## Usage ```javascript var incrme = require( '@stdlib/stats/incr/me' ); ``` #### incrme() Returns an accumulator `function` which incrementally computes the [mean error][mean-absolute-error]. ```javascript var accumulator = incrme(); ``` #### accumulator( \[x, y] ) If provided input values `x` and `y`, the accumulator function returns an updated [mean error][mean-absolute-error]. If not provided input values `x` and `y`, the accumulator function returns the current [mean error][mean-absolute-error]. ```javascript var accumulator = incrme(); var m = accumulator( 2.0, 3.0 ); // returns 1.0 m = accumulator( -1.0, -4.0 ); // returns -1.0 m = accumulator( -3.0, 5.0 ); // returns 2.0 m = accumulator(); // returns 2.0 ```

## 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 **all** 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. - Be careful when interpreting the [mean error][mean-absolute-error] as errors can cancel. This stated, that errors can cancel makes the [mean error][mean-absolute-error] suitable for measuring the bias in forecasts. - **Warning**: the [mean error][mean-absolute-error] is scale-dependent and, thus, the measure should **not** be used to make comparisons between datasets having different scales.

## Examples ```javascript var randu = require( '@stdlib/random/base/randu' ); var incrme = require( '@stdlib/stats/incr/me' ); var accumulator; var v1; var v2; var i; // Initialize an accumulator: accumulator = incrme(); // For each simulated datum, update the mean error... for ( i = 0; i < 100; i++ ) { v1 = ( randu()*100.0 ) - 50.0; v2 = ( randu()*100.0 ) - 50.0; accumulator( v1, v2 ); } console.log( accumulator() ); ```