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Merge branch 'develop' of https://github.com/stdlib-js/stdlib into develop
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‎README.md‎

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<section class="intro">
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Stdlib is a standard library for JavaScript and Node.js, with an emphasis on numeric computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more. This is the GitHub repository of stdlib source code and documentation.
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stdlib is a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing applications. The library provides a collection of robust, high performance libraries for mathematics, statistics, data processing, streams, and more and includes many of the utilities you would expect from a standard library.
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For help developing stdlib, see the [development guide][stdlib-development].
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This is the GitHub repository of stdlib source code and documentation. For help developing stdlib, see the [development guide][stdlib-development].
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## Features
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‎docs/editors/sublime-text/snippets/stdlib-cli.sublime-snippet‎

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<snippet>
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<content><![CDATA[<!-- Section for describing a command-line interface. -->
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* * *
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<section class="cli">
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## CLI
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<!-- CLI usage documentation. -->
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<section class="usage">
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### Usage
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```text
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Usage: TODO [options]
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Options:
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-h, --help Print this message.
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-V, --version Print the package version.
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```
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</section>
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<!-- /.usage -->
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<!-- CLI usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
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<section class="notes">
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</section>
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<!-- /.notes -->
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<!-- CLI usage examples. -->
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<section class="examples">
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### Examples
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```bash
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\$ TODO
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TODO
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```
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</section>
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<!-- /.examples -->
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</section>
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<!-- /.cli -->
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]]></content>
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<!-- Tab trigger to activate the snippet -->
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<tabTrigger>stdlib.cli</tabTrigger>
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<!-- Scope the tab trigger will be active in -->
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<scope>text.html.markdown</scope>
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<!-- Description to show in the menu -->
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<description>CLI documentation section</description>
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</snippet>

‎docs/editors/sublime-text/snippets/stdlib-equation.sublime-snippet‎

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<snippet>
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<content><![CDATA[<!-- <equation class="equation" label="eq:${1:TODO}" align="center" raw="${2:TODO}" alt="${3:TODO}"> -->
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<div class="equation" align="center" data-raw-text="${2:TODO}" data-equation="eq:${1:TODO}">
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<img src="TODO" alt="${3:TODO}">
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<br>
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</div>
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<!-- </equation> -->
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]]></content>
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<!-- Tab trigger to activate the snippet -->
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<tabTrigger>stdlib.equation</tabTrigger>
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<!-- Scope the tab trigger will be active in -->
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<scope>text.html.markdown</scope>
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<!-- Description to show in the menu -->
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<description>Markdown HTML equation</description>
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</snippet>

‎docs/editors/sublime-text/snippets/stdlib-figure.sublime-snippet‎

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<snippet>
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<content><![CDATA[<!-- <figure class="figure" align="center" label="fig:${1:TODO}" src="./srv/scripts/${2:TODO}.js" alt="${3:TODO}"> -->
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<div class="figure" align="center" data-figure="fig:${1:TODO}">
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<img src="TODO" alt="${3:TODO}">
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<br>
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</div>
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<!-- </figure> -->
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]]></content>
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<!-- Tab trigger to activate the snippet -->
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<tabTrigger>stdlib.figure</tabTrigger>
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<!-- Scope the tab trigger will be active in -->
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<scope>text.html.markdown</scope>
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<!-- Description to show in the menu -->
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<description>Markdown HTML figure</description>
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</snippet>

