chore: add missing value and revert test.quantile.js

---
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description: Results of running static analysis checks when committing changes.
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---

Author: saurabhraghuvanshii

lib/node_modules/@stdlib/stats/base/dists/levy/quantile/benchmark/benchmark.js

@@ -76,6 +76,8 @@ bench( pkg+':factory', function benchmark( b ) {
 	mu = 10.0;
 	c = 4.0;
 	myquantile = quantile.factory( mu, c );
+
+	len = 100;
 	p = new Float64Array( len );
 
 	for ( i = 0; i < len; i++ ) {
@@ -84,7 +86,7 @@ bench( pkg+':factory', function benchmark( b ) {
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		y = myquantile( p );
+		y = myquantile( p[ i % len ] );
 		if ( isnan( y ) ) {
 			b.fail( 'should not return NaN' );
 		}

lib/node_modules/@stdlib/stats/base/dists/levy/quantile/test/test.quantile.js

@@ -102,9 +102,15 @@ tape( 'the function evaluates the quantile function at `p` given positive `mu`',
 	c = positiveMean.c;
 	for ( i = 0; i < p.length; i++ ) {
 		y = quantile( p[i], mu[i], c[i] );
-		delta = abs( y - expected[ i ] );
-		tol = 20.0 * EPS * abs( expected[ i ] );
-		t.ok( delta <= tol, 'within tolerance. p: '+p[ i ]+'. mu: '+mu[i]+'. c: '+c[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+		if ( expected[i] !== null ) {
+			if ( y === expected[i] ) {
+				t.equal( y, expected[i], 'p: '+p[i]+', mu:'+mu[i]+', c: '+c[i]+', y: '+y+', expected: '+expected[i] );
+			} else {
+				delta = abs( y - expected[ i ] );
+				tol = 20.0 * EPS * abs( expected[ i ] );
+				t.ok( delta <= tol, 'within tolerance. p: '+p[ i ]+'. mu: '+mu[i]+'. c: '+c[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			}
+		}
 	}
 	t.end();
 });
@@ -125,9 +131,15 @@ tape( 'the function evaluates the quantile function at `p` given negative `mu`',
 	c = negativeMean.c;
 	for ( i = 0; i < p.length; i++ ) {
 		y = quantile( p[i], mu[i], c[i] );
-		delta = abs( y - expected[ i ] );
-		tol = 350.0 * EPS * abs( expected[ i ] );
-		t.ok( delta <= tol, 'within tolerance. p: '+p[ i ]+'. mu: '+mu[i]+'. c: '+c[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+		if ( expected[i] !== null ) {
+			if ( y === expected[i] ) {
+				t.equal( y, expected[i], 'p: '+p[i]+', mu:'+mu[i]+', c: '+c[i]+', y: '+y+', expected: '+expected[i] );
+			} else {
+				delta = abs( y - expected[ i ] );
+				tol = 350.0 * EPS * abs( expected[ i ] );
+				t.ok( delta <= tol, 'within tolerance. p: '+p[ i ]+'. mu: '+mu[i]+'. c: '+c[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			}
+		}
 	}
 	t.end();
 });
@@ -148,9 +160,15 @@ tape( 'the function evaluates the quantile function at `p` given large variance
 	c = largeVariance.c;
 	for ( i = 0; i < p.length; i++ ) {
 		y = quantile( p[i], mu[i], c[i] );
-		delta = abs( y - expected[ i ] );
-		tol = 30.0 * EPS * abs( expected[ i ] );
-		t.ok( delta <= tol, 'within tolerance. p: '+p[ i ]+'. mu: '+mu[i]+'. c: '+c[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+		if ( expected[i] !== null ) {
+			if ( y === expected[i] ) {
+				t.equal( y, expected[i], 'p: '+p[i]+', mu:'+mu[i]+', c: '+c[i]+', y: '+y+', expected: '+expected[i] );
+			} else {
+				delta = abs( y - expected[ i ] );
+				tol = 30.0 * EPS * abs( expected[ i ] );
+				t.ok( delta <= tol, 'within tolerance. p: '+p[ i ]+'. mu: '+mu[i]+'. c: '+c[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			}
+		}
 	}
 	t.end();
 });