# 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

## Usage

```
var incrmcv = require( '@stdlib/stats/incr/mcv' );
```

#### 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 in`NaN`

, the accumulated value is`NaN`

for**at least**`W-1`

future invocations. If non-numeric 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 first`W-1`

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/random/base/randu' );
var incrmcv = require( '@stdlib/stats/incr/mcv' );
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() );
```