‎docs/links/database.json‎

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"stats"
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]
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},
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"https://en.wikipedia.org/wiki/Arithmetic_mean": {
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"id": "arithmetic-mean",
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"description": "Wikipedia entry for the arithmetic mean.",
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"short_url": "",
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"keywords": [
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"arithmetic",
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"mean",
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"average",
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"avg",
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"statistics",
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"stats"
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]
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},
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"https://en.wikipedia.org/wiki/Bernoulli_number": {
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"id": "bernoulli-number",
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"description": "Wikipedia entry for the Bernoulli numbers.",
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"content delivery network"
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]
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},
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"https://en.wikipedia.org/wiki/Covariance": {
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"id": "covariance",
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"description": "Wikipedia entry for covariance, a measure of joint variability of two random variables.",
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"short_url": "",
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"keywords": [
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"covariance",
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"statistics",
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"stats",
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"variance"
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]
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},
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"https://en.wikipedia.org/wiki/Cumulative_distribution_function": {
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"id": "cdf",
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"description": "Wikipedia entry for the cumulative distribution function.",
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"geometric"
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]
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},
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"https://en.wikipedia.org/wiki/Geometric_mean": {
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"id": "geometric-mean",
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"description": "Wikipedia entry for the geometric mean which is defined as the nth root of a product of n numbers.",
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"short_url": "",
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"keywords": [
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"geometric",
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"mean",
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"product",
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"prod",
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"statistics",
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"stats"
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]
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},
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"https://en.wikipedia.org/wiki/Given_name": {
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"id": "given-name",
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"description": "Wikipedia entry on given / first names.",
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"format"
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]
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},
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"https://en.wikipedia.org/wiki/Harmonic_mean": {
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"id": "harmonic-mean",
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"description": "Wikipedia entry for the harmonic mean.",
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"short_url": "",
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"keywords": [
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"harmonic",
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"mean",
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"avg",
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"average",
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"statistics",
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"stats"
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]
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},
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"https://en.wikipedia.org/wiki/Horner%27s_method": {
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"id": "horners-method",
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"description": "Wikipedia entry for Horner's method, an algorithm for evaluating polynomials.",
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"probability"
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]
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},
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"https://en.wikipedia.org/wiki/Moving_average": {
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"id": "moving-average",
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"description": "Wikipedia entry for a moving average.",
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"short_url": "",
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"keywords": [
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"statistics",
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"stats",
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"average",
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"avg",
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"mean",
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"moving",
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"sliding window"
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]
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},
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"https://en.wikipedia.org/wiki/NOP": {
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"id": "nop",
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"description": "Wikipedia entry for the no operation instruction.",
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},
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"https://en.wikipedia.org/wiki/Pearson_correlation_coefficient": {
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"id": "pearson-correlation",
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"description": "Wikipedia entry for the Pearsonc correlation coefficient.",
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"description": "Wikipedia entry for the Pearson product-moment correlation coefficient, a measure of linear correlation between two variables X and Y.",
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"short_url": "",
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"keywords": [
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"statistics",
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"stats",
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"correlation",
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"pearson"
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"pearson",
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"linear",
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"pcc",
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"ppmcc",
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"coefficient"
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},
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"https://en.wikipedia.org/wiki/Percent-encoding": {

‎docs/policies/domains.md‎

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List of **currently** unused domains:
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- [stdlib.ai](http://stdlib.ai)
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- [stdlibjs.com](http://stdlibjs.com)
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- [stdlib.co](http://stdlib.co)
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- [stdlibjs.co](http://stdlibjs.co)

‎docs/references/bib.bib‎

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volume = {74},
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year = {1979}
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}
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@article{alakkari:2014a,
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abstract = {In this paper, we present an online adaptive PCA algorithm that is able to compute the full dimensional eigenspace per new time-step of sequential data. The algorithm is based on a one-step update rule that considers all second order correlations between previous samples and the new time-step. Our algorithm has O(n) complexity per new time-step in its deterministic mode and O(1) complexity per new time-step in its stochastic mode. We test our algorithm on a number of time-varying datasets of different physical phenomena. Explained variance curves indicate that our technique provides an excellent approximation to the original eigenspace computed using standard PCA in batch mode. In addition, our experiments show that the stochastic mode, despite its much lower computational complexity, converges to the same eigenspace computed using the deterministic mode.},
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author = {Salaheddin Alakkari and John Dingliana},
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doi = {},
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journal = {arXiv},
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keywords = {pca, incremental, on-line, online, statistics, stats, principal component analysis},
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month = {sep},
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number = {},
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pages = {},
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title = {{Adaptive PCA for Time-Varying Data}},
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url = {https://arxiv.org/abs/1709.02373},
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volume = {abs/1709.02373},
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year = {2017},
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}
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@article{chan:1983a,
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abstract = {The problem of computing the variance of a sample of \\(N\\) data points \\({x_i}\\) may be difficult for certain data sets, particularly when \\(N\\) is large and the variance is small. We present a survey of possible algorithms and their round-off error bounds, including some new analysis for computations with shifted data. Experimental results confirm these bounds and illustrate the dangers of some algorithms. Specific recommendations are made as to which algorithm should be used in various contexts.},
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author = {Tony F. Chan and Gene H. Golub and Randall J. LeVeque},
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issn = {00031305},
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journal = {The American Statistician},
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keywords = {statistics, stats, algorithms, variance, standard deviation},
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month = {aug},
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number = {3},
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pages = {242-247},
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publisher = {American Statistical Association, Taylor & Francis, Ltd.},
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title = {{Algorithms for Computing the Sample Variance: Analysis and Recommendations}},
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url = {http://www.jstor.org/stable/2683386},
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volume = {37},
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year = {1983},
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}
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@article{ling:1974a,
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abstract = {Several one-pass and two-pass algorithms for the computation of sample means and variances are compared by their performance on sets of randomly generated data and systematically generated data with random noise. The relation between the performance of each algorithm and the coefficient of variation of the population from which random data sets are generated is explored.},
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author = {Robert F. Ling},
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issn = {01621459},
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journal = {Journal of the American Statistical Association},
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keywords = {statistics, stats, algorithm, algo, mean, variance, average, incremental, on-line, online},
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month = {dec},
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number = {348},
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pages = {859-866},
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publisher = {American Statistical Association, Taylor & Francis, Ltd.},
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title = {{Comparison of Several Algorithms for Computing Sample Means and Variances}},
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url = {http://www.jstor.org/stable/2286154},
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volume = {69},
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year = {1974},
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}

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