diff --git a/lib/node_modules/@stdlib/blas/base/gscal/lib/main.js b/lib/node_modules/@stdlib/blas/base/gscal/lib/main.js
index 1285863e6fa1..0db33040c8ef 100644
--- a/lib/node_modules/@stdlib/blas/base/gscal/lib/main.js
+++ b/lib/node_modules/@stdlib/blas/base/gscal/lib/main.js
@@ -32,7 +32,7 @@ var ndarray = require( './ndarray.js' );
 * @param {PositiveInteger} N - number of indexed elements
 * @param {number} alpha - scalar constant
 * @param {NumericArray} x - input array
-* @param {PositiveInteger} stride -  stride length
+* @param {PositiveInteger} stride - stride length
 * @returns {NumericArray} input array
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/anglit/cdf/test/test.cdf.js b/lib/node_modules/@stdlib/stats/base/dists/anglit/cdf/test/test.cdf.js
index 5ad2f83751ab..b2e2f51df916 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/anglit/cdf/test/test.cdf.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/anglit/cdf/test/test.cdf.js
@@ -95,11 +95,11 @@ tape( 'the function evaluates the cdf for `x` given `mu` and `sigma`', function
 	sigma = data.sigma;
 
 	for ( i = 0; i < x.length; i++ ) {
-		y = cdf( x[i], mu[i], sigma[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', mu: '+mu[i]+', σ: '+sigma[i]+', y: '+y+', expected: '+expected[i] );
+		y = cdf( x[ i ], mu[ i ], sigma[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', mu: '+mu[ i ]+', σ: '+sigma[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 3 ), 'within tolerance. x: '+x[i]+'. mu: '+mu[i]+'. σ: '+sigma[i]+'. y: '+y+'. E: '+expected[i]+'.' );
+			t.ok( isAlmostSameValue( y, expected[ i ], 3 ), 'within tolerance. x: '+x[ i ]+'. mu: '+mu[ i ]+'. σ: '+sigma[ i ]+'. y: '+y+'. E: '+expected[ i ]+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/anglit/cdf/test/test.factory.js b/lib/node_modules/@stdlib/stats/base/dists/anglit/cdf/test/test.factory.js
index 420838e99b37..65e2813fbed4 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/anglit/cdf/test/test.factory.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/anglit/cdf/test/test.factory.js
@@ -130,12 +130,12 @@ tape( 'the created function evaluates the cdf for `x` given `mu` and `sigma`', f
 	sigma = data.sigma;
 
 	for ( i = 0; i < x.length; i++ ) {
-		cdf = factory( mu[i], sigma[i] );
-		y = cdf( x[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual(y, expected[i], 'x: '+x[i]+', mu: '+mu[i]+', σ: '+sigma[i]+', y: '+y+', expected: '+expected[i]);
+		cdf = factory( mu[ i ], sigma[ i ] );
+		y = cdf( x[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', mu: '+mu[ i ]+', σ: '+sigma[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 3 ), 'within tolerance. x: '+x[i]+'. mu: '+mu[i]+'. σ: '+sigma[i]+'. y: '+y+'. E: '+expected[i]+'.' );
+			t.ok( isAlmostSameValue( y, expected[ i ], 3 ), 'within tolerance. x: '+x[ i ]+'. mu: '+mu[ i ]+'. σ: '+sigma[ i ]+'. y: '+y+'. E: '+expected[ i ]+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/anglit/cdf/test/test.native.js b/lib/node_modules/@stdlib/stats/base/dists/anglit/cdf/test/test.native.js
index bf0bbe875a6c..60d93fc4a6e9 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/anglit/cdf/test/test.native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/anglit/cdf/test/test.native.js
@@ -104,11 +104,11 @@ tape( 'the function evaluates the cdf for `x` given `mu` and `sigma`', opts, fun
 	sigma = data.sigma;
 
