Add missing spaces and fix typo

Planeshifter · Planeshifter · commit 6ce9d1d7174a · 2019-02-15T23:50:44.000-05:00
diff --git a/lib/node_modules/@stdlib/stats/incr/cv/README.md b/lib/node_modules/@stdlib/stats/incr/cv/README.md
@@ -112,7 +112,7 @@ cv = accumulator();
 ## 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.
--   The [coefficient of variation][coefficient-of-variation] is typically computed on nonnegativealues. The measure may lack meaning for data which can assume both positive and negative values.
+-   The [coefficient of variation][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.
 
 </section>
diff --git a/lib/node_modules/@stdlib/stats/incr/mcv/README.md b/lib/node_modules/@stdlib/stats/incr/mcv/README.md
@@ -124,7 +124,7 @@ cv = accumulator();
 
 -   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][coefficient-of-variation] is typically computed on nonnegativevalues. The measure may lack meaning for data which can assume both positive and negative values.
+-   The [coefficient of variation][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.
 
 </section>
diff --git a/lib/node_modules/@stdlib/stats/incr/mvmr/README.md b/lib/node_modules/@stdlib/stats/incr/mvmr/README.md
@@ -137,7 +137,7 @@ F = accumulator();
 
     Accordingly, one can use the [variance-to-mean ratio][variance-to-mean-ratio] to assess whether observed data can be modeled as a Poisson process. When observed data is "under-dispersed", observed data may be more regular than as would be the case for a Poisson process. When observed data is "over-dispersed", observed data may contain clusters (i.e., clumped, concentrated data).
 
--   The [variance-to-mean ratio][variance-to-mean-ratio] is typically computed on nonnegativevalues. The measure may lack meaning for data which can assume both positive and negative values.
+-   The [variance-to-mean ratio][variance-to-mean-ratio] is typically computed on nonnegative values. The measure may lack meaning for data which can assume both positive and negative values.
 
 -   The [variance-to-mean ratio][variance-to-mean-ratio] is also known as the **index of dispersion**, **dispersion index**, **coefficient of dispersion**, **relative variance**, and the [**Fano factor**][fano-factor].
 
diff --git a/lib/node_modules/@stdlib/stats/incr/vmr/README.md b/lib/node_modules/@stdlib/stats/incr/vmr/README.md
@@ -124,7 +124,7 @@ D = accumulator();
 
     Accordingly, one can use the [variance-to-mean ratio][variance-to-mean-ratio] to assess whether observed data can be modeled as a Poisson process. When observed data is "under-dispersed", observed data may be more regular than as would be the case for a Poisson process. When observed data is "over-dispersed", observed data may contain clusters (i.e., clumped, concentrated data).
 
--   The [variance-to-mean ratio][variance-to-mean-ratio] is typically computed on nonnegativevalues. The measure may lack meaning for data which can assume both positive and negative values.
+-   The [variance-to-mean ratio][variance-to-mean-ratio] is typically computed on nonnegative values. The measure may lack meaning for data which can assume both positive and negative values.
 
 -   The [variance-to-mean ratio][variance-to-mean-ratio] is also known as the **index of dispersion**, **dispersion index**, **coefficient of dispersion**, and **relative variance**.
 
diff --git a/lib/node_modules/@stdlib/stats/lowess/test/test.validate.js b/lib/node_modules/@stdlib/stats/lowess/test/test.validate.js
@@ -111,7 +111,7 @@ tape( 'the function returns an error if provided an `nsteps` option which is not
 	t.end();
 });
 
-tape( 'the function returns an error if provided a `delta` option which is not a nonnegativenumber', function test( t ) {
+tape( 'the function returns an error if provided a `delta` option which is not a nonnegative number', function test( t ) {
 	var values;
 	var err;
 	var i;
