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‎CHANGELOG.md

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<section class="release" id="unreleased">
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## Unreleased (2024-06-06)
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## Unreleased (2024-06-08)
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<section class="packages">
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### Contributors
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A total of 6 people contributed to this release. Thank you to the following contributors:
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A total of 7 people contributed to this release. Thank you to the following contributors:
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- Athan Reines
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- Jaimin Godhani
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- Muhammad Haris
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- NightKnight
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- Philipp Burckhardt
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- Rejoan Sardar
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- nishant-s7
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<details>
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- [`922122e`](https://github.com/stdlib-js/stdlib/commit/922122e319c37170f16e14ce5d074273fd7f0675) - **docs:** improve examples for `stats/base/dists/normal` namespace _(by NightKnight, Philipp Burckhardt)_
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- [`c3ebfa8`](https://github.com/stdlib-js/stdlib/commit/c3ebfa80e311db338b171ebf8eb5f46bc66e9bf6) - **docs:** update namespace table of contents and address spelling errors _(by Philipp Burckhardt)_
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- [`ac73b91`](https://github.com/stdlib-js/stdlib/commit/ac73b9195cf4f4e087dca2df535ac49831e1aa07) - **docs:** satisfy spellchecker _(by Philipp Burckhardt)_
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- [`867726b`](https://github.com/stdlib-js/stdlib/commit/867726bb7ef6851ca0986ecdb91bf5a5f246ee5a) - **docs:** update examples in `stats/base/dists/uniform` _(by Muhammad Haris, Philipp Burckhardt)_

‎base/dists/normal/README.md

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<!-- eslint no-undef: "error" -->
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```javascript
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var objectKeys = require( '@stdlib/utils/keys' );
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var normal = require( '@stdlib/stats/base/dists/normal' );
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console.log( objectKeys( normal ) );
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/*
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A bakery is analyzing cake baking times to ensure consistency and better schedule their baking processes.
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The Central Limit Theorem (CLT) states that the average baking times from many batches will follow a normal distribution if there are enough batches (typically n > 30).
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Assuming each record represents the average baking time per batch and the bakery has collected the following data:
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- Mean baking time (μ/mu): 20 minutes.
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- Standard deviation in baking time (σ/sigma): 3 minutes.
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We can model the average bake times using a normal distribution with μ (mu) = 20.0 minutes and σ = 3.0 minutes.
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*/
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var mu = 20.0;
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var sigma = 3.0;
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var normalDist = new normal.Normal( mu, sigma );
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// Output the standard deviation of the baking times:
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console.log( normalDist.sigma );
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// => 3.0
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// Adjust distribution parameters
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normalDist.sigma = 4.0;
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// Adjusted standard deviation to reflect different variance scenario:
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console.log( normalDist.sigma );
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// => 4.0
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// Excess kurtosis of a normal distribution (measure of "tailedness"):
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console.log( normalDist.kurtosis );
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// => 0.0
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// Median baking time:
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console.log( normalDist.median );
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// => 20.0
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// Variance of the baking times after adjusting sigma:
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console.log( normalDist.variance );
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// => 16.0
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// Probability density function at the mean baking time:
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console.log( normal.pdf( 20.0, mu, sigma ) );
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// => ~0.133
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// Cumulative distribution function at the mean (portion of times ≤ 20 minutes):
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console.log( normal.cdf( 20.0, mu, sigma ) );
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// => ~0.5
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// 50th percentile (median) of the baking times:
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console.log( normal.quantile( 0.5, mu, sigma ) );
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// => 20.0
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// Moment-generating function value at 0.5 (used in probability theory):
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console.log( normal.mgf( 0.5, mu, sigma ) );
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// => ~67846.291
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// Entropy of the normal distribution (measure of uncertainty):
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console.log( normal.entropy( mu, sigma ) );
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// => ~2.518
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// Mean baking time:
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console.log( normal.mean( mu, sigma ) );
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// => 20.0
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// Median baking time:
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console.log( normal.median( mu, sigma ) );
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// => 20.0
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// Mode of the baking times (most frequent value):
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console.log( normal.mode( mu, sigma ) );
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// => 20.0
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// Variance of the baking times:
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console.log( normal.variance( mu, sigma ) );
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// => 9.0
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// Skewness of the distribution (symmetry measure):
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console.log( normal.skewness( mu, sigma ) );
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// => 0.0
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var myquantile = normal.quantile.factory( 20.0, 3.0 );
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// 20th percentile (value below which 20% baking times fall):
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console.log( myquantile( 0.2 ) );
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// => ~17.475
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// 80th percentile (value below which 80% baking times fall):
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console.log( myquantile( 0.8 ) );
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// => ~22.525
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var mylogpdf = normal.logpdf.factory( 20.0, 3.0 );
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// Logarithm of the probability density function at the mean:
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console.log( mylogpdf( 20.0 ) );
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// => ~-2.018
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// Logarithm of the probability density function at 15 minutes:
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console.log( mylogpdf( 15.0 ) );
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// => ~-3.406
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```
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</section>
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<!-- /.examples -->

