feat:node-modules

node_modules/mathjs/lib/esm/expression/embeddedDocs/function/statistics/corr.js

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+export var corrDocs = {
+  name: 'corr',
+  category: 'Statistics',
+  syntax: ['corr(A,B)'],
+  description: 'Compute the correlation coefficient of a two list with values, For matrices, the matrix correlation coefficient is calculated.',
+  examples: ['corr([2, 4, 6, 8],[1, 2, 3, 6])', 'corr(matrix([[1, 2.2, 3, 4.8, 5], [1, 2, 3, 4, 5]]), matrix([[4, 5.3, 6.6, 7, 8], [1, 2, 3, 4, 5]]))'],
+  seealso: ['max', 'mean', 'min', 'median', 'min', 'prod', 'std', 'sum']
+};

node_modules/mathjs/lib/esm/expression/embeddedDocs/function/statistics/cumsum.js

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+export var cumSumDocs = {
+  name: 'cumsum',
+  category: 'Statistics',
+  syntax: ['cumsum(a, b, c, ...)', 'cumsum(A)'],
+  description: 'Compute the cumulative sum of all values.',
+  examples: ['cumsum(2, 3, 4, 1)', 'cumsum([2, 3, 4, 1])', 'cumsum([1, 2; 3, 4])', 'cumsum([1, 2; 3, 4], 1)', 'cumsum([1, 2; 3, 4], 2)'],
+  seealso: ['max', 'mean', 'median', 'min', 'prod', 'std', 'sum', 'variance']
+};

node_modules/mathjs/lib/esm/expression/embeddedDocs/function/statistics/mad.js

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+export var madDocs = {
+  name: 'mad',
+  category: 'Statistics',
+  syntax: ['mad(a, b, c, ...)', 'mad(A)'],
+  description: 'Compute the median absolute deviation of a matrix or a list with values. The median absolute deviation is defined as the median of the absolute deviations from the median.',
+  examples: ['mad(10, 20, 30)', 'mad([1, 2, 3])'],
+  seealso: ['mean', 'median', 'std', 'abs']
+};

node_modules/mathjs/lib/esm/expression/embeddedDocs/function/statistics/max.js

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+export var maxDocs = {
+  name: 'max',
+  category: 'Statistics',
+  syntax: ['max(a, b, c, ...)', 'max(A)', 'max(A, dimension)'],
+  description: 'Compute the maximum value of a list of values.',
+  examples: ['max(2, 3, 4, 1)', 'max([2, 3, 4, 1])', 'max([2, 5; 4, 3])', 'max([2, 5; 4, 3], 1)', 'max([2, 5; 4, 3], 2)', 'max(2.7, 7.1, -4.5, 2.0, 4.1)', 'min(2.7, 7.1, -4.5, 2.0, 4.1)'],
+  seealso: ['mean', 'median', 'min', 'prod', 'std', 'sum', 'variance']
+};

node_modules/mathjs/lib/esm/expression/embeddedDocs/function/statistics/mean.js

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+export var meanDocs = {
+  name: 'mean',
+  category: 'Statistics',
+  syntax: ['mean(a, b, c, ...)', 'mean(A)', 'mean(A, dimension)'],
+  description: 'Compute the arithmetic mean of a list of values.',
+  examples: ['mean(2, 3, 4, 1)', 'mean([2, 3, 4, 1])', 'mean([2, 5; 4, 3])', 'mean([2, 5; 4, 3], 1)', 'mean([2, 5; 4, 3], 2)', 'mean([1.0, 2.7, 3.2, 4.0])'],
+  seealso: ['max', 'median', 'min', 'prod', 'std', 'sum', 'variance']
+};

node_modules/mathjs/lib/esm/expression/embeddedDocs/function/statistics/median.js

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+export var medianDocs = {
+  name: 'median',
+  category: 'Statistics',
+  syntax: ['median(a, b, c, ...)', 'median(A)'],
+  description: 'Compute the median of all values. The values are sorted and the middle value is returned. In case of an even number of values, the average of the two middle values is returned.',
+  examples: ['median(5, 2, 7)', 'median([3, -1, 5, 7])'],
+  seealso: ['max', 'mean', 'min', 'prod', 'std', 'sum', 'variance', 'quantileSeq']
+};

node_modules/mathjs/lib/esm/expression/embeddedDocs/function/statistics/min.js

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+export var minDocs = {
+  name: 'min',
+  category: 'Statistics',
+  syntax: ['min(a, b, c, ...)', 'min(A)', 'min(A, dimension)'],
+  description: 'Compute the minimum value of a list of values.',
+  examples: ['min(2, 3, 4, 1)', 'min([2, 3, 4, 1])', 'min([2, 5; 4, 3])', 'min([2, 5; 4, 3], 1)', 'min([2, 5; 4, 3], 2)', 'min(2.7, 7.1, -4.5, 2.0, 4.1)', 'max(2.7, 7.1, -4.5, 2.0, 4.1)'],
+  seealso: ['max', 'mean', 'median', 'prod', 'std', 'sum', 'variance']
+};