 	for ( i = 0; i < x.length; i++ ) {
-		y = cdf( x[i], mu[i], sigma[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', mu: '+mu[i]+', σ: '+sigma[i]+', y: '+y+', expected: '+expected[i] );
+		y = cdf( x[ i ], mu[ i ], sigma[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', mu: '+mu[ i ]+', σ: '+sigma[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 3 ), 'within tolerance. x: '+x[i]+'. mu: '+mu[i]+'. σ: '+sigma[i]+'. y: '+y+'. E: '+expected[i]+'.' );
+			t.ok( isAlmostSameValue( y, expected[ i ], 3 ), 'within tolerance. x: '+x[ i ]+'. mu: '+mu[ i ]+'. σ: '+sigma[ i ]+'. y: '+y+'. E: '+expected[ i ]+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/anglit/entropy/test/test.js b/lib/node_modules/@stdlib/stats/base/dists/anglit/entropy/test/test.js
index eba19774fbab..2439ec8f71f3 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/anglit/entropy/test/test.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/anglit/entropy/test/test.js
@@ -72,11 +72,11 @@ tape( 'the function returns the differential entropy of an anglit distribution',
 	mu = data.mu;
 	sigma = data.sigma;
 	for ( i = 0; i < expected.length; i++ ) {
-		y = entropy( mu[i], sigma[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'mu: '+mu[i]+', sigma: '+sigma[i]+', y: '+y+', expected: '+expected[i] );
+		y = entropy( mu[ i ], sigma[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'mu: '+mu[ i ]+', sigma: '+sigma[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 5 ), 'within tolerance. mu: '+mu[i]+'. sigma: '+sigma[i]+'. y: '+y+'. E: '+expected[i]+'.' );
+			t.ok( isAlmostSameValue( y, expected[ i ], 5 ), 'within tolerance. mu: '+mu[ i ]+'. sigma: '+sigma[ i ]+'. y: '+y+'. E: '+expected[ i ]+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/anglit/entropy/test/test.native.js b/lib/node_modules/@stdlib/stats/base/dists/anglit/entropy/test/test.native.js
index 58963fd9096d..c88399e827bb 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/anglit/entropy/test/test.native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/anglit/entropy/test/test.native.js
@@ -81,11 +81,11 @@ tape( 'the function returns the differential entropy of an anglit distribution',
 	mu = data.mu;
 	sigma = data.sigma;
 	for ( i = 0; i < expected.length; i++ ) {
-		y = entropy( mu[i], sigma[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'mu: '+mu[i]+', sigma: '+sigma[i]+', y: '+y+', expected: '+expected[i] );
+		y = entropy( mu[ i ], sigma[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'mu: '+mu[ i ]+', sigma: '+sigma[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 5 ), 'within tolerance. mu: '+mu[i]+'. sigma: '+sigma[i]+'. y: '+y+'. E: '+expected[i]+'.' );
+			t.ok( isAlmostSameValue( y, expected[ i ], 5 ), 'within tolerance. mu: '+mu[ i ]+'. sigma: '+sigma[ i ]+'. y: '+y+'. E: '+expected[ i ]+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/anglit/mean/test/test.js b/lib/node_modules/@stdlib/stats/base/dists/anglit/mean/test/test.js
index 8351e0200b10..b7a92fec1d2a 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/anglit/mean/test/test.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/anglit/mean/test/test.js
@@ -87,8 +87,8 @@ tape( 'the function returns the expected value of an anglit distribution', funct
 	mu = data.mu;
 	sigma = data.sigma;
 	for ( i = 0; i < mu.length; i++ ) {
-		y = mean( mu[i], sigma[i] );
-		t.strictEqual( y, expected[i], 'returns expected value' );
+		y = mean( mu[ i ], sigma[ i ] );
+		t.strictEqual( y, expected[ i ], 'returns expected value' );
 	}
 	t.end();
 });
@@ -104,8 +104,8 @@ tape( 'the function returns the expected value for small `mu` values', function
 	mu = smallMu.mu;
 	sigma = smallMu.sigma;
 	for ( i = 0; i < mu.length; i++ ) {
-		y = mean( mu[i], sigma[i] );
-		t.strictEqual( y, expected[i], 'returns expected value' );
+		y = mean( mu[ i ], sigma[ i ] );
+		t.strictEqual( y, expected[ i ], 'returns expected value' );
 	}
 	t.end();
 });
@@ -121,8 +121,8 @@ tape( 'the function returns the expected value for large `mu` values', function
 	mu = largeMu.mu;
 	sigma = largeMu.sigma;
 	for ( i = 0; i < mu.length; i++ ) {
-		y = mean( mu[i], sigma[i] );
-		t.strictEqual( y, expected[i], 'returns expected value' );
+		y = mean( mu[ i ], sigma[ i ] );
+		t.strictEqual( y, expected[ i ], 'returns expected value' );
 	}
 	t.end();
 });
@@ -138,8 +138,8 @@ tape( 'the function returns the expected value for small `sigma` values', functi
 	mu = smallSigma.mu;
 	sigma = smallSigma.sigma;
 	for ( i = 0; i < mu.length; i++ ) {
-		y = mean( mu[i], sigma[i] );
-		t.strictEqual( y, expected[i], 'returns expected value' );
+		y = mean( mu[ i ], sigma[ i ] );
+		t.strictEqual( y, expected[ i ], 'returns expected value' );
 	}
 	t.end();
 });
@@ -155,8 +155,8 @@ tape( 'the function returns the expected value for large `sigma` values', functi
 	mu = largeSigma.mu;
 	sigma = largeSigma.sigma;
 	for ( i = 0; i < mu.length; i++ ) {
-		y = mean( mu[i], sigma[i] );
-		t.strictEqual( y, expected[i], 'returns expected value' );
+		y = mean( mu[ i ], sigma[ i ] );
+		t.strictEqual( y, expected[ i ], 'returns expected value' );
 	}
 	t.end();
 });
diff --git a/lib/node_modules/@stdlib/stats/base/dists/anglit/mean/test/test.native.js b/lib/node_modules/@stdlib/stats/base/dists/anglit/mean/test/test.native.js
index bdd84cd6c701..26ba65426d94 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/anglit/mean/test/test.native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/anglit/mean/test/test.native.js
@@ -95,8 +95,8 @@ tape( 'the function returns the expected value of an anglit distribution', opts,
 	mu = data.mu;
 	sigma = data.sigma;
 	for ( i = 0; i < mu.length; i++ ) {
-		y = mean( mu[i], sigma[i] );
-		t.strictEqual( y, expected[i], 'returns expected value' );
+		y = mean( mu[ i ], sigma[ i ] );
+		t.strictEqual( y, expected[ i ], 'returns expected value' );
 	}
 	t.end();
 });
@@ -112,8 +112,8 @@ tape( 'the function returns the expected value for small `mu` values', opts, fun
 	mu = smallMu.mu;
 	sigma = smallMu.sigma;
 	for ( i = 0; i < mu.length; i++ ) {
-		y = mean( mu[i], sigma[i] );
-		t.strictEqual( y, expected[i], 'returns expected value' );
+		y = mean( mu[ i ], sigma[ i ] );
+		t.strictEqual( y, expected[ i ], 'returns expected value' );
 	}
 	t.end();
 });
@@ -129,8 +129,8 @@ tape( 'the function returns the expected value for large `mu` values', opts, fun
 	mu = largeMu.mu;
 	sigma = largeMu.sigma;
 	for ( i = 0; i < mu.length; i++ ) {
-		y = mean( mu[i], sigma[i] );
-		t.strictEqual( y, expected[i], 'returns expected value' );
+		y = mean( mu[ i ], sigma[ i ] );
+		t.strictEqual( y, expected[ i ], 'returns expected value' );
 	}
 	t.end();
 });
@@ -146,8 +146,8 @@ tape( 'the function returns the expected value for small `sigma` values', opts,
 	mu = smallSigma.mu;
 	sigma = smallSigma.sigma;
 	for ( i = 0; i < mu.length; i++ ) {
-		y = mean( mu[i], sigma[i] );
-		t.strictEqual( y, expected[i], 'returns expected value' );
+		y = mean( mu[ i ], sigma[ i ] );
+		t.strictEqual( y, expected[ i ], 'returns expected value' );
 	}
 	t.end();
 });
@@ -163,8 +163,8 @@ tape( 'the function returns the expected value for large `sigma` values', opts,
 	mu = largeSigma.mu;
 	sigma = largeSigma.sigma;
 	for ( i = 0; i < mu.length; i++ ) {
-		y = mean( mu[i], sigma[i] );
-		t.strictEqual( y, expected[i], 'returns expected value' );
+		y = mean( mu[ i ], sigma[ i ] );
+		t.strictEqual( y, expected[ i ], 'returns expected value' );
 	}
 	t.end();
 });
diff --git a/lib/node_modules/@stdlib/stats/base/dists/anglit/median/test/test.js b/lib/node_modules/@stdlib/stats/base/dists/anglit/median/test/test.js
index ed4fb98d11d5..1320a94e94c2 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/anglit/median/test/test.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/anglit/median/test/test.js
@@ -83,8 +83,8 @@ tape( 'the function returns the median of an anglit distribution', function test
 	mu = data.mu;
 	sigma = data.sigma;
 	for ( i = 0; i < mu.length; i++ ) {
-		y = median( mu[i], sigma[i] );
-		t.strictEqual( y, expected[i], 'returns expected value' );
+		y = median( mu[ i ], sigma[ i ] );
+		t.strictEqual( y, expected[ i ], 'returns expected value' );
 	}
 	t.end();
 });
diff --git a/lib/node_modules/@stdlib/stats/base/dists/anglit/median/test/test.native.js b/lib/node_modules/@stdlib/stats/base/dists/anglit/median/test/test.native.js
index f75b43d567cd..6a0e37cad7cd 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/anglit/median/test/test.native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/anglit/median/test/test.native.js
@@ -92,8 +92,8 @@ tape( 'the function returns the median of an anglit distribution', opts, functio
 	mu = data.mu;
 	sigma = data.sigma;
 	for ( i = 0; i < mu.length; i++ ) {
-		y = median( mu[i], sigma[i] );
-		t.strictEqual( y, expected[i], 'returns expected value' );
+		y = median( mu[ i ], sigma[ i ] );
+		t.strictEqual( y, expected[ i ], 'returns expected value' );
 	}
 	t.end();
 });
diff --git a/lib/node_modules/@stdlib/stats/base/dists/anglit/mode/test/test.js b/lib/node_modules/@stdlib/stats/base/dists/anglit/mode/test/test.js
index a7c264c87b75..bb4c012918c5 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/anglit/mode/test/test.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/anglit/mode/test/test.js
@@ -83,8 +83,8 @@ tape( 'the function returns the mode of an anglit distribution', function test(
 	mu = data.mu;
 	sigma = data.sigma;
 	for ( i = 0; i < mu.length; i++ ) {
-		y = mode( mu[i], sigma[i] );
-		t.strictEqual( y, expected[i], 'returns expected value' );
+		y = mode( mu[ i ], sigma[ i ] );
+		t.strictEqual( y, expected[ i ], 'returns expected value' );
 	}
 	t.end();
 });
diff --git a/lib/node_modules/@stdlib/stats/base/dists/anglit/mode/test/test.native.js b/lib/node_modules/@stdlib/stats/base/dists/anglit/mode/test/test.native.js
index 8beb6f881162..cc531a07af13 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/anglit/mode/test/test.native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/anglit/mode/test/test.native.js
@@ -95,8 +95,8 @@ tape( 'the function returns the mode of an anglit distribution', opts, function
 	mu = data.mu;
 	sigma = data.sigma;
 	for ( i = 0; i < mu.length; i++ ) {
-		y = mode( mu[i], sigma[i] );
-		t.strictEqual( y, expected[i], 'returns expected value' );
+		y = mode( mu[ i ], sigma[ i ] );
+		t.strictEqual( y, expected[ i ], 'returns expected value' );
 	}
 	t.end();
 });
diff --git a/lib/node_modules/@stdlib/stats/base/dists/anglit/quantile/test/test.factory.js b/lib/node_modules/@stdlib/stats/base/dists/anglit/quantile/test/test.factory.js
index 5d6aa2009793..630b1f632bea 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/anglit/quantile/test/test.factory.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/anglit/quantile/test/test.factory.js
@@ -140,12 +140,12 @@ tape( 'the created function evaluates the quantile function at `p` given positiv
 	mu = positiveMu.mu;
 	sigma = positiveMu.sigma;
 	for ( i = 0; i < p.length; i++ ) {
-		quantile = factory( mu[i], sigma[i] );
-		y = quantile( p[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'p: '+p[i]+', mu: '+mu[i]+', sigma: '+sigma[i]+', y: '+y+', expected: '+expected[i] );
+		quantile = factory( mu[ i ], sigma[ i ] );
+		y = quantile( p[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'p: '+p[ i ]+', mu: '+mu[ i ]+', sigma: '+sigma[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 5 ), 'within tolerance. p: '+p[ i ]+'. mu: '+mu[i]+'. sigma: '+sigma[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
+			t.ok( isAlmostSameValue( y, expected[ i ], 5 ), 'within tolerance. p: '+p[ i ]+'. mu: '+mu[ i ]+'. sigma: '+sigma[ i ]+'. y: '+y+'. E: '+expected[ i ]+'.' );
 		}
 	}
 	t.end();
@@ -165,12 +165,12 @@ tape( 'the created function evaluates the quantile function at `p` given negativ
 	mu = negativeMu.mu;
 	sigma = negativeMu.sigma;
 	for ( i = 0; i < p.length; i++ ) {
-		quantile = factory( mu[i], sigma[i] );
-		y = quantile( p[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'p: '+p[i]+', mu: '+mu[i]+', sigma: '+sigma[i]+', y: '+y+', expected: '+expected[i] );
+		quantile = factory( mu[ i ], sigma[ i ] );
+		y = quantile( p[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'p: '+p[ i ]+', mu: '+mu[ i ]+', sigma: '+sigma[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 80 ), 'within tolerance. p: '+p[ i ]+'. mu: '+mu[i]+'. sigma: '+sigma[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
+			t.ok( isAlmostSameValue( y, expected[ i ], 80 ), 'within tolerance. p: '+p[ i ]+'. mu: '+mu[ i ]+'. sigma: '+sigma[ i ]+'. y: '+y+'. E: '+expected[ i ]+'.' );
 		}
 	}
 	t.end();
@@ -190,12 +190,12 @@ tape( 'the created function evaluates the quantile function at `p` given large `
 	mu = largeSigma.mu;
 	sigma = largeSigma.sigma;
 	for ( i = 0; i < p.length; i++ ) {
-		quantile = factory( mu[i], sigma[i] );
-		y = quantile( p[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'p: '+p[i]+', mu: '+mu[i]+', sigma: '+sigma[i]+', y: '+y+', expected: '+expected[i] );
+		quantile = factory( mu[ i ], sigma[ i ] );
+		y = quantile( p[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'p: '+p[ i ]+', mu: '+mu[ i ]+', sigma: '+sigma[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 500 ), 'within tolerance. p: '+p[ i ]+'. mu: '+mu[i]+'. sigma: '+sigma[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
+			t.ok( isAlmostSameValue( y, expected[ i ], 500 ), 'within tolerance. p: '+p[ i ]+'. mu: '+mu[ i ]+'. sigma: '+sigma[ i ]+'. y: '+y+'. E: '+expected[ i ]+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/anglit/quantile/test/test.native.js b/lib/node_modules/@stdlib/stats/base/dists/anglit/quantile/test/test.native.js
index a268017c324a..18a4f9e572ef 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/anglit/quantile/test/test.native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/anglit/quantile/test/test.native.js
@@ -125,11 +125,11 @@ tape( 'the function evaluates the quantile function at `p` given positive `mu`',
 	mu = positiveMu.mu;
 	sigma = positiveMu.sigma;
 	for ( i = 0; i < p.length; i++ ) {
-		y = quantile( p[i], mu[i], sigma[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'p: '+p[i]+', mu: '+mu[i]+', sigma: '+sigma[i]+', y: '+y+', expected: '+expected[i] );
+		y = quantile( p[ i ], mu[ i ], sigma[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'p: '+p[ i ]+', mu: '+mu[ i ]+', sigma: '+sigma[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 5 ), 'within tolerance. p: '+p[ i ]+'. mu: '+mu[i]+'. sigma: '+sigma[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
+			t.ok( isAlmostSameValue( y, expected[ i ], 5 ), 'within tolerance. p: '+p[ i ]+'. mu: '+mu[ i ]+'. sigma: '+sigma[ i ]+'. y: '+y+'. E: '+expected[ i ]+'.' );
 		}
 	}
 	t.end();
@@ -148,11 +148,11 @@ tape( 'the function evaluates the quantile function at `p` given negative `mu`',
 	mu = negativeMu.mu;
 	sigma = negativeMu.sigma;
 	for ( i = 0; i < p.length; i++ ) {
-		y = quantile( p[i], mu[i], sigma[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'p: '+p[i]+', mu: '+mu[i]+', sigma: '+sigma[i]+', y: '+y+', expected: '+expected[i] );
+		y = quantile( p[ i ], mu[ i ], sigma[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'p: '+p[ i ]+', mu: '+mu[ i ]+', sigma: '+sigma[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 80 ), 'within tolerance. p: '+p[ i ]+'. mu: '+mu[i]+'. sigma: '+sigma[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
+			t.ok( isAlmostSameValue( y, expected[ i ], 80 ), 'within tolerance. p: '+p[ i ]+'. mu: '+mu[ i ]+'. sigma: '+sigma[ i ]+'. y: '+y+'. E: '+expected[ i ]+'.' );
 		}
 	}
 	t.end();
@@ -171,11 +171,11 @@ tape( 'the function evaluates the quantile function at `p` given large `sigma`',
 	mu = largeSigma.mu;
 	sigma = largeSigma.sigma;
 	for ( i = 0; i < p.length; i++ ) {
-		y = quantile( p[i], mu[i], sigma[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'p: '+p[i]+', mu: '+mu[i]+', sigma: '+sigma[i]+', y: '+y+', expected: '+expected[i] );
+		y = quantile( p[ i ], mu[ i ], sigma[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'p: '+p[ i ]+', mu: '+mu[ i ]+', sigma: '+sigma[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 500 ), 'within tolerance. p: '+p[ i ]+'. mu: '+mu[i]+'. sigma: '+sigma[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
+			t.ok( isAlmostSameValue( y, expected[ i ], 500 ), 'within tolerance. p: '+p[ i ]+'. mu: '+mu[ i ]+'. sigma: '+sigma[ i ]+'. y: '+y+'. E: '+expected[ i ]+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/anglit/quantile/test/test.quantile.js b/lib/node_modules/@stdlib/stats/base/dists/anglit/quantile/test/test.quantile.js
index 2d6eaef387de..4721ef21e403 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/anglit/quantile/test/test.quantile.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/anglit/quantile/test/test.quantile.js
@@ -116,11 +116,11 @@ tape( 'the function evaluates the quantile function at `p` given positive `mu`',
 	mu = positiveMu.mu;
 	sigma = positiveMu.sigma;
 	for ( i = 0; i < p.length; i++ ) {
-		y = quantile( p[i], mu[i], sigma[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'p: '+p[i]+', mu: '+mu[i]+', sigma: '+sigma[i]+', y: '+y+', expected: '+expected[i] );
+		y = quantile( p[ i ], mu[ i ], sigma[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'p: '+p[ i ]+', mu: '+mu[ i ]+', sigma: '+sigma[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 5 ), 'within tolerance. p: '+p[ i ]+'. mu: '+mu[i]+'. sigma: '+sigma[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
+			t.ok( isAlmostSameValue( y, expected[ i ], 5 ), 'within tolerance. p: '+p[ i ]+'. mu: '+mu[ i ]+'. sigma: '+sigma[ i ]+'. y: '+y+'. E: '+expected[ i ]+'.' );
 		}
 	}
 	t.end();
@@ -139,11 +139,11 @@ tape( 'the function evaluates the quantile function at `p` given negative `mu`',
 	mu = negativeMu.mu;
 	sigma = negativeMu.sigma;
 	for ( i = 0; i < p.length; i++ ) {
-		y = quantile( p[i], mu[i], sigma[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'p: '+p[i]+', mu: '+mu[i]+', sigma: '+sigma[i]+', y: '+y+', expected: '+expected[i] );
+		y = quantile( p[ i ], mu[ i ], sigma[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'p: '+p[ i ]+', mu: '+mu[ i ]+', sigma: '+sigma[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 80 ), 'within tolerance. p: '+p[ i ]+'. mu: '+mu[i]+'. sigma: '+sigma[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
+			t.ok( isAlmostSameValue( y, expected[ i ], 80 ), 'within tolerance. p: '+p[ i ]+'. mu: '+mu[ i ]+'. sigma: '+sigma[ i ]+'. y: '+y+'. E: '+expected[ i ]+'.' );
 		}
 	}
 	t.end();
@@ -162,11 +162,11 @@ tape( 'the function evaluates the quantile function at `p` given large `sigma`',
 	mu = largeSigma.mu;
 	sigma = largeSigma.sigma;
 	for ( i = 0; i < p.length; i++ ) {
-		y = quantile( p[i], mu[i], sigma[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'p: '+p[i]+', mu: '+mu[i]+', sigma: '+sigma[i]+', y: '+y+', expected: '+expected[i] );
+		y = quantile( p[ i ], mu[ i ], sigma[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'p: '+p[ i ]+', mu: '+mu[ i ]+', sigma: '+sigma[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 500 ), 'within tolerance. p: '+p[ i ]+'. mu: '+mu[i]+'. sigma: '+sigma[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
+			t.ok( isAlmostSameValue( y, expected[ i ], 500 ), 'within tolerance. p: '+p[ i ]+'. mu: '+mu[ i ]+'. sigma: '+sigma[ i ]+'. y: '+y+'. E: '+expected[ i ]+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/anglit/skewness/test/test.js b/lib/node_modules/@stdlib/stats/base/dists/anglit/skewness/test/test.js
index ba29ffa2920b..43ca33fcddc1 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/anglit/skewness/test/test.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/anglit/skewness/test/test.js
@@ -83,8 +83,8 @@ tape( 'the function returns the skewness of an anglit distribution', function te
 	mu = data.mu;
 	sigma = data.sigma;
 	for ( i = 0; i < mu.length; i++ ) {
-		y = skewness( mu[i], sigma[i] );
-		t.strictEqual( y, expected[i], 'returns expected value' );
+		y = skewness( mu[ i ], sigma[ i ] );
+		t.strictEqual( y, expected[ i ], 'returns expected value' );
 	}
 	t.end();
 });
diff --git a/lib/node_modules/@stdlib/stats/base/dists/anglit/skewness/test/test.native.js b/lib/node_modules/@stdlib/stats/base/dists/anglit/skewness/test/test.native.js
index c51884ee9501..dce8b23ca3a6 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/anglit/skewness/test/test.native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/anglit/skewness/test/test.native.js
@@ -92,8 +92,8 @@ tape( 'the function returns the skewness of an anglit distribution', opts, funct
 	mu = data.mu;
 	sigma = data.sigma;
 	for ( i = 0; i < mu.length; i++ ) {
-		y = skewness( mu[i], sigma[i] );
-		t.strictEqual( y, expected[i], 'returns expected value' );
+		y = skewness( mu[ i ], sigma[ i ] );
+		t.strictEqual( y, expected[ i ], 'returns expected value' );
 	}
 	t.end();
 });
diff --git a/lib/node_modules/@stdlib/stats/base/dists/anglit/stdev/test/test.js b/lib/node_modules/@stdlib/stats/base/dists/anglit/stdev/test/test.js
index 82f22b191bf1..df8e7798be5d 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/anglit/stdev/test/test.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/anglit/stdev/test/test.js
@@ -86,8 +86,8 @@ tape( 'the function returns the standard deviation of an anglit distribution', f
 	mu = data.mu;
 	sigma = data.sigma;
 	for ( i = 0; i < mu.length; i++ ) {
-		y = stdev( mu[i], sigma[i] );
-		t.strictEqual( isAlmostSameValue( y, expected[i], 1 ), true, 'returns expected value' );
+		y = stdev( mu[ i ], sigma[ i ] );
+		t.strictEqual( isAlmostSameValue( y, expected[ i ], 1 ), true, 'returns expected value' );
 	}
 	t.end();
 });
diff --git a/lib/node_modules/@stdlib/stats/base/dists/anglit/stdev/test/test.native.js b/lib/node_modules/@stdlib/stats/base/dists/anglit/stdev/test/test.native.js
index 2ec5dcec5e84..eaa5801979ea 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/anglit/stdev/test/test.native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/anglit/stdev/test/test.native.js
@@ -93,8 +93,8 @@ tape( 'the function returns the standard deviation of an anglit distribution', o
 	mu = data.mu;
 	sigma = data.sigma;
 	for ( i = 0; i < mu.length; i++ ) {
-		y = stdev( mu[i], sigma[i] );
-		t.strictEqual( isAlmostSameValue( y, expected[i], 1 ), true, 'returns expected value' );
+		y = stdev( mu[ i ], sigma[ i ] );
+		t.strictEqual( isAlmostSameValue( y, expected[ i ], 1 ), true, 'returns expected value' );
 	}
 	t.end();
 });
diff --git a/lib/node_modules/@stdlib/stats/base/dists/anglit/variance/test/test.js b/lib/node_modules/@stdlib/stats/base/dists/anglit/variance/test/test.js
index 1ba8774042e2..e77de7c7de85 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/anglit/variance/test/test.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/anglit/variance/test/test.js
@@ -86,8 +86,8 @@ tape( 'the function returns the variance of an anglit distribution', function te
 	mu = data.mu;
 	sigma = data.sigma;
 	for ( i = 0; i < expected.length; i++ ) {
-		y = variance( mu[i], sigma[i] );
-		t.strictEqual( isAlmostSameValue( y, expected[i], 2 ), true, 'returns expected value' );
+		y = variance( mu[ i ], sigma[ i ] );
+		t.strictEqual( isAlmostSameValue( y, expected[ i ], 2 ), true, 'returns expected value' );
 	}
 	t.end();
 });
diff --git a/lib/node_modules/@stdlib/stats/base/dists/anglit/variance/test/test.native.js b/lib/node_modules/@stdlib/stats/base/dists/anglit/variance/test/test.native.js
index 87a781fb2fe1..0625a035a6cd 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/anglit/variance/test/test.native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/anglit/variance/test/test.native.js
@@ -95,8 +95,8 @@ tape( 'the function returns the variance of an anglit distribution', opts, funct
 	mu = data.mu;
 	sigma = data.sigma;
 	for ( i = 0; i < expected.length; i++ ) {
-		y = variance( mu[i], sigma[i] );
-		t.strictEqual( isAlmostSameValue( y, expected[i], 2 ), true, 'returns expected value' );
+		y = variance( mu[ i ], sigma[ i ] );
+		t.strictEqual( isAlmostSameValue( y, expected[ i ], 2 ), true, 'returns expected value' );
 	}
 	t.end();
 });
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/cdf/README.md b/lib/node_modules/@stdlib/stats/base/dists/invgamma/cdf/README.md
index d216dd66a7d7..3be9e3c29f98 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/cdf/README.md
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/cdf/README.md
@@ -55,7 +55,7 @@ var cdf = require( '@stdlib/stats/base/dists/invgamma/cdf' );
 