‎base/dists/normal/examples/index.js

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'use strict';
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var objectKeys = require( '@stdlib/utils/keys' );
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var normal = require( './../lib' );
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console.log( objectKeys( normal ) );
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/*
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A bakery is analyzing cake baking times to ensure consistency and better schedule their baking processes.
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The Central Limit Theorem (CLT) states that the average baking times from many batches will follow a normal distribution if there are enough batches (typically n > 30).
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Assuming each record represents the average baking time per batch and the bakery has collected the following data:
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- Mean baking time (μ/mu): 20 minutes.
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- Standard deviation in baking time (σ/sigma): 3 minutes.
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We can model the average bake times using a normal distribution with μ (mu) = 20.0 minutes and σ = 3.0 minutes.
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*/
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var mu = 20.0;
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var sigma = 3.0;
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var normalDist = new normal.Normal( mu, sigma );
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// Output the standard deviation of the baking times:
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console.log( normalDist.sigma );
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// => 3.0
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// Adjust distribution parameters
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normalDist.sigma = 4.0;
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// Adjusted standard deviation to reflect different variance scenario:
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console.log( normalDist.sigma );
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// => 4.0
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// Excess kurtosis of a normal distribution (measure of "tailedness"):
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console.log( normalDist.kurtosis );
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// => 0.0
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// Median baking time:
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console.log( normalDist.median );
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// => 20.0
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// Variance of the baking times after adjusting sigma:
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console.log( normalDist.variance );
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// => 16.0
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// Probability density function at the mean baking time:
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console.log( normal.pdf( 20.0, mu, sigma ) );
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// => ~0.133
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// Cumulative distribution function at the mean (portion of times ≤ 20 minutes):
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console.log( normal.cdf( 20.0, mu, sigma ) );
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// => ~0.5
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// 50th percentile (median) of the baking times:
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console.log( normal.quantile( 0.5, mu, sigma ) );
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// => 20.0
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// Moment-generating function value at 0.5 (used in probability theory):
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console.log( normal.mgf( 0.5, mu, sigma ) );
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// => ~67846.291
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// Entropy of the normal distribution (measure of uncertainty):
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console.log( normal.entropy( mu, sigma ) );
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// => ~2.518
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// Mean baking time:
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console.log( normal.mean( mu, sigma ) );
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// => 20.0
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// Median baking time:
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console.log( normal.median( mu, sigma ) );
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// => 20.0
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// Mode of the baking times (most frequent value):
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console.log( normal.mode( mu, sigma ) );
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// => 20.0
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// Variance of the baking times:
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console.log( normal.variance( mu, sigma ) );
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// => 9.0
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// Skewness of the distribution (symmetry measure):
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console.log( normal.skewness( mu, sigma ) );
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// => 0.0
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var myquantile = normal.quantile.factory( 20.0, 3.0 );
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// 20th percentile (value below which 20% baking times fall):
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console.log( myquantile( 0.2 ) );
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// => ~17.475
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// 80th percentile (value below which 80% baking times fall):
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console.log( myquantile( 0.8 ) );
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// => ~22.525
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var mylogpdf = normal.logpdf.factory( 20.0, 3.0 );
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// Logarithm of the probability density function at the mean:
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console.log( mylogpdf( 20.0 ) );
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// => ~-2.018
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// Logarithm of the probability density function at 15 minutes:
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console.log( mylogpdf( 15.0 ) );
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// => ~-3.406

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