updated readme

Author: prajjwalbajpai

lib/node_modules/@stdlib/stats/base/variancetk/README.md

@@ -98,7 +98,7 @@ The use of the term `n-1` is commonly referred to as Bessel's correction. Note,
 var variancetk = require( '@stdlib/stats/base/variancetk' );
 ```
 
-#### variancetk( N, correction, x, stride )
+#### variancetk( N, correction, x, strideX )
 
 Computes the [variance][variance] of a strided array `x` using a one-pass textbook algorithm.
 
@@ -114,17 +114,14 @@ The function has the following parameters:
 -   **N**: number of indexed elements.
 -   **correction**: degrees of freedom adjustment. Setting this parameter to a value other than `0` has the effect of adjusting the divisor during the calculation of the [variance][variance] according to `N-c` where `c` corresponds to the provided degrees of freedom adjustment. When computing the [variance][variance] of a population, setting this parameter to `0` is the standard choice (i.e., the provided array contains data constituting an entire population). When computing the unbiased sample [variance][variance], setting this parameter to `1` is the standard choice (i.e., the provided array contains data sampled from a larger population; this is commonly referred to as Bessel's correction).
 -   **x**: input [`Array`][mdn-array] or [`typed array`][mdn-typed-array].
--   **stride**: index increment for `x`.
+-   **strideX**: stride length for `x`.
 
-The `N` and `stride` parameters determine which elements in `x` are accessed at runtime. For example, to compute the [variance][variance] of every other element in `x`,
+The `N` and stride parameters determine which elements in the strided array are accessed at runtime. For example, to compute the [variance][variance] of every other element in `x`,
 
 ```javascript
-var floor = require( '@stdlib/math/base/special/floor' );
-
 var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ];
-var N = floor( x.length / 2 );
 
-var v = variancetk( N, 1, x, 2 );
+var v = variancetk( 4, 1, x, 2 );
 // returns 6.25
 ```
 
@@ -134,18 +131,15 @@ Note that indexing is relative to the first index. To introduce an offset, use [
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
-var floor = require( '@stdlib/math/base/special/floor' );
 
 var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
 var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
 
-var N = floor( x0.length / 2 );
-
-var v = variancetk( N, 1, x1, 2 );
+var v = variancetk( 4, 1, x1, 2 );
 // returns 6.25
 ```
 
-#### variancetk.ndarray( N, correction, x, stride, offset )
+#### variancetk.ndarray( N, correction, x, strideX, offsetX )
 
 Computes the [variance][variance] of a strided array using a one-pass textbook algorithm and alternative indexing semantics.
 
@@ -158,17 +152,14 @@ var v = variancetk.ndarray( x.length, 1, x, 1, 0 );
 
 The function has the following additional parameters:
 
--   **offset**: starting index for `x`.
+-   **offsetX**: starting index for `x`.
 
-While [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying `buffer`, the `offset` parameter supports indexing semantics based on a starting index. For example, to calculate the [variance][variance] for every other value in `x` starting from the second value
+While [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying buffer, the offset parameter supports indexing semantics based on a starting index. For example, to calculate the [variance][variance] for every element value in the strided array starting from the second element
 
 ```javascript
-var floor = require( '@stdlib/math/base/special/floor' );
-
 var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ];
-var N = floor( x.length / 2 );
 
-var v = variancetk.ndarray( N, 1, x, 2, 1 );
+var v = variancetk.ndarray( 4, 1, x, 2, 1 );
 // returns 6.25
 ```
 
@@ -181,6 +172,7 @@ var v = variancetk.ndarray( N, 1, x, 2, 1 );
 ## Notes
 
 -   If `N <= 0`, both functions return `NaN`.
+-   Both functions support array-like objects having getter and setter accessors for array element access (e.g., [`@stdlib/array/base/accessor`][@stdlib/array/base/accessor]).
 -   If `N - c` is less than or equal to `0` (where `c` corresponds to the provided degrees of freedom adjustment), both functions return `NaN`.
 -   Some caution should be exercised when using the one-pass textbook algorithm. Literature overwhelmingly discourages the algorithm's use for two reasons: 1) the lack of safeguards against underflow and overflow and 2) the risk of catastrophic cancellation when subtracting the two sums if the sums are large and the variance small. These concerns have merit; however, the one-pass textbook algorithm should not be dismissed outright. For data distributions with a moderately large standard deviation to mean ratio (i.e., **coefficient of variation**), the one-pass textbook algorithm may be acceptable, especially when performance is paramount and some precision loss is acceptable (including a risk of returning a negative variance due to floating-point rounding errors!). In short, no single "best" algorithm for computing the variance exists. The "best" algorithm depends on the underlying data distribution, your performance requirements, and your minimum precision requirements. When evaluating which algorithm to use, consider the relative pros and cons, and choose the algorithm which best serves your needs.
 -   Depending on the environment, the typed versions ([`dvariancetk`][@stdlib/stats/base/dvariancetk], [`svariancetk`][@stdlib/stats/base/svariancetk], etc.) are likely to be significantly more performant.
@@ -196,18 +188,12 @@ var v = variancetk.ndarray( N, 1, x, 2, 1 );
 <!-- eslint no-undef: "error" -->
 
 ```javascript
-var randu = require( '@stdlib/random/base/randu' );
-var round = require( '@stdlib/math/base/special/round' );
-var Float64Array = require( '@stdlib/array/float64' );
+var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
 var variancetk = require( '@stdlib/stats/base/variancetk' );
 
-var x;
-var i;
-
-x = new Float64Array( 10 );
-for ( i = 0; i < x.length; i++ ) {
-    x[ i ] = round( (randu()*100.0) - 50.0 );
-}
+var x = discreteUniform( 10, -50, 50, {
+    'dtype': 'float64'
+});
 console.log( x );
 
 var v = variancetk( x.length, 1, x, 1 );
@@ -257,6 +243,8 @@ console.log( v );
 
 [mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray
 
+[@stdlib/array/base/accessor]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/array/base/accessor
+
 [@stdlib/stats/base/svariancetk]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/svariancetk
 
 [@ling:1974a]: https://doi.org/10.2307/2286154

lib/node_modules/@stdlib/stats/base/variancetk/lib/accessors.js

@@ -53,6 +53,9 @@ function variancetk( N, correction, x, strideX, offsetX ) {
 	var i;
 
 	n = N - correction;
+	if ( N <= 0 || n <= 0.0 ) {
+		return NaN;
+	}
 
 	// Cache reference to array data:
 	xbuf = x.data;
@@ -64,7 +67,7 @@ function variancetk( N, correction, x, strideX, offsetX ) {
 		return get( xbuf, offsetX );
 	}
 	ix = offsetX;
-	S2 = get( xbuf, ix );
+	S2 = 0.0;
 	S = 0.0;
 	for ( i = 0; i < N; i++ ) {
 		v = get( xbuf, ix );