NaN` values.\n- [`nanminBy( x[, options], clbk[, thisArg] )`][@stdlib/stats/nanmin-by]: compute the minimum value along one or more ndarray dimensions according to a callback function, ignoring `NaN` values.\n- [`nanmin( x[, options] )`][@stdlib/stats/nanmin]: compute the minimum value along one or more ndarray dimensions, ignoring `NaN` values.\n- [`nanminabs( x[, options] )`][@stdlib/stats/nanminabs]: compute the minimum absolute value along one or more ndarray dimensions, ignoring `NaN` values.\n- [`nanrangeBy( x[, options], clbk[, thisArg] )`][@stdlib/stats/nanrange-by]: compute the **range** along one or more ndarray dimensions according to a callback function, ignoring `NaN` values.\n- [`nanrange( x[, options] )`][@stdlib/stats/nanrange]: compute the range along one or more ndarray dimensions, ignoring `NaN` values.\n- [`padjust( pvals, method[, comparisons] )`][@stdlib/stats/padjust]: adjust supplied p-values for multiple comparisons.\n- [`rangeBy( x[, options], clbk[, thisArg] )`][@stdlib/stats/range-by]: compute the **range** along one or more ndarray dimensions according to a callback function.\n- [`range( x[, options] )`][@stdlib/stats/range]: compute the range along one or more ndarray dimensions.\n- [`rangeabs( x[, options] )`][@stdlib/stats/rangeabs]: compute the range of absolute values along one or more ndarray dimensions.\n- [`ranks( arr[, opts] )`][@stdlib/stats/ranks]: compute ranks for values of an array-like object.\n\n
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