 #### cdf( x, alpha, beta )
 
-Evaluates the [cumulative distribution function][cdf] (CDF) for an [inverse gamma][inverse-gamma] distribution with parameters `alpha` (shape parameter) and `beta` (rate parameter).
+Evaluates the [cumulative distribution function][cdf] (CDF) for an [inverse gamma][inverse-gamma] distribution with parameters `alpha` (shape parameter) and `beta` (scale parameter).
 
 ```javascript
 var y = cdf( 2.0, 1.0, 1.0 );
@@ -103,7 +103,7 @@ var y = cdf( 2.0, 0.5, -1.0 );
 
 #### cdf.factory( alpha, beta )
 
-Returns a function for evaluating the [cumulative distribution function][cdf] for an [inverse gamma][inverse-gamma] distribution with parameters `alpha` (shape parameter) and `beta` (rate parameter).
+Returns a function for evaluating the [cumulative distribution function][cdf] for an [inverse gamma][inverse-gamma] distribution with parameters `alpha` (shape parameter) and `beta` (scale parameter).
 
 ```javascript
 var mycdf = cdf.factory( 0.5, 0.1 );
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/cdf/include/stdlib/stats/base/dists/invgamma/cdf.h b/lib/node_modules/@stdlib/stats/base/dists/invgamma/cdf/include/stdlib/stats/base/dists/invgamma/cdf.h
index b5e88b4cf1e5..24dee8b45bb6 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/cdf/include/stdlib/stats/base/dists/invgamma/cdf.h
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/cdf/include/stdlib/stats/base/dists/invgamma/cdf.h
@@ -24,7 +24,7 @@ extern "C" {
 #endif
 
 /**
-* Evaluates the cumulative distribution function (CDF) for an inverse gamma distribution.
+* Evaluates the cumulative distribution function (CDF) for an inverse gamma distribution with shape parameter `alpha` and scale parameter `beta` at a value `x`.
 */
 double stdlib_base_dists_invgamma_cdf( const double x, const double alpha, const double beta );
 
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/cdf/test/fixtures/julia/runner.jl b/lib/node_modules/@stdlib/stats/base/dists/invgamma/cdf/test/fixtures/julia/runner.jl
index aacf7a8ed6c6..34e88f0610e7 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/cdf/test/fixtures/julia/runner.jl
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/cdf/test/fixtures/julia/runner.jl
@@ -28,7 +28,7 @@ Generate fixture data and write to file.
 
 * `x`: input value
 * `alpha`: shape parameter
-* `beta`: rate parameter
+* `beta`: scale parameter
 * `name::AbstractString`: output filename
 
 # Examples
@@ -76,7 +76,7 @@ alpha = ( rand( 1000 ) .* 10.0 ) .+ 10.0;
 beta = rand( 1000 ) .* 10.0;
 gen( x, alpha, beta, "large_shape.json" );
 
-# Large rate parameter:
+# Large scale parameter:
 x = rand( 1000 ) .* 5.0;
 alpha = rand( 1000 ) .* 10.0;
 beta = ( rand( 1000 ) .* 10.0 ) .+ 10.0;
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/cdf/test/test.cdf.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/cdf/test/test.cdf.js
index 8ca914428bd4..bfafbb9fbad7 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/cdf/test/test.cdf.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/cdf/test/test.cdf.js
@@ -117,13 +117,13 @@ tape( 'the function evaluates the cdf for `x` given large `alpha` and `beta`', f
 	alpha = bothLarge.alpha;
 	beta = bothLarge.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		y = cdf( x[i], alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha:'+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = cdf( x[ i ], alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha:'+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 950.0 * EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
@@ -144,19 +144,19 @@ tape( 'the function evaluates the cdf for `x` given large shape parameter `alpha
 	alpha = largeShape.alpha;
 	beta = largeShape.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		y = cdf( x[i], alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha:'+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = cdf( x[ i ], alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha:'+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 400.0 * EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
 });
 
