docs: improve README examples of stats/base/dists/negative-binomial

PR-URL: #1774 Closes: #1637 --------- Signed-off-by: Philipp Burckhardt <[email protected]> Co-authored-by: Philipp Burckhardt <[email protected]> Reviewed-by: Philipp Burckhardt <[email protected]>
stdlib-js · Mar 12, 2024 · 37f46fe · 37f46fe · stdlib-bot · Mar 12, 2024
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diff --git a/lib/node_modules/@stdlib/stats/base/dists/negative-binomial/README.md b/lib/node_modules/@stdlib/stats/base/dists/negative-binomial/README.md
@@ -106,10 +106,43 @@ var y = dist.pmf( 4.0 );
 <!-- eslint no-undef: "error" -->
 
 ```javascript
-var objectKeys = require( '@stdlib/utils/keys' );
 var negativeBinomial = require( '@stdlib/stats/base/dists/negative-binomial' );
 
-console.log( objectKeys( negativeBinomial ) );
+/*
+* Let's take an example of flipping a biased coin until getting 5 heads.
+* This situation can be modeled using a Negative Binomial distribution with r = 5 and p = 1/2.
+*/
+
+var r = 5.0;
+var p = 1/2;
+
+// Mean can be used to calculate the average number of trials needed to get 5 heads:
+console.log( negativeBinomial.mean( r, p ) );
+// => 5
+
+// PMF can be used to calculate the probability of getting heads on a specific trial (say on the 8th trial):
+console.log( negativeBinomial.pmf( 8, r, p ) );
+// => ~0.06
+
+// CDF can be used to calculate the probability up to a certain number of trials (say up to 8 trials):
+console.log( negativeBinomial.cdf( 8, r, p ) );
+// => ~0.867
+
+// Quantile can be used to calculate the number of trials at which you can be 80% confident that the actual number will not exceed:
+console.log( negativeBinomial.quantile( 0.8, r, p ) );
+// => 7
+
+// Standard deviation can be used to calculate the measure of the spread of trials around the mean:
+console.log( negativeBinomial.stdev( r, p ) );
+// => ~3.162
+
+// Skewness can be used to calculate the asymmetry of the distribution of trials:
+console.log( negativeBinomial.skewness( r, p ) );
+// => ~0.949
+
+// MGF can be used for more advanced statistical analyses and generating moments of the distribution:
+console.log( negativeBinomial.mgf( 0.5, r, p ) );
+// => ~2277.597
 ```
 
 </section>

diff --git a/lib/node_modules/@stdlib/stats/base/dists/negative-binomial/examples/index.js b/lib/node_modules/@stdlib/stats/base/dists/negative-binomial/examples/index.js
@@ -18,7 +18,40 @@
 
 'use strict';
 
-var objectKeys = require( '@stdlib/utils/keys' );
 var negativeBinomial = require( './../lib' );
 
-console.log( objectKeys( negativeBinomial ) );
+/*
+* Let's take an example of flipping a biased coin until getting 5 heads.
+* This situation can be modeled using a Negative Binomial distribution with r = 5 and p = 1/2.
+*/
+
+var r = 5.0;
+var p = 1/2;
+
+// Mean can be used to calculate the average number of trials needed to get 5 heads:
+console.log( negativeBinomial.mean( r, p ) );
+// => 5
+
+// PMF can be used to calculate the probability of getting heads on a specific trial (say on the 8th trial):
+console.log( negativeBinomial.pmf( 8, r, p ) );
+// => ~0.06
+
+// CDF can be used to calculate the probability up to a certain number of trials (say up to 8 trials):
+console.log( negativeBinomial.cdf( 8, r, p ) );
+// => ~0.867
+
+// Quantile can be used to calculate the number of trials at which you can be 80% confident that the actual number will not exceed:
+console.log( negativeBinomial.quantile( 0.8, r, p ) );
+// => 7
+
+// Standard deviation can be used to calculate the measure of the spread of trials around the mean:
+console.log( negativeBinomial.stdev( r, p ) );
+// => ~3.162
+
+// Skewness can be used to calculate the asymmetry of the distribution of trials:
+console.log( negativeBinomial.skewness( r, p ) );
+// => ~0.949
+
+// MGF can be used for more advanced statistical analyses and generating moments of the distribution:
+console.log( negativeBinomial.mgf( 0.5, r, p ) );
+// => ~2277.597