chore: minor clean-up

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

Author: Planeshifter

lib/node_modules/@stdlib/random/base/uniform/docs/types/index.d.ts

@@ -121,7 +121,7 @@ interface BinaryFunction extends PRNG {
 }
 
 /**
-* Interface for generating pseudorandom numbers drawn from an uniform distribution.
+* Interface for generating pseudorandom numbers drawn from a uniform distribution.
 */
 interface Random extends PRNG {
 	/**

lib/node_modules/@stdlib/stats/base/dists/exponential/stdev/README.md

@@ -195,7 +195,7 @@ int main( void ) {
     for ( i = 0; i < 25; i++ ) {
         lambda = random_uniform( 0.0, 20.0 );
         y = stdlib_base_dists_exponential_stdev( lambda );
-        printf( "λ: %lf, Standard Deviation[X;λ]: %lf\n", lambda, y );
+        printf( "λ: %lf, SD(X;λ): %lf\n", lambda, y );
     }
 }
 ```

lib/node_modules/@stdlib/stats/base/dists/exponential/stdev/examples/c/example.c

@@ -33,6 +33,6 @@ int main( void ) {
 	for ( i = 0; i < 25; i++ ) {
 		lambda = random_uniform( 0.0, 20.0 );
 		y = stdlib_base_dists_exponential_stdev( lambda );
-		printf( "λ: %lf, Standard Deviation[X;λ]: %lf\n", lambda, y );
+		printf( "λ: %lf, SD(X;λ): %lf\n", lambda, y );
 	}
 }

lib/node_modules/@stdlib/stats/base/dists/exponential/stdev/test/test.native.js

@@ -84,7 +84,7 @@ tape( 'the function returns the standard deviation of an exponential distributio
 			t.equal( y, expected[i], 'lambda: '+lambda[i]+', y: '+y+', expected: '+expected[i] );
 		} else {
 			delta = abs( y - expected[ i ] );
-			tol = 2.0 * EPS * abs( expected[ i ] );
+			tol = 1.0 * EPS * abs( expected[ i ] );
 			t.ok( delta <= tol, 'within tolerance. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[i]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}

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

@@ -213,7 +213,7 @@ int main( void ) {
         mu = random_uniform( 0.0, 10.0 ) - 5.0;
         s = random_uniform( 0.0, 20.0 );
         y = stdlib_base_dists_logistic_mean( mu, s );
-        printf( "µ: %lf, s: %lf, Mean(X;µ,s): %lf\n", mu, s, y );
+        printf( "µ: %lf, s: %lf, E(X;µ,s): %lf\n", mu, s, y );
     }
 }
 ```

lib/node_modules/@stdlib/stats/base/dists/logistic/mean/examples/c/example.c

@@ -35,6 +35,6 @@ int main( void ) {
 		mu = random_uniform( 0.0, 10.0 ) - 5.0;
 		s = random_uniform( 0.0, 20.0 );
 		y = stdlib_base_dists_logistic_mean( mu, s );
-		printf( "µ: %lf, s: %lf, Mean(X;µ,s): %lf\n", mu, s, y );
+		printf( "µ: %lf, s: %lf, E(X;µ,s): %lf\n", mu, s, y );
 	}
 }

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

@@ -198,7 +198,7 @@ int main( void ) {
     for ( i = 0; i < 25; i++ ) {
         sigma = random_uniform( 0.0, 20.0 );
         y = stdlib_base_dists_rayleigh_mean( sigma );
-        printf( "σ: %lf, Mean(X;σ): %lf\n", sigma, y );
+        printf( "σ: %lf, E(X;σ): %lf\n", sigma, y );
     }
 }
 ```

lib/node_modules/@stdlib/stats/base/dists/rayleigh/mean/examples/c/example.c

@@ -33,6 +33,6 @@ int main( void ) {
 	for ( i = 0; i < 25; i++ ) {
 		sigma = random_uniform( 0.0, 20.0 );
 		y = stdlib_base_dists_rayleigh_mean( sigma );
-		printf( "σ: %lf, Mean(X;σ): %lf\n", sigma, y );
+		printf( "σ: %lf, E(X;σ): %lf\n", sigma, y );
 	}
 }

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

@@ -24,9 +24,7 @@ var resolve = require( 'path' ).resolve;
 var tape = require( 'tape' );
 var tryRequire = require( '@stdlib/utils/try-require' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
-var abs = require( '@stdlib/math/base/special/abs' );
 var NINF = require( '@stdlib/constants/float64/ninf' );
-var EPS = require( '@stdlib/constants/float64/eps' );
 
 
 // VARIABLES //
@@ -68,25 +66,17 @@ tape( 'if provided a scale `sigma` that is not a nonnegative number, the functio
 	t.end();
 });
 
-tape( 'the function returns the mean of a Rayleigh distribution', opts, function test( t ) {
+tape( 'the function returns the expected value of a Rayleigh distribution', function test( t ) {
 	var expected;
-	var delta;
 	var sigma;
-	var tol;
 	var i;
 	var y;
 
 	expected = data.expected;
 	sigma = data.sigma;
 	for ( i = 0; i < expected.length; i++ ) {
 		y = mean( sigma[i] );
-		if ( y === expected[i] ) {
-			t.equal( y, expected[i], ', sigma: '+sigma[i]+', y: '+y+', expected: '+expected[i] );
-		} else {
-			delta = abs( y - expected[ i ] );
-			tol = 1.0 * EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. sigma: '+sigma[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
-		}
+		t.equal( y, expected[i], 'sigma: '+sigma[i]+', y: '+y+', expected: '+expected[i] );
 	}
 	t.end();
 });

lib/node_modules/@stdlib/stats/base/dists/uniform/entropy/README.md

@@ -161,7 +161,7 @@ for ( i = 0; i < 10; i++ ) {
 
 #### stdlib_base_dists_uniform_entropy( a, b )
 
-Evaluates the [entropy][entropy] of a [uniform][uniform-distribution] distribution with `a` the minimum support and `b` the maximum support.
+Evaluates the [entropy][entropy] of a [uniform][uniform-distribution] distribution with minimum support `a` and maximum support `b`.
 
 ```c
 double out = stdlib_base_dists_uniform_entropy( 2.0, 8.0 );