refactor: collapse exact-match guard in stats/base/dists/wald tests

Propagates the test-assertion simplification from eeaf7c4 ("refactor:
reduce number of arithmetic operations") to sibling Wald distribution
packages (pdf, variance, skewness). Replaces the redundant
`if ( y === expected[i] ) { t.strictEqual( y, ... ) } else { t.ok(
isAlmostSameValue( y, expected[i], TOL ) ) }` block with an unconditional
`t.strictEqual( isAlmostSameValue( y, expected[i], TOL ), true, ... )`,
since `isAlmostSameValue` already accepts exact matches.

Author: claude

lib/node_modules/@stdlib/stats/base/dists/wald/pdf/test/test.factory.js

@@ -256,11 +256,7 @@ tape( 'the created function evaluates the pdf for `x` given parameters `mu` and
 	for ( i = 0; i < x.length; i++ ) {
 		pdf = factory( mu[i], lambda[i] );
 		y = pdf( x[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', mu: '+mu[i]+', lambda: '+lambda[i]+', y: '+y+', expected: '+expected[i] );
-		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 1500 ), 'within tolerance. x: '+x[ i ]+'. mu: '+mu[i]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
-		}
+		t.strictEqual( isAlmostSameValue( y, expected[i], 1500 ), true, 'within tolerance. x: '+x[ i ]+'. mu: '+mu[i]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
 	}
 	t.end();
 });

lib/node_modules/@stdlib/stats/base/dists/wald/pdf/test/test.native.js

@@ -217,11 +217,7 @@ tape( 'the function evaluates the pdf for `x` given parameters `mu` and `lambda`
 	lambda = data.lambda;
 	for ( i = 0; i < x.length; i++ ) {
 		y = pdf( x[i], mu[i], lambda[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', mu:'+mu[i]+', lambda: '+lambda[i]+', y: '+y+', expected: '+expected[i] );
-		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 1500 ), 'within tolerance. x: '+x[ i ]+'. mu: '+mu[i]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
-		}
+		t.strictEqual( isAlmostSameValue( y, expected[i], 1500 ), true, 'within tolerance. x: '+x[ i ]+'. mu: '+mu[i]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
 	}
 	t.end();
 });

lib/node_modules/@stdlib/stats/base/dists/wald/pdf/test/test.pdf.js

@@ -208,11 +208,7 @@ tape( 'the function evaluates the pdf for `x` given parameters `mu` and `lambda`
 	lambda = data.lambda;
 	for ( i = 0; i < x.length; i++ ) {
 		y = pdf( x[i], mu[i], lambda[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', mu:'+mu[i]+', lambda: '+lambda[i]+', y: '+y+', expected: '+expected[i] );
-		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 1500 ), 'within tolerance. x: '+x[ i ]+'. mu: '+mu[i]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
-		}
+		t.strictEqual( isAlmostSameValue( y, expected[i], 1500 ), true, 'within tolerance. x: '+x[ i ]+'. mu: '+mu[i]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
 	}
 	t.end();
 });

lib/node_modules/@stdlib/stats/base/dists/wald/skewness/test/test.js

@@ -111,11 +111,7 @@ tape( 'the function returns the skewness of a Wald distribution', function test(
 	lambda = data.lambda;
 	for ( i = 0; i < mu.length; i++ ) {
 		y = skewness( mu[i], lambda[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'mu:'+mu[i]+', lambda: '+lambda[i]+', y: '+y+', expected: '+expected[i] );
-		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 20 ), 'within tolerance. mu: '+mu[i]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
-		}
+		t.strictEqual( isAlmostSameValue( y, expected[i], 20 ), true, 'within tolerance. mu: '+mu[i]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
 	}
 	t.end();
 });

lib/node_modules/@stdlib/stats/base/dists/wald/skewness/test/test.native.js

@@ -120,11 +120,7 @@ tape( 'the function returns the skewness of a Wald distribution', opts, function
 	lambda = data.lambda;
 	for ( i = 0; i < mu.length; i++ ) {
 		y = skewness( mu[i], lambda[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'mu:'+mu[i]+', lambda: '+lambda[i]+', y: '+y+', expected: '+expected[i] );
-		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 20 ), 'within tolerance. mu: '+mu[i]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
-		}
+		t.strictEqual( isAlmostSameValue( y, expected[i], 20 ), true, 'within tolerance. mu: '+mu[i]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
 	}
 	t.end();
 });

lib/node_modules/@stdlib/stats/base/dists/wald/variance/test/test.js

@@ -111,11 +111,7 @@ tape( 'the function returns the variance of a Wald distribution', function test(
 	lambda = data.lambda;
 	for ( i = 0; i < mu.length; i++ ) {
 		y = variance( mu[i], lambda[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'mu:'+mu[i]+', lambda: '+lambda[i]+', y: '+y+', expected: '+expected[i] );
-		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 20 ), 'within tolerance. mu: '+mu[i]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
-		}
+		t.strictEqual( isAlmostSameValue( y, expected[i], 20 ), true, 'within tolerance. mu: '+mu[i]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
 	}
 	t.end();
 });

lib/node_modules/@stdlib/stats/base/dists/wald/variance/test/test.native.js

@@ -120,11 +120,7 @@ tape( 'the function returns the variance of a Wald distribution', opts, function
 	lambda = data.lambda;
 	for ( i = 0; i < mu.length; i++ ) {
 		y = variance( mu[i], lambda[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'mu:'+mu[i]+', lambda: '+lambda[i]+', y: '+y+', expected: '+expected[i] );
-		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 20 ), 'within tolerance. mu: '+mu[i]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
-		}
+		t.strictEqual( isAlmostSameValue( y, expected[i], 20 ), true, 'within tolerance. mu: '+mu[i]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
 	}
 	t.end();
 });