node_modules/mathjs/lib/esm/expression/embeddedDocs/function/statistics/mode.js

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+export var modeDocs = {
+  name: 'mode',
+  category: 'Statistics',
+  syntax: ['mode(a, b, c, ...)', 'mode(A)', 'mode(A, a, b, B, c, ...)'],
+  description: 'Computes the mode of all values as an array. In case mode being more than one, multiple values are returned in an array.',
+  examples: ['mode(2, 1, 4, 3, 1)', 'mode([1, 2.7, 3.2, 4, 2.7])', 'mode(1, 4, 6, 1, 6)'],
+  seealso: ['max', 'mean', 'min', 'median', 'prod', 'std', 'sum', 'variance']
+};

node_modules/mathjs/lib/esm/expression/embeddedDocs/function/statistics/prod.js

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+export var prodDocs = {
+  name: 'prod',
+  category: 'Statistics',
+  syntax: ['prod(a, b, c, ...)', 'prod(A)'],
+  description: 'Compute the product of all values.',
+  examples: ['prod(2, 3, 4)', 'prod([2, 3, 4])', 'prod([2, 5; 4, 3])'],
+  seealso: ['max', 'mean', 'min', 'median', 'min', 'std', 'sum', 'variance']
+};

node_modules/mathjs/lib/esm/expression/embeddedDocs/function/statistics/quantileSeq.js

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+export var quantileSeqDocs = {
+  name: 'quantileSeq',
+  category: 'Statistics',
+  syntax: ['quantileSeq(A, prob[, sorted])', 'quantileSeq(A, [prob1, prob2, ...][, sorted])', 'quantileSeq(A, N[, sorted])'],
+  description: 'Compute the prob order quantile of a matrix or a list with values. The sequence is sorted and the middle value is returned. Supported types of sequence values are: Number, BigNumber, Unit Supported types of probability are: Number, BigNumber. \n\nIn case of a (multi dimensional) array or matrix, the prob order quantile of all elements will be calculated.',
+  examples: ['quantileSeq([3, -1, 5, 7], 0.5)', 'quantileSeq([3, -1, 5, 7], [1/3, 2/3])', 'quantileSeq([3, -1, 5, 7], 2)', 'quantileSeq([-1, 3, 5, 7], 0.5, true)'],
+  seealso: ['mean', 'median', 'min', 'max', 'prod', 'std', 'sum', 'variance']
+};

node_modules/mathjs/lib/esm/expression/embeddedDocs/function/statistics/std.js

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+export var stdDocs = {
+  name: 'std',
+  category: 'Statistics',
+  syntax: ['std(a, b, c, ...)', 'std(A)', 'std(A, dimension)', 'std(A, normalization)', 'std(A, dimension, normalization)'],
+  description: 'Compute the standard deviation of all values, defined as std(A) = sqrt(variance(A)). Optional parameter normalization can be "unbiased" (default), "uncorrected", or "biased".',
+  examples: ['std(2, 4, 6)', 'std([2, 4, 6, 8])', 'std([2, 4, 6, 8], "uncorrected")', 'std([2, 4, 6, 8], "biased")', 'std([1, 2, 3; 4, 5, 6])'],
+  seealso: ['max', 'mean', 'min', 'median', 'prod', 'sum', 'variance']
+};

node_modules/mathjs/lib/esm/expression/embeddedDocs/function/statistics/sum.js

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+export var sumDocs = {
+  name: 'sum',
+  category: 'Statistics',
+  syntax: ['sum(a, b, c, ...)', 'sum(A)', 'sum(A, dimension)'],
+  description: 'Compute the sum of all values.',
+  examples: ['sum(2, 3, 4, 1)', 'sum([2, 3, 4, 1])', 'sum([2, 5; 4, 3])'],
+  seealso: ['max', 'mean', 'median', 'min', 'prod', 'std', 'sum', 'variance']
+};

node_modules/mathjs/lib/esm/expression/embeddedDocs/function/statistics/variance.js

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+export var varianceDocs = {
+  name: 'variance',
+  category: 'Statistics',
+  syntax: ['variance(a, b, c, ...)', 'variance(A)', 'variance(A, dimension)', 'variance(A, normalization)', 'variance(A, dimension, normalization)'],
+  description: 'Compute the variance of all values. Optional parameter normalization can be "unbiased" (default), "uncorrected", or "biased".',
+  examples: ['variance(2, 4, 6)', 'variance([2, 4, 6, 8])', 'variance([2, 4, 6, 8], "uncorrected")', 'variance([2, 4, 6, 8], "biased")', 'variance([1, 2, 3; 4, 5, 6])'],
+  seealso: ['max', 'mean', 'min', 'median', 'min', 'prod', 'std', 'sum']
+};