-tape( 'the function evaluates the cdf for `x` given large rate parameter `beta`', function test( t ) {
+tape( 'the function evaluates the cdf for `x` given large scale parameter `beta`', function test( t ) {
 	var expected;
 	var delta;
 	var alpha;
@@ -171,13 +171,13 @@ tape( 'the function evaluates the cdf for `x` given large rate parameter `beta`'
 	alpha = largeRate.alpha;
 	beta = largeRate.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		y = cdf( x[i], alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha:'+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = cdf( x[ i ], alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha:'+alpha[ i ]+', beta: '+beta[ 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. x: '+x[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/cdf/test/test.factory.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/cdf/test/test.factory.js
index 8d70032cbb58..a3df648e23de 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/cdf/test/test.factory.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/cdf/test/test.factory.js
@@ -177,14 +177,14 @@ tape( 'the created function evaluates the cdf for `x` given large `alpha` and `b
 	alpha = bothLarge.alpha;
 	beta = bothLarge.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		cdf = factory( alpha[i], beta[i] );
-		y = cdf( x[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha:'+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		cdf = factory( alpha[ i ], beta[ i ] );
+		y = cdf( x[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha:'+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 950.0 * EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
@@ -206,20 +206,20 @@ tape( 'the created function evaluates the cdf for `x` given large shape paramete
 	alpha = largeShape.alpha;
 	beta = largeShape.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		cdf = factory( alpha[i], beta[i] );
-		y = cdf( x[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha:'+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		cdf = factory( alpha[ i ], beta[ i ] );
+		y = cdf( x[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha:'+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 400.0 * EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
 });
 
-tape( 'the created function evaluates the cdf for `x` given large rate parameter `beta`', function test( t ) {
+tape( 'the created function evaluates the cdf for `x` given large scale parameter `beta`', function test( t ) {
 	var expected;
 	var delta;
 	var alpha;
@@ -235,14 +235,14 @@ tape( 'the created function evaluates the cdf for `x` given large rate parameter
 	alpha = largeRate.alpha;
 	beta = largeRate.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		cdf = factory( alpha[i], beta[i] );
-		y = cdf( x[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha:'+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		cdf = factory( alpha[ i ], beta[ i ] );
+		y = cdf( x[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha:'+alpha[ i ]+', beta: '+beta[ 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. x: '+x[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/cdf/test/test.native.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/cdf/test/test.native.js
index 1f146435a2e9..bb770095d0d3 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/cdf/test/test.native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/cdf/test/test.native.js
@@ -130,13 +130,13 @@ tape( 'the function evaluates the cdf for `x` given large `alpha`', opts, functi
 	alpha = largeShape.alpha;
 	beta = largeShape.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		y = cdf( x[i], alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = cdf( x[ i ], alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha: '+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 1e-11 * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. x: '+x[i]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
@@ -157,13 +157,13 @@ tape( 'the function evaluates the cdf for `x` given large `beta`', opts, functio
 	alpha = largeRate.alpha;
 	beta = largeRate.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		y = cdf( x[i], alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = cdf( x[ i ], alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha: '+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 1e-11 * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. x: '+x[i]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
@@ -184,13 +184,13 @@ tape( 'the function evaluates the cdf for `x` given large `alpha` and `beta`', o
 	alpha = bothLarge.alpha;
 	beta = bothLarge.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		y = cdf( x[i], alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = cdf( x[ i ], alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha: '+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 1e-11 * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. x: '+x[i]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/ctor/README.md b/lib/node_modules/@stdlib/stats/base/dists/invgamma/ctor/README.md
index c351c381fd34..2557e850760e 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/ctor/README.md
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/ctor/README.md
@@ -51,7 +51,7 @@ var mode = invgamma.mode;
 // returns 0.5
 ```
 
-By default, `alpha = 1.0` and `beta = 1.0`. To create a distribution having a different `alpha` (shape parameter) and `beta` (rate parameter), provide the corresponding arguments.
+By default, `alpha = 1.0` and `beta = 1.0`. To create a distribution having a different `alpha` (shape parameter) and `beta` (scale parameter), provide the corresponding arguments.
 
 ```javascript
 var invgamma = new InvGamma( 2.0, 4.0 );
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/ctor/test/test.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/ctor/test/test.js
index a3860e06e772..d8752467c267 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/ctor/test/test.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/ctor/test/test.js
@@ -64,7 +64,7 @@ tape( 'the function throws an error if provided an `alpha` argument which is not
 	];
 
 	for ( i = 0; i < values.length; i++ ) {
-		t.throws( badValue( values[i] ), TypeError, 'throws an error when provided '+values[i] );
+		t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided '+values[ i ] );
 	}
 	t.end();
 
@@ -95,7 +95,7 @@ tape( 'the function throws an error if provided a `beta` argument which is not a
 	];
 
 	for ( i = 0; i < values.length; i++ ) {
-		t.throws( badValue( values[i] ), TypeError, 'throws an error when provided '+values[i] );
+		t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided '+values[ i ] );
 	}
 	t.end();
 
@@ -173,7 +173,7 @@ tape( 'the created distribution throws an error if one attempts to set `alpha` t
 	];
 
 	for ( i = 0; i < values.length; i++ ) {
-		t.throws( badValue( values[i] ), TypeError, 'throws an error when provided '+values[i] );
+		t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided '+values[ i ] );
 	}
 	t.end();
 
@@ -217,7 +217,7 @@ tape( 'the created distribution throws an error if one attempts to set `beta` to
 	];
 
 	for ( i = 0; i < values.length; i++ ) {
-		t.throws( badValue( values[i] ), TypeError, 'throws an error when provided '+values[i] );
+		t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided '+values[ i ] );
 	}
 	t.end();
 
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/docs/types/index.d.ts b/lib/node_modules/@stdlib/stats/base/dists/invgamma/docs/types/index.d.ts
index 340f3cdeb996..f435e6c33ee9 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/docs/types/index.d.ts
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/docs/types/index.d.ts
@@ -74,7 +74,7 @@ interface Namespace {
 	* -   If `alpha <= 0` or `beta <= 0`, the function returns `NaN`.
 	*
 	* @param alpha - shape parameter
-	* @param beta - rate parameter
+	* @param beta - scale parameter
 	* @returns entropy
 	*
 	* @example
@@ -115,7 +115,7 @@ interface Namespace {
 	* -   If `alpha <= 0` or `beta <= 0`, the function returns `NaN`.
 	*
 	* @param alpha - shape parameter
-	* @param beta - rate parameter
+	* @param beta - scale parameter
 	* @returns kurtosis
 	*
 	* @example
@@ -178,7 +178,7 @@ interface Namespace {
 	* -   If `alpha <= 0` or `beta <= 0`, the function returns `NaN`.
 	*
 	* @param alpha - shape parameter
-	* @param beta - rate parameter
+	* @param beta - scale parameter
 	* @returns expected value
 	*
 	* @example
@@ -219,7 +219,7 @@ interface Namespace {
 	* -   If `alpha <= 0` or `beta <= 0`, the function returns `NaN`.
 	*
 	* @param alpha - shape parameter
-	* @param beta - rate parameter
+	* @param beta - scale parameter
 	* @returns mode
 	*
 	* @example
@@ -257,7 +257,7 @@ interface Namespace {
 	*
 	* @param x - input value
 	* @param alpha - shape parameter
-	* @param beta - rate parameter
+	* @param beta - scale parameter
 	* @returns evaluated PDF
 	*
 	* @example
@@ -299,7 +299,7 @@ interface Namespace {
 	* -   If `alpha <= 0` or `beta <= 0`, the function returns `NaN`.
 	*
 	* @param alpha - shape parameter
-	* @param beta - rate parameter
+	* @param beta - scale parameter
 	* @returns skewness
 	*
 	* @example
@@ -340,7 +340,7 @@ interface Namespace {
 	* -   If `alpha <= 0` or `beta <= 0`, the function returns `NaN`.
 	*
 	* @param alpha - shape parameter
-	* @param beta - rate parameter
+	* @param beta - scale parameter
 	* @returns standard deviation
 	*
 	* @example
@@ -381,7 +381,7 @@ interface Namespace {
 	* -   If `alpha <= 0` or `beta <= 0`, the function returns `NaN`.
 	*
 	* @param alpha - shape parameter
-	* @param beta - rate parameter
+	* @param beta - scale parameter
 	* @returns variance
 	*
 	* @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/README.md b/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/README.md
index 9c303c82d853..5ab978d22075 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/README.md
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/README.md
@@ -41,7 +41,7 @@ h\left( X \right) = \alpha \!+\!\ln(\beta \, \Gamma (\alpha ))\!-\!(1\!+\!\alpha
 
 <!-- </equation> -->
 
-where `α > 0` is the shape parameter, `β > 0` is the rate parameter, `Γ` and denotes the [gamma][gamma-function] and `Ψ` the [digamma][digamma] function.
+where `α > 0` is the shape parameter, `β > 0` is the scale parameter, `Γ` and denotes the [gamma][gamma-function] and `Ψ` the [digamma][digamma] function.
 
 </section>
 
@@ -59,7 +59,7 @@ var entropy = require( '@stdlib/stats/base/dists/invgamma/entropy' );
 
 #### entropy( alpha, beta )
 
-Returns the [differential entropy][entropy] of an [inverse gamma][invgamma-distribution] distribution with shape parameter `alpha` and rate parameter `beta` (in [nats][nats]).
+Returns the [differential entropy][entropy] of an [inverse gamma][invgamma-distribution] distribution with shape parameter `alpha` and scale parameter `beta` (in [nats][nats]).
 
 ```javascript
 var v = entropy( 1.0, 1.0 );
@@ -179,7 +179,7 @@ double out = stdlib_base_dists_invgamma_entropy( 1.0, 1.0 );
 The function accepts the following arguments:
 
 -   **alpha**: `[in] double` shape parameter.
--   **beta**: `[in] double` rate parameter.
+-   **beta**: `[in] double` scale parameter.
 
 ```c
 double stdlib_base_dists_invgamma_entropy( const double alpha, const double beta );
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/docs/types/index.d.ts b/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/docs/types/index.d.ts
index e3d925be459f..44dce6ec5384 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/docs/types/index.d.ts
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/docs/types/index.d.ts
@@ -26,7 +26,7 @@
 * -   If `alpha <= 0` or `beta <= 0`, the function returns `NaN`.
 *
 * @param alpha - shape parameter
-* @param beta - rate parameter
+* @param beta - scale parameter
 * @returns entropy
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/lib/main.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/lib/main.js
index de917ea50151..d34314944070 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/lib/main.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/lib/main.js
@@ -32,7 +32,7 @@ var ln = require( '@stdlib/math/base/special/ln' );
 * Returns the differential entropy of an inverse gamma distribution.
 *
 * @param {PositiveNumber} alpha - shape parameter
-* @param {PositiveNumber} beta - rate parameter
+* @param {PositiveNumber} beta - scale parameter
 * @returns {number} entropy
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/lib/native.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/lib/native.js
index 67aeb85e0905..57310912edea 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/lib/native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/lib/native.js
@@ -30,7 +30,7 @@ var addon = require( './../src/addon.node' );
 *
 * @private
 * @param {PositiveNumber} alpha - shape parameter
-* @param {PositiveNumber} beta - rate parameter
+* @param {PositiveNumber} beta - scale parameter
 * @returns {number} entropy
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/src/main.c b/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/src/main.c
index 6a8733054b94..3dac5662c2d4 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/src/main.c
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/src/main.c
@@ -25,7 +25,7 @@
 * Returns the differential entropy of an inverse gamma distribution.
 *
 * @param alpha    shape parameter
-* @param beta     rate parameter
+* @param beta     scale parameter
 * @return         entropy
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/test/fixtures/julia/runner.jl b/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/test/fixtures/julia/runner.jl
index be54c18a9e2a..da8d077afc8a 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/test/fixtures/julia/runner.jl
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/test/fixtures/julia/runner.jl
@@ -27,7 +27,7 @@ Generate fixture data and write to file.
 # Arguments
 
 * `alpha`: shape parameter
-* `beta`: rate parameter
+* `beta`: scale parameter
 * `name::AbstractString`: output filename
 
 # Examples
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/test/test.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/test/test.js
index b8764411d5e9..73af74fca059 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/test/test.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/test/test.js
@@ -106,13 +106,13 @@ tape( 'the function returns the differential entropy of an inverse gamma distrib
 	alpha = data.alpha;
 	beta = data.beta;
 	for ( i = 0; i < expected.length; i++ ) {
-		y = entropy( alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = entropy( alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'alpha: '+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 1e-10 * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/test/test.native.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/test/test.native.js
index 0bdebbfa2cbd..0934dbe7ff43 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/test/test.native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/entropy/test/test.native.js
@@ -115,13 +115,13 @@ tape( 'the function returns the differential entropy of an inverse gamma distrib
 	alpha = data.alpha;
 	beta = data.beta;
 	for ( i = 0; i < expected.length; i++ ) {
-		y = entropy( alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = entropy( alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'alpha: '+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 1e-10 * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/README.md b/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/README.md
index dee5e2de5cd2..333720eaf7bf 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/README.md
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/README.md
@@ -26,7 +26,7 @@ limitations under the License.
 
