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incrmcv
Compute a moving coefficient of variation (CV) incrementally.
For a window of size W
, the corrected sample standard deviation is defined as
and the arithmetic mean is defined as
The coefficient of variation (also known as relative standard deviation, RSD) is defined as
Installation
npm install @stdlib/statsincrmcv
Usage
var incrmcv = require( '@stdlib/statsincrmcv' );
incrmcv( window[, mean] )
Returns an accumulator function
which incrementally computes a moving coefficient of variation. The window
parameter defines the number of values over which to compute the moving coefficient of variation.
var accumulator = incrmcv( 3 );
If the mean is already known, provide a mean
argument.
var accumulator = incrmcv( 3, 5.0 );
accumulator( [x] )
If provided an input value x
, the accumulator function returns an updated accumulated value. If not provided an input value x
, the accumulator function returns the current accumulated value.
var accumulator = incrmcv( 3 );
var cv = accumulator();
// returns null
// Fill the window...
cv = accumulator( 2.0 ); // [2.0]
// returns 0.0
cv = accumulator( 1.0 ); // [2.0, 1.0]
// returns ~0.47
cv = accumulator( 3.0 ); // [2.0, 1.0, 3.0]
// returns 0.5
// Window begins sliding...
cv = accumulator( 7.0 ); // [1.0, 3.0, 7.0]
// returns ~0.83
cv = accumulator( 5.0 ); // [3.0, 7.0, 5.0]
// returns ~0.40
cv = accumulator();
// returns ~0.40
Notes
 Input values are not type checked. If provided
NaN
or a value which, when used in computations, results inNaN
, the accumulated value isNaN
for at leastW1
future invocations. If nonnumeric inputs are possible, you are advised to type check and handle accordingly before passing the value to the accumulator function.  As
W
values are needed to fill the window buffer, the firstW1
returned values are calculated from smaller sample sizes. Until the window is full, each returned value is calculated from all provided values.  The coefficient of variation is typically computed on nonnegative values. The measure may lack meaning for data which can assume both positive and negative values.
 For small and moderately sized samples, the accumulated value tends to be too low and is thus a biased estimator. Provided the generating distribution is known (e.g., a normal distribution), you may want to adjust the accumulated value or use an alternative implementation providing an unbiased estimator.
Examples
var randu = require( '@stdlib/randombaserandu' );
var incrmcv = require( '@stdlib/statsincrmcv' );
var accumulator;
var v;
var i;
// Initialize an accumulator:
accumulator = incrmcv( 5 );
// For each simulated datum, update the moving coefficient of variation...
for ( i = 0; i < 100; i++ ) {
v = randu() * 100.0;
accumulator( v );
}
console.log( accumulator() );
See Also

@stdlib/statsincr/cv
: compute the coefficient of variation (CV) incrementally. 
@stdlib/statsincr/mmean
: compute a moving arithmetic mean incrementally. 
@stdlib/statsincr/mstdev
: compute a moving corrected sample standard deviation incrementally. 
@stdlib/statsincr/mvmr
: compute a moving variancetomean ratio (VMR) incrementally.
Notice
This package is part of stdlib, a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.
For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.
Community
License
See LICENSE.
Copyright
Copyright © 20162024. The Stdlib Authors.