chore: update package.json & README notes & example

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Author: officiallyanee

lib/node_modules/@stdlib/stats/incr/nanmpe/README.md

@@ -33,7 +33,7 @@ The [mean percentage error][mean-percentage-error] is defined as
 ```
 
 <!-- <div class="equation" align="center" data-raw-text="\operatorname{MPE}  = \frac{100}{n} \sum_{i=0}^{n-1} \frac{a_i - f_i}{a_i}" data-equation="eq:mean_percentage_error">
-    <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@2acedf866c9a4f1353af22f95780535612c5ee06/lib/node_modules/@stdlib/stats/incr/nampe/docs/img/equation_mean_percentage_error.svg" alt="Equation for the mean percentage error.">
+    <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@2acedf866c9a4f1353af22f95780535612c5ee06/lib/node_modules/@stdlib/stats/incr/nanmpe/docs/img/equation_mean_percentage_error.svg" alt="Equation for the mean percentage error.">
     <br>
 </div> -->
 
@@ -95,8 +95,8 @@ m = accumulator();
 
 ## Notes
 
--   Input values are **not** type checked. If provided `NaN`, the accumulated value is `NaN` for **all** future invocations **until** a non-`NaN` value pair is provided. If non-numeric inputs are possible, you are advised to type check and handle accordingly **before** passing the value to the accumulator function.
--   Note that if either `f` or `a` is `NaN`, the input value pair is **skipped** (i.e., the pair does not contribute to the accumulated value).
+-   Input values are **not** type checked.If provided `NaN`, the value is ignored and the accumulator function returns the current [mean percentage error][mean-percentage-error] without updating the window. If non-numeric inputs are possible, you are advised to type check and handle accordingly **before** passing the value to the accumulator function.
+-   If either `f` or `a` is `NaN`, the input value pair is **skipped** (i.e., the pair does not contribute to the accumulated value).
 -   Be careful when interpreting the [mean percentage error][mean-percentage-error] as errors can cancel. This stated, that errors can cancel makes the [mean percentage error][mean-percentage-error] suitable for measuring the bias in forecasts.
 -   **Warning**: the [mean percentage error][mean-percentage-error] is **not** suitable for intermittent demand patterns (i.e., when `a_i` is `0`). Interpretation is most straightforward when actual and forecast values are positive valued (e.g., number of widgets sold).
 
@@ -112,21 +112,19 @@ m = accumulator();
 
 ```javascript
 var bernoulli = require( '@stdlib/random/base/bernoulli' );
-var randu = require( '@stdlib/random/base/randu' );
+var uniform = require( '@stdlib/random/base/uniform' );
 var incrnanmpe = require( '@stdlib/stats/incr/nanmpe' );
 
-var accumulator;
-var v1;
-var v2;
-var i;
-
 // Initialize an accumulator:
-accumulator = incrnanmpe();
+var accumulator = incrnanmpe();
 
 // For each simulated datum, update the mean percentage error...
+var v1;
+var v2;
+var i;
 for ( i = 0; i < 100; i++ ) {
-    v1 = ( bernoulli( 0.8 ) === 0 ) ? NaN : ( randu()*100.0 ) + 50.0;
-    v2 = ( bernoulli( 0.8 ) === 0 ) ? NaN : ( randu()*100.0 ) + 50.0;
+    v1 = ( bernoulli( 0.8 ) < 1 ) ? NaN : uniform( -50.0, 50.0 );
+    v2 = ( bernoulli( 0.8 ) < 1 ) ? NaN : uniform( -50.0, 50.0 );
     accumulator( v1, v2 );
 }
 console.log( accumulator() );

lib/node_modules/@stdlib/stats/incr/nanmpe/package.json

@@ -73,6 +73,8 @@
     "time series",
     "timeseries",
     "forecasting",
-    "forecast"
+    "forecast",
+    "nan",
+    "ignore"
   ]
 }