 <section class="intro">
 
-The [excess kurtosis][kurtosis] for an [inverse gamma][invgamma-distribution] random variable with shape parameter `α` and rate parameter `β` is
+The [excess kurtosis][kurtosis] for an [inverse gamma][invgamma-distribution] random variable with shape parameter `α` and scale parameter `β` is
 
 <!-- <equation class="equation" label="eq:invgamma_kurtosis" align="center" raw="\operatorname{Kurt}\left( X \right) = \frac {30\,\alpha -66}{(\alpha -3)(\alpha -4)}" alt="Excess kurtosis for an inverse gamma distribution."> -->
 
@@ -59,7 +59,7 @@ var kurtosis = require( '@stdlib/stats/base/dists/invgamma/kurtosis' );
 
 #### kurtosis( alpha, beta )
 
-Returns the [excess kurtosis][kurtosis] of an [inverse gamma][invgamma-distribution] distribution with parameters `alpha` (shape parameter) and `beta` (rate parameter).
+Returns the [excess kurtosis][kurtosis] of an [inverse gamma][invgamma-distribution] distribution with parameters `alpha` (shape parameter) and `beta` (scale parameter).
 
 ```javascript
 var v = kurtosis( 7.0, 5.0 );
@@ -169,7 +169,7 @@ logEachMap( 'α: %0.4f, β: %0.4f, Kurt(X;α,β): %0.4f', alpha, beta, kurtosis
 
 #### stdlib_base_dists_invgamma_kurtosis( alpha, beta )
 
-Evaluates the [excess kurtosis][kurtosis] of an [inverse gamma][invgamma-distribution] distribution with parameters `alpha` (shape parameter) and `beta` (rate parameter).
+Evaluates the [excess kurtosis][kurtosis] of an [inverse gamma][invgamma-distribution] distribution with parameters `alpha` (shape parameter) and `beta` (scale parameter).
 
 ```c
 double out = stdlib_base_dists_invgamma_kurtosis( 6.0, 1.0 );
@@ -179,7 +179,7 @@ double out = stdlib_base_dists_invgamma_kurtosis( 6.0, 1.0 );
 The function accepts the following arguments:
 
 -   **alpha**: `[in] double` shape parameter.
--   **beta**: `[in] double` rate parameter.
+-   **beta**: `[in] double` scale parameter.
 
 ```c
 double stdlib_base_dists_invgamma_kurtosis( const double alpha, const double beta );
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/docs/types/index.d.ts b/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/docs/types/index.d.ts
index 6a518235070b..bd8b62bd1797 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/docs/types/index.d.ts
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/docs/types/index.d.ts
@@ -26,7 +26,7 @@
 * -   If `alpha <= 0` or `beta <= 0`, the function returns `NaN`.
 *
 * @param alpha - shape parameter
-* @param beta - rate parameter
+* @param beta - scale parameter
 * @returns kurtosis
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/lib/main.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/lib/main.js
index 19aacc56627b..09861a37e2f7 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/lib/main.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/lib/main.js
@@ -29,7 +29,7 @@ var isnan = require( '@stdlib/math/base/assert/is-nan' );
 * Returns the excess kurtosis of an inverse gamma distribution.
 *
 * @param {PositiveNumber} alpha - shape parameter
-* @param {PositiveNumber} beta - rate parameter
+* @param {PositiveNumber} beta - scale parameter
 * @returns {PositiveNumber} excess kurtosis
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/lib/native.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/lib/native.js
index 2761ad350116..6f742032d6ed 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/lib/native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/lib/native.js
@@ -30,7 +30,7 @@ var addon = require( './../src/addon.node' );
 *
 * @private
 * @param {PositiveNumber} alpha - shape parameter
-* @param {PositiveNumber} beta - rate parameter
+* @param {PositiveNumber} beta - scale parameter
 * @returns {PositiveNumber} excess kurtosis
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/src/main.c b/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/src/main.c
index c5a26ef2de24..b7f72e8d4f87 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/src/main.c
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/src/main.c
@@ -23,7 +23,7 @@
 * Returns the excess kurtosis of an inverse gamma distribution.
 *
 * @param alpha    shape parameter
-* @param beta     rate parameter
+* @param beta     scale parameter
 * @return         kurtosis
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/test/fixtures/julia/runner.jl b/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/test/fixtures/julia/runner.jl
index b3534ace4965..58f8eaadbe02 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/test/fixtures/julia/runner.jl
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/test/fixtures/julia/runner.jl
@@ -27,7 +27,7 @@ Generate fixture data and write to file.
 # Arguments
 
 * `alpha`: shape parameter
-* `beta`: rate parameter
+* `beta`: scale parameter
 * `name::AbstractString`: output filename
 
 # Examples
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/test/test.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/test/test.js
index cb3bdb3a1512..2abe74d5057c 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/test/test.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/test/test.js
@@ -116,13 +116,13 @@ tape( 'the function returns the excess kurtosis of an inverse gamma distribution
 	alpha = data.alpha;
 	beta = data.beta;
 	for ( i = 0; i < expected.length; i++ ) {
-		y = kurtosis( alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = kurtosis( alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'alpha: '+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 2.0 * 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+'.' );
+			t.ok( delta <= tol, 'within tolerance. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/test/test.native.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/test/test.native.js
index e628e6031c3d..35c7adba9b17 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/test/test.native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/kurtosis/test/test.native.js
@@ -125,13 +125,13 @@ tape( 'the function returns the excess kurtosis of an inverse gamma distribution
 	alpha = data.alpha;
 	beta = data.beta;
 	for ( i = 0; i < expected.length; i++ ) {
-		y = kurtosis( alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = kurtosis( alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'alpha: '+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 2.0 * 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+'.' );
+			t.ok( delta <= tol, 'within tolerance. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/logpdf/README.md b/lib/node_modules/@stdlib/stats/base/dists/invgamma/logpdf/README.md
index b8aff5dd17b1..24d05ac5b826 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/logpdf/README.md
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/logpdf/README.md
@@ -55,7 +55,7 @@ var logpdf = require( '@stdlib/stats/base/dists/invgamma/logpdf' );
 
 #### logpdf( x, alpha, beta )
 
-Evaluates the natural logarithm of the [probability density function][pdf] (PDF) for an [inverse gamma][inverse-gamma] distribution with parameters `alpha` (shape parameter) and `beta` (rate parameter).
+Evaluates the natural logarithm of the [probability density function][pdf] (PDF) for an [inverse gamma][inverse-gamma] distribution with parameters `alpha` (shape parameter) and `beta` (scale parameter).
 
 ```javascript
 var y = logpdf( 2.0, 0.5, 1.0 );
@@ -103,7 +103,7 @@ y = logpdf( 2.0, 1.0, -1.0 );
 
 #### logpdf.factory( alpha, beta )
 
-Returns a `function` for evaluating the natural logarithm of the [PDF][pdf] for an [inverse gamma][inverse-gamma] distribution with parameters `alpha` (shape parameter) and `beta` (rate parameter).
+Returns a `function` for evaluating the natural logarithm of the [PDF][pdf] for an [inverse gamma][inverse-gamma] distribution with parameters `alpha` (shape parameter) and `beta` (scale parameter).
 
 ```javascript
 var mylogPDF = logpdf.factory( 6.0, 7.0 );
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/logpdf/test/fixtures/julia/runner.jl b/lib/node_modules/@stdlib/stats/base/dists/invgamma/logpdf/test/fixtures/julia/runner.jl
index af249df51299..5b1b00089f2e 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/logpdf/test/fixtures/julia/runner.jl
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/logpdf/test/fixtures/julia/runner.jl
@@ -28,7 +28,7 @@ Generate fixture data and write to file.
 
 * `x`: input value
 * `alpha`: shape parameter
-* `beta`: rate parameter
+* `beta`: scale parameter
 * `name::AbstractString`: output filename
 
 # Examples
@@ -76,7 +76,7 @@ alpha = ( rand( 1000 ) .* 10.0 ) .+ 10.0;
 beta = rand( 1000 ) .* 10.0;
 gen( x, alpha, beta, "large_shape.json" );
 
-# Large rate parameter:
+# Large scale parameter:
 x = rand( 1000 ) .* 5.0;
 alpha = rand( 1000 ) .* 10.0;
 beta = ( rand( 1000 ) .* 10.0 ) .+ 10.0;
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/logpdf/test/test.factory.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/logpdf/test/test.factory.js
index 151d07edeb4f..eb283f76b58b 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/logpdf/test/test.factory.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/logpdf/test/test.factory.js
@@ -177,14 +177,14 @@ tape( 'the created function evaluates the logpdf for `x` given large `alpha` and
 	alpha = bothLarge.alpha;
 	beta = bothLarge.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		logpdf = factory( alpha[i], beta[i] );
-		y = logpdf( x[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha:'+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		logpdf = factory( alpha[ i ], beta[ i ] );
+		y = logpdf( x[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha:'+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
@@ -206,20 +206,20 @@ tape( 'the created function evaluates the logpdf for `x` given a large shape par
 	alpha = largeShape.alpha;
 	beta = largeShape.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		logpdf = factory( alpha[i], beta[i] );
-		y = logpdf( x[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha:'+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		logpdf = factory( alpha[ i ], beta[ i ] );
+		y = logpdf( x[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha:'+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
 });
 
-tape( 'the created function evaluates the logpdf for `x` given a large rate parameter `beta`', function test( t ) {
+tape( 'the created function evaluates the logpdf for `x` given a large scale parameter `beta`', function test( t ) {
 	var expected;
 	var logpdf;
 	var delta;
@@ -235,14 +235,14 @@ tape( 'the created function evaluates the logpdf for `x` given a large rate para
 	alpha = largeRate.alpha;
 	beta = largeRate.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		logpdf = factory( alpha[i], beta[i] );
-		y = logpdf( x[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha:'+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		logpdf = factory( alpha[ i ], beta[ i ] );
+		y = logpdf( x[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha:'+alpha[ i ]+', beta: '+beta[ 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. x: '+x[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/logpdf/test/test.logpdf.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/logpdf/test/test.logpdf.js
index 46699dc1c59a..86c70ad2e982 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/logpdf/test/test.logpdf.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/logpdf/test/test.logpdf.js
@@ -129,13 +129,13 @@ tape( 'the function evaluates the logpdf for `x` given large `alpha` and `beta`'
 	alpha = bothLarge.alpha;
 	beta = bothLarge.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		y = logpdf( x[i], alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = logpdf( x[ i ], alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha: '+alpha[ i ]+', beta: '+beta[ 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. x: '+x[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
@@ -156,19 +156,19 @@ tape( 'the function evaluates the logpdf for `x` given large shape parameter `al
 	alpha = largeShape.alpha;
 	beta = largeShape.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		y = logpdf( x[i], alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = logpdf( x[ i ], alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha: '+alpha[ i ]+', beta: '+beta[ 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. x: '+x[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
 });
 
-tape( 'the function evaluates the logpdf for `x` given large rate parameter `beta`', function test( t ) {
+tape( 'the function evaluates the logpdf for `x` given large scale parameter `beta`', function test( t ) {
 	var expected;
 	var delta;
 	var alpha;
@@ -183,13 +183,13 @@ tape( 'the function evaluates the logpdf for `x` given large rate parameter `bet
 	alpha = largeRate.alpha;
 	beta = largeRate.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		y = logpdf( x[i], alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = logpdf( x[ i ], alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha: '+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 5.0 * EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/logpdf/test/test.native.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/logpdf/test/test.native.js
index c8d89de06ea2..d88310b76834 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/logpdf/test/test.native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/logpdf/test/test.native.js
@@ -138,13 +138,13 @@ tape( 'the function evaluates the logpdf for `x` given large `alpha` and `beta`'
 	alpha = bothLarge.alpha;
 	beta = bothLarge.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		y = logpdf( x[i], alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = logpdf( x[ i ], alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha: '+alpha[ i ]+', beta: '+beta[ 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. x: '+x[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
@@ -165,19 +165,19 @@ tape( 'the function evaluates the logpdf for `x` given large shape parameter `al
 	alpha = largeShape.alpha;
 	beta = largeShape.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		y = logpdf( x[i], alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = logpdf( x[ i ], alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha: '+alpha[ i ]+', beta: '+beta[ 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. x: '+x[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
 });
 
-tape( 'the function evaluates the logpdf for `x` given large rate parameter `beta`', opts, function test( t ) {
+tape( 'the function evaluates the logpdf for `x` given large scale parameter `beta`', opts, function test( t ) {
 	var expected;
 	var delta;
 	var alpha;
@@ -192,13 +192,13 @@ tape( 'the function evaluates the logpdf for `x` given large rate parameter `bet
 	alpha = largeRate.alpha;
 	beta = largeRate.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		y = logpdf( x[i], alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = logpdf( x[ i ], alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha: '+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 5.0 * EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/README.md b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/README.md
index 3c15d10c7ffd..17ad34fa3f55 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/README.md
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/README.md
@@ -41,7 +41,7 @@ The [expected value][expected-value] for an [inverse-gamma][invgamma-distributio
 
 <!-- </equation> -->
 
-where `α > 0` is the shape parameter and `β > 0` is the rate parameter.
+where `α > 0` is the shape parameter and `β > 0` is the scale parameter.
 
 </section>
 
@@ -59,7 +59,7 @@ var mean = require( '@stdlib/stats/base/dists/invgamma/mean' );
 
 #### mean( alpha, beta )
 
-Returns the [expected value][expected-value] of an [inverse gamma][invgamma-distribution] distribution with parameters `alpha` (shape parameter) and `beta` (rate parameter).
+Returns the [expected value][expected-value] of an [inverse gamma][invgamma-distribution] distribution with parameters `alpha` (shape parameter) and `beta` (scale parameter).
 
