chore: clean-up

Author: gururaj1512

lib/node_modules/@stdlib/stats/array/nanstdevtk/README.md

@@ -112,7 +112,7 @@ var v = nanstdevtk( x );
 The function has the following parameters:
 
 -   **x**: input array.
--   **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 [standard deviation][standard-deviation] according to `N-c` where `N` corresponds to the number of array elements and `c` corresponds to the provided degrees of freedom adjustment. When computing the [standard deviation][standard-deviation] 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 [standard deviation][standard-deviation], 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). Default: `1.0`.
+-   **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 [standard deviation][standard-deviation] according to `N-c` where `N` corresponds to the number of non-`NaN` array elements and `c` corresponds to the provided degrees of freedom adjustment. When computing the [standard deviation][standard-deviation] 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 [standard deviation][standard-deviation], 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). Default: `1.0`.
 
 By default, the function computes the sample [standard deviation][standard-deviation]. To adjust the degrees of freedom when computing the [standard deviation][standard-deviation], provide a `correction` argument.
 
@@ -132,7 +132,7 @@ var v = nanstdevtk( x, 0.0 );
 ## Notes
 
 -   If provided an empty array, the function returns `NaN`.
--   If `N - c` is less than or equal to `0` (where `c` corresponds to the provided degrees of freedom adjustment), the function returns `NaN`.
+-   If `N - c` is less than or equal to `0` (where `c` corresponds to the provided degrees of freedom adjustment and `N` corresponds to the number of non-`NaN` array elements), the function returns `NaN`.
 -   The function supports array-like objects having getter and setter accessors for array element access (e.g., [`@stdlib/array/base/accessor`][@stdlib/array/base/accessor]).
 -   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 computing a negative variance due to floating-point rounding errors!). In short, no single "best" algorithm for computing the standard deviation 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.
 

lib/node_modules/@stdlib/stats/array/nanstdevtk/docs/repl.txt

@@ -14,14 +14,14 @@
         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 standard deviation according to `N-c` where `N` corresponds to
-        the number of array elements and `c` corresponds to the provided
+        the number of non-NaN array elements and `c` corresponds to the provided
         degrees of freedom adjustment. When computing the standard deviation 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 standard deviation,
         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). Default: `1.0`.
+        commonly referred to as Bessel's correction). Default: 1.0.
 
     Returns
     -------

lib/node_modules/@stdlib/stats/array/nanstdevtk/docs/types/index.d.ts

@@ -32,10 +32,10 @@ type InputArray = NumericArray | Collection<number> | AccessorArrayLike<number>;
 *
 * ## Notes
 *
-* -   Setting the correction parameter to a value other than `0` has the effect of adjusting the divisor during the calculation of the standard deviation according to `N-c` where `c` corresponds to the provided degrees of freedom adjustment. When computing the standard deviation of a population, setting the correction parameter to `0` is the standard choice (i.e., the provided array contains data constituting an entire population). When computing the corrected sample standard deviation, setting the correction 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).
+* -   Setting the correction parameter to a value other than `0` has the effect of adjusting the divisor during the calculation of the standard deviation according to `N-c` where `N` corresponds to the number of non-`NaN` array elements and `c` corresponds to the provided degrees of freedom adjustment. When computing the standard deviation of a population, setting the correction parameter to `0` is the standard choice (i.e., the provided array contains data constituting an entire population). When computing the corrected sample standard deviation, setting the correction 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).
 *
 * @param x - input array
-* @param correction - degrees of freedom adjustment
+* @param correction - degrees of freedom adjustment (default: 1.0)
 * @returns standard deviation
 *
 * @example

lib/node_modules/@stdlib/stats/array/nanstdevtk/lib/main.js

@@ -45,7 +45,7 @@ var GENERIC_DTYPE = 'generic';
 * @param {number} [correction=1.0] - degrees of freedom adjustment
 * @throws {TypeError} first argument must have a supported data type
 * @throws {TypeError} first argument must be an array-like object
-* @throws {TypeError} second argument must be an number
+* @throws {TypeError} second argument must be a number
 * @returns {number} standard deviation
 *
 * @example

lib/node_modules/@stdlib/stats/array/nanstdevtk/test/test.js

@@ -320,7 +320,7 @@ tape( 'if provided an array containing a single element, the function returns `0
 	t.end();
 });
 
-tape( 'if provided a `correction` parameter which is greater than or equal to the input array length, the function returns `NaN`', function test( t ) {
+tape( 'if provided a `correction` parameter yielding a correction term less than or equal to `0`, the function returns `NaN`', function test( t ) {
 	var x;
 	var v;
 
@@ -335,7 +335,7 @@ tape( 'if provided a `correction` parameter which is greater than or equal to th
 	t.end();
 });
 
-tape( 'if provided a `correction` parameter which is greater than or equal to the input array length, the function returns `NaN` (accessors)', function test( t ) {
+tape( 'if provided a `correction` parameter yielding a correction term less than or equal to `0`, the function returns `NaN` (accessors)', function test( t ) {
 	var x;
 	var v;