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Provides a method to compute an arithmetic mean incrementally.


$ npm install compute-incrmean

For use in the browser, use browserify.


To use the module,

var incrmean = require( 'compute-incrmean' );


Returns an initialized method to compute an arithmetic mean incrementally.

var mean = incrmean();

mean( [value] )

If provided a value, the method updates and returns the updated mean value. If not provided a value, the method returns the current mean value.

mean( 2 );
console.log( mean( 1 ) );
// returns 1.5
mean( 3 );
console.log( mean() );
// returns 2


var incrmean = require( 'compute-incrmean' );
// Initialize a method to calculate the mean incrementally:
var mean = incrmean();
// Simulate some data...
for ( var i = 0; i < 1000; i++ ) {
    mean( Math.random() * 100 );
console.log( mean() );
// returns ~0.5

To run the example code from the top-level application directory,

$ node ./examples/index.js


The use case for this module differs from the conventional vector implementation and the stream implementation.

The use case for the vector implementation is where you have a known dataset and want to calculate a summary statistic (e.g., a single number characterizing the central tendency).

The use case for the stream implementation is where you have either (1) a stream source, which may or may not be definite, or (2) a desire to continually stream each updated value.

The incremental implementation overlaps both use cases, but also provides an additional benefit. Namely, this module decouples the act of updating the mean from the act of consuming the mean.

For example, suppose every 2 seconds your application receives a new value from a remote data source and you want to continuously update the data's mean value.

In a streaming implementation, the updated mean value is either pooled (chunked) or automatically piped to a new destination. The consumer is ultimately responsible for discarding incoming observations.

In contrast to the streaming (push) model, an incremental implementation provides a pull model in which consumers can choose when to observe the current value. Such behavior is important if we consider that, instead of observing on a regular interval (streaming), observations may be random. This module is more amenable to such observation indeterminacy.



Unit tests use the Mocha test framework with Chai assertions. To run the tests, execute the following command in the top-level application directory:

$ make test

All new feature development should have corresponding unit tests to validate correct functionality.

Test Coverage

This repository uses Istanbul as its code coverage tool. To generate a test coverage report, execute the following command in the top-level application directory:

$ make test-cov

Istanbul creates a ./reports/coverage directory. To access an HTML version of the report,

$ make view-cov


MIT license.


Copyright © 2014. Athan Reines.

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