 ```javascript
 var v = mean( 4.0, 12.0 );
@@ -166,7 +166,7 @@ logEachMap( 'α: %0.4f, β: %0.4f, E(X;α,β): %0.4f', alpha, beta, mean );
 
 #### stdlib_base_dists_invgamma_mean( alpha, beta )
 
-Evaluates the [expected value][expected-value] of an [inverse gamma][invgamma-distribution] distribution with parameters `alpha` (shape parameter) and `beta` (rate parameter).
+Evaluates the [expected value][expected-value] of an [inverse gamma][invgamma-distribution] distribution with parameters `alpha` (shape parameter) and `beta` (scale parameter).
 
 ```c
 double out = stdlib_base_dists_invgamma_mean( 4.0, 12.0 );
@@ -176,7 +176,7 @@ double out = stdlib_base_dists_invgamma_mean( 4.0, 12.0 );
 The function accepts the following arguments:
 
 -   **alpha**: `[in] double` shape parameter.
--   **beta**: `[in] double` rate parameter.
+-   **beta**: `[in] double` scale parameter.
 
 ```c
 double stdlib_base_dists_invgamma_mean( const double alpha, const double beta );
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/docs/types/index.d.ts b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/docs/types/index.d.ts
index 42a7025c225f..d31deedeb334 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/docs/types/index.d.ts
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/docs/types/index.d.ts
@@ -26,7 +26,7 @@
 * -   If `alpha <= 0` or `beta <= 0`, the function returns `NaN`.
 *
 * @param alpha - shape parameter
-* @param beta - rate parameter
+* @param beta - scale parameter
 * @returns expected value
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/lib/main.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/lib/main.js
index 4001c7d32410..7dcebf6e217e 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/lib/main.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/lib/main.js
@@ -29,7 +29,7 @@ var isnan = require( '@stdlib/math/base/assert/is-nan' );
 * Returns the expected value of an inverse gamma distribution.
 *
 * @param {PositiveNumber} alpha - shape parameter
-* @param {PositiveNumber} beta - rate parameter
+* @param {PositiveNumber} beta - scale parameter
 * @returns {PositiveNumber} expected value
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/lib/native.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/lib/native.js
index a059f1b2a8b0..9ed8c97ef0f9 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/lib/native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/lib/native.js
@@ -30,7 +30,7 @@ var addon = require( './../src/addon.node' );
 *
 * @private
 * @param {PositiveNumber} alpha - shape parameter
-* @param {PositiveNumber} beta - rate parameter
+* @param {PositiveNumber} beta - scale parameter
 * @returns {PositiveNumber} expected value
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/src/main.c b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/src/main.c
index 7386e82022f5..5d0d1c1575ec 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/src/main.c
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/src/main.c
@@ -22,7 +22,7 @@
 * Returns the expected value of an inverse gamma distribution.
 *
 * @param alpha    shape parameter
-* @param beta     rate parameter
+* @param beta     scale parameter
 * @return         expected value
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/test/fixtures/julia/runner.jl b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/test/fixtures/julia/runner.jl
index 6a8b3e33a588..af0db9ce43a9 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/test/fixtures/julia/runner.jl
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/test/fixtures/julia/runner.jl
@@ -27,7 +27,7 @@ Generate fixture data and write to file.
 # Arguments
 
 * `alpha`: shape parameter
-* `beta`: rate parameter
+* `beta`: scale parameter
 * `name::AbstractString`: output filename
 
 # Examples
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/test/test.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/test/test.js
index e2faede36b33..11405d3557ef 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/test/test.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/test/test.js
@@ -107,13 +107,13 @@ tape( 'the function returns the mean of an inverse gamma distribution', function
 	alpha = data.alpha;
 	beta = data.beta;
 	for ( i = 0; i < expected.length; i++ ) {
-		y = mean( alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = mean( alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'alpha: '+alpha[ i ]+', beta: '+beta[ 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. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/test/test.native.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/test/test.native.js
index 6db0aad8c6e7..7f480d73f894 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/test/test.native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mean/test/test.native.js
@@ -116,13 +116,13 @@ tape( 'the function returns the mean of an inverse gamma distribution', opts, fu
 	alpha = data.alpha;
 	beta = data.beta;
 	for ( i = 0; i < expected.length; i++ ) {
-		y = mean( alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = mean( alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'alpha: '+alpha[ i ]+', beta: '+beta[ 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. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/README.md b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/README.md
index 3a7327ad0a86..e293400a6de9 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/README.md
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/README.md
@@ -41,7 +41,7 @@ The [mode][mode] for an [inverse gamma][invgamma-distribution] random variable i
 
 <!-- </equation> -->
 
-where `α > 0` is the shape parameter and `β > 0` is the rate parameter.
+where `α > 0` is the shape parameter and `β > 0` is the scale parameter.
 
 </section>
 
@@ -59,7 +59,7 @@ var mode = require( '@stdlib/stats/base/dists/invgamma/mode' );
 
 #### mode( alpha, beta )
 
-Returns the [mode][mode] of an [inverse gamma][invgamma-distribution] distribution with parameters `alpha` (shape parameter) and `beta` (rate parameter).
+Returns the [mode][mode] of an [inverse gamma][invgamma-distribution] distribution with parameters `alpha` (shape parameter) and `beta` (scale parameter).
 
 ```javascript
 var v = mode( 1.0, 1.0 );
@@ -169,7 +169,7 @@ logEachMap( 'α: %0.4f, β: %0.4f, mode(X;α,β): %0.4f', alpha, beta, mode );
 
 #### stdlib_base_dists_invgamma_mode( alpha, beta )
 
-Evaluates the [mode][mode] of an [inverse gamma][invgamma-distribution] distribution with parameters `alpha` (shape parameter) and `beta` (rate parameter).
+Evaluates the [mode][mode] of an [inverse gamma][invgamma-distribution] distribution with parameters `alpha` (shape parameter) and `beta` (scale parameter).
 
 ```c
 double out = stdlib_base_dists_invgamma_mode( 1.0, 1.0 );
@@ -179,7 +179,7 @@ double out = stdlib_base_dists_invgamma_mode( 1.0, 1.0 );
 The function accepts the following arguments:
 
 -   **alpha**: `[in] double` shape parameter.
--   **beta**: `[in] double` rate parameter.
+-   **beta**: `[in] double` scale parameter.
 
 ```c
 double stdlib_base_dists_invgamma_mode( const double alpha, const double beta );
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/docs/types/index.d.ts b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/docs/types/index.d.ts
index 867f88337486..4e3603d26ed5 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/docs/types/index.d.ts
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/docs/types/index.d.ts
@@ -26,7 +26,7 @@
 * -   If `alpha <= 0` or `beta <= 0`, the function returns `NaN`.
 *
 * @param alpha - shape parameter
-* @param beta - rate parameter
+* @param beta - scale parameter
 * @returns mode
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/lib/main.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/lib/main.js
index 0cd4e8441e27..58b233e32432 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/lib/main.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/lib/main.js
@@ -29,7 +29,7 @@ var isnan = require( '@stdlib/math/base/assert/is-nan' );
 * Returns the mode of an inverse gamma distribution.
 *
 * @param {PositiveNumber} alpha - shape parameter
-* @param {PositiveNumber} beta - rate parameter
+* @param {PositiveNumber} beta - scale parameter
 * @returns {PositiveNumber} mode
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/lib/native.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/lib/native.js
index 6dfa57b3b115..6dde29e4df46 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/lib/native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/lib/native.js
@@ -30,7 +30,7 @@ var addon = require( './../src/addon.node' );
 *
 * @private
 * @param {PositiveNumber} alpha - shape parameter
-* @param {PositiveNumber} beta - rate parameter
+* @param {PositiveNumber} beta - scale parameter
 * @returns {PositiveNumber} mode
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/src/main.c b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/src/main.c
index c0f23a8a1ada..d40a1dc9d432 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/src/main.c
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/src/main.c
@@ -22,7 +22,7 @@
 * Returns the mode of an inverse gamma distribution.
 *
 * @param alpha    shape parameter
-* @param beta     rate parameter
+* @param beta     scale parameter
 * @return         mode
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/test/fixtures/julia/runner.jl b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/test/fixtures/julia/runner.jl
index c2eea8907a71..5ce89c384015 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/test/fixtures/julia/runner.jl
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/test/fixtures/julia/runner.jl
@@ -27,7 +27,7 @@ Generate fixture data and write to file.
 # Arguments
 
 * `alpha`: shape parameter
-* `beta`: rate parameter
+* `beta`: scale parameter
 * `name::AbstractString`: output filename
 
 # Examples
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/test/test.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/test/test.js
index 0b98b86df25f..23182b6e74ed 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/test/test.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/test/test.js
@@ -107,13 +107,13 @@ tape( 'the function returns the mode of an inverse gamma distribution', function
 	alpha = data.alpha;
 	beta = data.beta;
 	for ( i = 0; i < expected.length; i++ ) {
-		y = mode( alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = mode( alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'alpha: '+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 2.0 * 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+'.' );
+			t.ok( delta <= tol, 'within tolerance. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/test/test.native.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/test/test.native.js
index 9acd9d9a3c7c..b36db614b3d6 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/test/test.native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/mode/test/test.native.js
@@ -116,13 +116,13 @@ tape( 'the function returns the mode of an inverse gamma distribution', opts, fu
 	alpha = data.alpha;
 	beta = data.beta;
 	for ( i = 0; i < expected.length; i++ ) {
-		y = mode( alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = mode( alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'alpha: '+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 2.0 * 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+'.' );
+			t.ok( delta <= tol, 'within tolerance. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/README.md b/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/README.md
index e2a54bfff3bc..ca94b3a03792 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/README.md
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/README.md
@@ -55,7 +55,7 @@ var pdf = require( '@stdlib/stats/base/dists/invgamma/pdf' );
 
 #### pdf( x, alpha, beta )
 
-Evaluates the [probability density function][pdf] (PDF) for an [inverse gamma][inverse-gamma]  distribution with parameters `alpha` (shape parameter) and `beta` (rate parameter).
+Evaluates the [probability density function][pdf] (PDF) for an [inverse gamma][inverse-gamma]  distribution with parameters `alpha` (shape parameter) and `beta` (scale parameter).
 
