chore: minor clean-up

---
type: pre_commit_static_analysis_report
description: Results of running static analysis checks when committing changes.
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    status: passed
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---

Author: Planeshifter

lib/node_modules/@stdlib/stats/base/dists/beta/mean/README.md

@@ -185,7 +185,7 @@ The function accepts the following arguments:
 -   **beta**: `[in] double` second shape parameter.
 
 ```c
-double stdlib_base_dists_beta_mean( const double a, const double b );
+double stdlib_base_dists_beta_mean( const double alpha, const double beta );
 ```
 
 </section>
@@ -222,7 +222,7 @@ int main( void ) {
     double beta;
     double y;
     int i;
-    
+
     for ( i = 0; i < 25; i++ ) {
         alpha = random_uniform( 0.0, 10.0 );
         beta = random_uniform( 0.0, 10.0 );

lib/node_modules/@stdlib/stats/base/dists/beta/mean/benchmark/c/benchmark.c

@@ -101,8 +101,8 @@ static double benchmark( void ) {
 	int i;
 
 	for ( i = 0; i < 100; i++ ) {
-		alpha[ i ] = random_uniform( -20.0, 0.0 );
-		beta[ i ] = random_uniform( alpha[ i ], alpha[ i ]+40.0 );
+		alpha[ i ] = random_uniform( STDLIB_CONSTANT_FLOAT64_EPS, 20.0 );
+		beta[ i ] = random_uniform( STDLIB_CONSTANT_FLOAT64_EPS, 20.0 );
 	}
 
 	t = tic();

lib/node_modules/@stdlib/stats/base/dists/beta/mean/test/test.js

@@ -112,7 +112,7 @@ tape( 'the function returns the mean of a beta distribution', function test( t )
 			t.equal( y, expected[i], 'alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
 		} else {
 			delta = abs( y - expected[ i ] );
-			tol = 15.0 * EPS * abs( expected[ i ] );
+			tol = 1.0 * EPS * abs( expected[ i ] );
 			t.ok( delta <= tol, 'within tolerance. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}

lib/node_modules/@stdlib/stats/base/dists/beta/mean/test/test.native.js

@@ -121,7 +121,7 @@ tape( 'the function returns the mean of a beta distribution', opts, function tes
 			t.equal( y, expected[i], 'alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
 		} else {
 			delta = abs( y - expected[ i ] );
-			tol = 15.0 * EPS * abs( expected[ i ] );
+			tol = 1.0 * EPS * abs( expected[ i ] );
 			t.ok( delta <= tol, 'within tolerance. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}

lib/node_modules/@stdlib/stats/base/dists/discrete-uniform/skewness/lib/native.js

@@ -28,6 +28,7 @@ var addon = require( './../src/addon.node' );
 /**
 * Returns the skewness of a discrete uniform distribution.
 *
+* @private
 * @param {integer} a - minimum support
 * @param {integer} b - maximum support
 * @returns {number} skewness

lib/node_modules/@stdlib/stats/base/dists/kumaraswamy/logcdf/lib/native.js

@@ -28,6 +28,7 @@ var addon = require( './../src/addon.node' );
 /**
 * Evaluates the natural logarithm of the cumulative density function (CDF) for a uniform distribution with minimum support `a` and maximum support `b` at a value `x`.
 *
+* @private
 * @param {number} x - input value
 * @param {PositiveNumber} a - first shape parameter
 * @param {PositiveNumber} b - second shape parameter