 ```javascript
 var y = pdf( 2.0, 0.5, 1.0 );
@@ -103,7 +103,7 @@ y = pdf( 2.0, 1.0, -1.0 );
 
 #### pdf.factory( alpha, beta )
 
-Returns a `function` for evaluating the [PDF][pdf] of an [inverse gamma][inverse-gamma] distribution with parameters `alpha` (shape parameter) and `beta` (rate parameter).
+Returns a `function` for evaluating the [PDF][pdf] of an [inverse gamma][inverse-gamma] distribution with parameters `alpha` (shape parameter) and `beta` (scale parameter).
 
 ```javascript
 var myPDF = pdf.factory( 6.0, 7.0 );
@@ -169,7 +169,7 @@ logEachMap( 'x: %0.4f, α: %0.4f, β: %0.4f, f(x;α,β): %0.4f', x, alpha, beta,
 
 #### stdlib_base_dists_invgamma_pdf( x, alpha, beta )
 
-Evaluates the [probability density function][pdf] (PDF) for an [inverse gamma][inverse-gamma]  distribution with parameters `alpha` (shape parameter) and `beta` (rate parameter).
+Evaluates the [probability density function][pdf] (PDF) for an [inverse gamma][inverse-gamma]  distribution with parameters `alpha` (shape parameter) and `beta` (scale parameter).
 
 ```c
 double out = stdlib_base_dists_invgamma_pdf( 2.0, 0.5, 1.0 );
@@ -180,7 +180,7 @@ The function accepts the following arguments:
 
 -   **x**: `[in] double` input value.
 -   **alpha**: `[in] double` shape parameter.
--   **beta**: `[in] double` rate parameter.
+-   **beta**: `[in] double` scale parameter.
 
 ```c
 double stdlib_base_dists_invgamma_pdf( const double x, const double alpha, const double beta );
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/docs/types/index.d.ts b/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/docs/types/index.d.ts
index c34095118352..5cd47260ab0b 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/docs/types/index.d.ts
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/docs/types/index.d.ts
@@ -79,10 +79,10 @@ interface PDF {
 	( x: number, alpha: number, beta: number ): number;
 
 	/**
-	* Returns a function for evaluating the probability density function (PDF) for an inverse gamma distribution with shape parameter `alpha` and rate parameter `beta`.
+	* Returns a function for evaluating the probability density function (PDF) for an inverse gamma distribution with shape parameter `alpha` and scale parameter `beta`.
 	*
 	* @param alpha - shape parameter
-	* @param beta - rate parameter
+	* @param beta - scale parameter
 	* @returns PDF
 	*
 	* @example
@@ -102,7 +102,7 @@ interface PDF {
 *
 * @param x - input value
 * @param alpha - shape parameter
-* @param beta - rate parameter
+* @param beta - scale parameter
 * @returns evaluated PDF
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/include/stdlib/stats/base/dists/invgamma/pdf.h b/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/include/stdlib/stats/base/dists/invgamma/pdf.h
index 0c05ff820542..afdb167c929d 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/include/stdlib/stats/base/dists/invgamma/pdf.h
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/include/stdlib/stats/base/dists/invgamma/pdf.h
@@ -27,7 +27,7 @@ extern "C" {
 #endif
 
 /**
-* Evaluates the probability density function (PDF) for an inverse gamma distribution.
+* Evaluates the probability density function (PDF) for an inverse gamma distribution with shape parameter `alpha` and scale parameter `beta` at a value `x`.
 */
 double stdlib_base_dists_invgamma_pdf( const double x, const double alpha, const double beta );
 
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/test/fixtures/julia/runner.jl b/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/test/fixtures/julia/runner.jl
index 3055a6c47c88..de48ac68dc32 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/test/fixtures/julia/runner.jl
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/test/fixtures/julia/runner.jl
@@ -28,7 +28,7 @@ Generate fixture data and write to file.
 
 * `x`: input value
 * `alpha`: shape parameter
-* `beta`: rate parameter
+* `beta`: scale parameter
 * `name::AbstractString`: output filename
 
 # Examples
@@ -76,7 +76,7 @@ alpha = ( rand( 1000 ) .* 10.0 ) .+ 10.0;
 beta = rand( 1000 ) .* 10.0;
 gen( x, alpha, beta, "large_shape.json" );
 
-# Large rate parameter:
+# Large scale parameter:
 x = rand( 1000 ) .* 5.0;
 alpha = rand( 1000 ) .* 10.0;
 beta = ( rand( 1000 ) .* 10.0 ) .+ 10.0;
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/test/test.factory.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/test/test.factory.js
index ae0780777cdb..989feac989db 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/test/test.factory.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/test/test.factory.js
@@ -177,14 +177,14 @@ tape( 'the created function evaluates the pdf for `x` given large `alpha` and `b
 	alpha = bothLarge.alpha;
 	beta = bothLarge.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		pdf = factory( alpha[i], beta[i] );
-		y = pdf( x[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha:'+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		pdf = factory( alpha[ i ], beta[ i ] );
+		y = pdf( x[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha:'+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
@@ -206,20 +206,20 @@ tape( 'the created function evaluates the pdf for `x` given a large shape parame
 	alpha = largeShape.alpha;
 	beta = largeShape.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		pdf = factory( alpha[i], beta[i] );
-		y = pdf( x[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha:'+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		pdf = factory( alpha[ i ], beta[ i ] );
+		y = pdf( x[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha:'+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
 });
 
-tape( 'the created function evaluates the pdf for `x` given a large rate parameter `beta`', function test( t ) {
+tape( 'the created function evaluates the pdf for `x` given a large scale parameter `beta`', function test( t ) {
 	var expected;
 	var delta;
 	var alpha;
@@ -235,14 +235,14 @@ tape( 'the created function evaluates the pdf for `x` given a large rate paramet
 	alpha = largeRate.alpha;
 	beta = largeRate.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		pdf = factory( alpha[i], beta[i] );
-		y = pdf( x[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha:'+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		pdf = factory( alpha[ i ], beta[ i ] );
+		y = pdf( x[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha:'+alpha[ i ]+', beta: '+beta[ 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. x: '+x[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/test/test.native.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/test/test.native.js
index 2f236a2357a9..0191665d9512 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/test/test.native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/test/test.native.js
@@ -138,13 +138,13 @@ tape( 'the function evaluates the pdf for `x` given large `alpha` and `beta`', o
 	alpha = bothLarge.alpha;
 	beta = bothLarge.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		y = pdf( x[i], alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = pdf( x[ i ], alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha: '+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 130.0 * EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
@@ -165,19 +165,19 @@ tape( 'the function evaluates the pdf for `x` given large shape parameter `alpha
 	alpha = largeShape.alpha;
 	beta = largeShape.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		y = pdf( x[i], alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = pdf( x[ i ], alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha: '+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 70.0 * EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
 });
 
-tape( 'the function evaluates the pdf for `x` given large rate parameter `beta`', opts, function test( t ) {
+tape( 'the function evaluates the pdf for `x` given large scale parameter `beta`', opts, function test( t ) {
 	var expected;
 	var delta;
 	var alpha;
@@ -192,13 +192,13 @@ tape( 'the function evaluates the pdf for `x` given large rate parameter `beta`'
 	alpha = largeRate.alpha;
 	beta = largeRate.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		y = pdf( x[i], alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = pdf( x[ i ], alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha: '+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 75.0 * EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/test/test.pdf.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/test/test.pdf.js
index cdf3b7bd3372..f24b55a9a744 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/test/test.pdf.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/pdf/test/test.pdf.js
@@ -129,13 +129,13 @@ tape( 'the function evaluates the pdf for `x` given large `alpha` and `beta`', f
 	alpha = bothLarge.alpha;
 	beta = bothLarge.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		y = pdf( x[i], alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = pdf( x[ i ], alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha: '+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
@@ -156,19 +156,19 @@ tape( 'the function evaluates the pdf for `x` given large shape parameter `alpha
 	alpha = largeShape.alpha;
 	beta = largeShape.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		y = pdf( x[i], alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = pdf( x[ i ], alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha: '+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
 });
 
-tape( 'the function evaluates the pdf for `x` given large rate parameter `beta`', function test( t ) {
+tape( 'the function evaluates the pdf for `x` given large scale parameter `beta`', function test( t ) {
 	var expected;
 	var delta;
 	var alpha;
@@ -183,13 +183,13 @@ tape( 'the function evaluates the pdf for `x` given large rate parameter `beta`'
 	alpha = largeRate.alpha;
 	beta = largeRate.beta;
 	for ( i = 0; i < x.length; i++ ) {
-		y = pdf( x[i], alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = pdf( x[ i ], alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', alpha: '+alpha[ i ]+', beta: '+beta[ 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. x: '+x[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/docs/repl.txt b/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/docs/repl.txt
index e5781bfe7446..116437cfcf72 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/docs/repl.txt
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/docs/repl.txt
@@ -47,7 +47,7 @@
     > y = {{alias}}( 0.5, -1.0, 1.0 )
     NaN
 
-    // Non-positive rate parameter:
+    // Non-positive scale parameter:
     > y = {{alias}}( 0.5, 1.0, -1.0 )
     NaN
 
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/docs/types/index.d.ts b/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/docs/types/index.d.ts
index f9e6822e6147..5a3171d98e4c 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/docs/types/index.d.ts
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/docs/types/index.d.ts
@@ -81,7 +81,7 @@ interface Quantile {
 	* // returns NaN
 	*
 	* @example
-	* // Non-positive rate parameter:
+	* // Non-positive scale parameter:
 	* var y = quantile( 0.5, 1.0, -1.0 );
 	* // returns NaN
 	*/
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/test/fixtures/julia/runner.jl b/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/test/fixtures/julia/runner.jl
index d6e686cee337..95b0f04d74a9 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/test/fixtures/julia/runner.jl
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/test/fixtures/julia/runner.jl
@@ -28,7 +28,7 @@ Generate fixture data and write to file.
 
 * `p`: input value
 * `alpha`: shape parameter
-* `beta`: rate parameter
+* `beta`: scale parameter
 * `name::AbstractString`: output filename
 
 # Examples
@@ -70,7 +70,7 @@ file = @__FILE__;
 # Extract the directory in which this file resides:
 dir = dirname( file );
 
-# Large rate parameter:
+# Large scale parameter:
 p = rand( 1000 );
 alpha = rand( 1000 ) .* 10.0;
 beta = ( rand( 1000 ) .* 10.0 ) .+ 10.0;
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/test/test.factory.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/test/test.factory.js
index e5ed29c2dfbc..c4527ac6a72f 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/test/test.factory.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/test/test.factory.js
@@ -169,14 +169,14 @@ tape( 'the created function evaluates the quantile for `p` given large `alpha` a
 	alpha = bothLarge.alpha;
 	beta = bothLarge.beta;
 	for ( i = 0; i < p.length; i++ ) {
-		quantile = factory( alpha[i], beta[i] );
-		y = quantile( p[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'p: '+p[i]+', alpha:'+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		quantile = factory( alpha[ i ], beta[ i ] );
+		y = quantile( p[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'p: '+p[ i ]+', alpha:'+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 200.0 * EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. p: '+p[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. p: '+p[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
@@ -198,20 +198,20 @@ tape( 'the created function evaluates the quantile for `p` given large shape par
 	alpha = largeShape.alpha;
 	beta = largeShape.beta;
 	for ( i = 0; i < p.length; i++ ) {
-		quantile = factory( alpha[i], beta[i] );
-		y = quantile( p[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'p: '+p[i]+', alpha:'+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		quantile = factory( alpha[ i ], beta[ i ] );
+		y = quantile( p[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'p: '+p[ i ]+', alpha:'+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 100.0 * EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. p: '+p[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. p: '+p[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
 });
 
-tape( 'the created function evaluates the quantile for `p` given large rate parameter `beta`', function test( t ) {
+tape( 'the created function evaluates the quantile for `p` given large scale parameter `beta`', function test( t ) {
 	var expected;
 	var quantile;
 	var delta;
@@ -227,14 +227,14 @@ tape( 'the created function evaluates the quantile for `p` given large rate para
 	alpha = largeRate.alpha;
 	beta = largeRate.beta;
 	for ( i = 0; i < p.length; i++ ) {
-		quantile = factory( alpha[i], beta[i] );
-		y = quantile( p[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'p: '+p[i]+', alpha:'+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		quantile = factory( alpha[ i ], beta[ i ] );
+		y = quantile( p[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'p: '+p[ i ]+', alpha:'+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 50.0 * EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. p: '+p[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. p: '+p[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/test/test.quantile.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/test/test.quantile.js
index d0fd33d5ec7b..a8240882f3fb 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/test/test.quantile.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/test/test.quantile.js
@@ -125,13 +125,13 @@ tape( 'the function evaluates the quantile for `x` given large parameters `alpha
 	alpha = bothLarge.alpha;
 	beta = bothLarge.beta;
 	for ( i = 0; i < p.length; i++ ) {
-		y = quantile( p[i], alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'p: '+p[i]+', alpha:'+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = quantile( p[ i ], alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'p: '+p[ i ]+', alpha:'+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 200.0 * EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. p: '+p[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. p: '+p[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
@@ -152,19 +152,19 @@ tape( 'the function evaluates the quantile for `x` given large shape parameter `
 	alpha = largeShape.alpha;
 	beta = largeShape.beta;
 	for ( i = 0; i < p.length; i++ ) {
-		y = quantile( p[i], alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'p: '+p[i]+', alpha:'+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = quantile( p[ i ], alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'p: '+p[ i ]+', alpha:'+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 100.0 * EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. p: '+p[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. p: '+p[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
 });
 
-tape( 'the function evaluates the quantile for `x` given large rate parameter `beta`', function test( t ) {
+tape( 'the function evaluates the quantile for `x` given large scale parameter `beta`', function test( t ) {
 	var expected;
 	var delta;
 	var alpha;
@@ -179,13 +179,13 @@ tape( 'the function evaluates the quantile for `x` given large rate parameter `b
 	alpha = largeRate.alpha;
 	beta = largeRate.beta;
 	for ( i = 0; i < p.length; i++ ) {
-		y = quantile( p[i], alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'p: '+p[i]+', alpha:'+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = quantile( p[ i ], alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'p: '+p[ i ]+', alpha:'+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 50.0 * EPS * abs( expected[ i ] );
-			t.ok( delta <= tol, 'within tolerance. p: '+p[ i ]+'. alpha: '+alpha[i]+'. beta: '+beta[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
+			t.ok( delta <= tol, 'within tolerance. p: '+p[ i ]+'. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/README.md b/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/README.md
index baefc0e63a18..3fdda76e5c73 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/README.md
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/README.md
@@ -26,7 +26,7 @@ limitations under the License.
 
 <section class="intro">
 
-The [skewness][skewness] for an [inverse gamma][invgamma-distribution] random variable with shape parameter `α` and rate parameter `β` is
+The [skewness][skewness] for an [inverse gamma][invgamma-distribution] random variable with shape parameter `α` and scale parameter `β` is
 
 <!-- <equation class="equation" label="eq:invgamma_skewness" align="center" raw="\operatorname{skew}\left( X \right) = \frac{4\sqrt{\alpha-2}}{\alpha-3}" alt="Skewness for an inverse gamma distribution."> -->
 
@@ -59,7 +59,7 @@ var skewness = require( '@stdlib/stats/base/dists/invgamma/skewness' );
 
 #### skewness( alpha, beta )
 
-Returns the [skewness][skewness] of a [inverse gamma][invgamma-distribution] distribution with parameters `alpha` (shape parameter) and `beta` (rate parameter).
+Returns the [skewness][skewness] of a [inverse gamma][invgamma-distribution] distribution with parameters `alpha` (shape parameter) and `beta` (scale parameter).
 
 ```javascript
 var v = skewness( 4.0, 12.0 );
@@ -166,7 +166,7 @@ logEachMap( 'α: %0.4f, β: %0.4f, skew(X;α,β): %0.4f', alpha, beta, skewness
 
 #### stdlib_base_dists_invgamma_skewness( alpha, beta )
 
-Evaluates the [skewness][skewness] of a [inverse gamma][invgamma-distribution] distribution with parameters `alpha` (shape parameter) and `beta` (rate parameter).
+Evaluates the [skewness][skewness] of a [inverse gamma][invgamma-distribution] distribution with parameters `alpha` (shape parameter) and `beta` (scale parameter).
 
 ```c
 double out = stdlib_base_dists_invgamma_skewness( 4.0, 12.0 );
@@ -176,7 +176,7 @@ double out = stdlib_base_dists_invgamma_skewness( 4.0, 12.0 );
 The function accepts the following arguments:
 
 -   **alpha**: `[in] double` shape parameter.
--   **beta**: `[in] double` rate parameter.
+-   **beta**: `[in] double` scale parameter.
 
 ```c
 double stdlib_base_dists_invgamma_skewness( const double alpha, const double beta );
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/docs/types/index.d.ts b/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/docs/types/index.d.ts
index bdf48e2c7b35..4dec81fde289 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/docs/types/index.d.ts
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/docs/types/index.d.ts
@@ -26,7 +26,7 @@
 * -   If `alpha <= 0` or `beta <= 0`, the function returns `NaN`.
 *
 * @param alpha - shape parameter
-* @param beta - rate parameter
+* @param beta - scale parameter
 * @returns skewness
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/lib/main.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/lib/main.js
index 468698e79636..8e830ad9dcc3 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/lib/main.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/lib/main.js
@@ -30,7 +30,7 @@ var sqrt = require( '@stdlib/math/base/special/sqrt' );
 * Returns the skewness of an inverse gamma distribution.
 *
 * @param {PositiveNumber} alpha - shape parameter
-* @param {PositiveNumber} beta - rate parameter
+* @param {PositiveNumber} beta - scale parameter
 * @returns {PositiveNumber} skewness
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/lib/native.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/lib/native.js
index 74d4cf54588f..ec0b57fb21e5 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/lib/native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/lib/native.js
@@ -30,7 +30,7 @@ var addon = require( './../src/addon.node' );
 *
 * @private
 * @param {PositiveNumber} alpha - shape parameter
-* @param {PositiveNumber} beta - rate parameter
+* @param {PositiveNumber} beta - scale parameter
 * @returns {PositiveNumber} skewness
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/src/main.c b/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/src/main.c
index c33edd73234f..88848b19e28b 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/src/main.c
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/src/main.c
@@ -24,7 +24,7 @@
 * Returns the skewness of an inverse gamma distribution.
 *
 * @param alpha    shape parameter
-* @param beta     rate parameter
+* @param beta     scale parameter
 * @return         skewness
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/test/fixtures/julia/runner.jl b/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/test/fixtures/julia/runner.jl
index 0426b1aee620..cb2641a15beb 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/test/fixtures/julia/runner.jl
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/test/fixtures/julia/runner.jl
@@ -27,7 +27,7 @@ Generate fixture data and write to file.
 # Arguments
 
 * `alpha`: shape parameter
-* `beta`: rate parameter
+* `beta`: scale parameter
 * `name::AbstractString`: output filename
 
 # Examples
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/test/test.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/test/test.js
index 5a6424c57ba5..f5cf1beba37e 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/test/test.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/test/test.js
@@ -107,13 +107,13 @@ tape( 'the function returns the skewness of a gamma distribution', function test
 	alpha = data.alpha;
 	beta = data.beta;
 	for ( i = 0; i < expected.length; i++ ) {
-		y = skewness( alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = skewness( alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'alpha: '+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 2.0 * 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+'.' );
+			t.ok( delta <= tol, 'within tolerance. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/test/test.native.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/test/test.native.js
index b30695cbbe37..10c94a2950e6 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/test/test.native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/skewness/test/test.native.js
@@ -116,13 +116,13 @@ tape( 'the function returns the skewness of a gamma distribution', opts, functio
 	alpha = data.alpha;
 	beta = data.beta;
 	for ( i = 0; i < expected.length; i++ ) {
-		y = skewness( alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = skewness( alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'alpha: '+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 2.0 * 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+'.' );
+			t.ok( delta <= tol, 'within tolerance. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/README.md b/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/README.md
index c466899c7f65..6d6038960e79 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/README.md
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/README.md
@@ -26,7 +26,7 @@ limitations under the License.
 
 <section class="intro">
 
-The [standard deviation][standard-deviation] for an [inverse gamma][invgamma-distribution] random variable with shape parameter `α` and rate parameter `β` is
+The [standard deviation][standard-deviation] for an [inverse gamma][invgamma-distribution] random variable with shape parameter `α` and scale parameter `β` is
 
 <!-- <equation class="equation" label="eq:invgamma_stdev" align="center" raw="\sigma = \frac{\beta}{(\alpha-1)\sqrt{\alpha-2}}" alt="Standard deviation for an inverse gamma distribution."> -->
 
@@ -59,7 +59,7 @@ var stdev = require( '@stdlib/stats/base/dists/invgamma/stdev' );
 
 #### stdev( alpha, beta )
 
-Returns the [standard deviation][standard-deviation] of a [inverse gamma][invgamma-distribution] distribution with parameters `alpha` (shape parameter) and `beta` (rate parameter).
+Returns the [standard deviation][standard-deviation] of a [inverse gamma][invgamma-distribution] distribution with parameters `alpha` (shape parameter) and `beta` (scale parameter).
 
 ```javascript
 var v = stdev( 7.0, 7.0 );
@@ -169,7 +169,7 @@ logEachMap( 'α: %0.4f, β: %0.4f, SD(X;α,β): %0.4f', alpha, beta, stdev );
 
 #### stdlib_base_dists_invgamma_stdev( alpha, beta )
 
-Evaluates the [standard deviation][standard-deviation] of a [inverse gamma][invgamma-distribution] distribution with parameters `alpha` (shape parameter) and `beta` (rate parameter).
+Evaluates the [standard deviation][standard-deviation] of a [inverse gamma][invgamma-distribution] distribution with parameters `alpha` (shape parameter) and `beta` (scale parameter).
 
 ```c
 double out = stdlib_base_dists_invgamma_stdev( 3.0, 5.0 );
@@ -179,7 +179,7 @@ double out = stdlib_base_dists_invgamma_stdev( 3.0, 5.0 );
 The function accepts the following arguments:
 
 -   **alpha**: `[in] double` shape parameter.
--   **beta**: `[in] double` rate parameter.
+-   **beta**: `[in] double` scale parameter.
 
 ```c
 double stdlib_base_dists_invgamma_stdev( const double alpha, const double beta );
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/docs/types/index.d.ts b/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/docs/types/index.d.ts
index 7226a45be94d..7c745a3643a8 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/docs/types/index.d.ts
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/docs/types/index.d.ts
@@ -26,7 +26,7 @@
 * -   If `alpha <= 0` or `beta <= 0`, the function returns `NaN`.
 *
 * @param alpha - shape parameter
-* @param beta - rate parameter
+* @param beta - scale parameter
 * @returns standard deviation
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/lib/main.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/lib/main.js
index 6d63cc88d3bc..5453826c27c9 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/lib/main.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/lib/main.js
@@ -30,7 +30,7 @@ var sqrt = require( '@stdlib/math/base/special/sqrt' );
 * Returns the standard deviation of an inverse gamma distribution.
 *
 * @param {PositiveNumber} alpha - shape parameter
-* @param {PositiveNumber} beta - rate parameter
+* @param {PositiveNumber} beta - scale parameter
 * @returns {PositiveNumber} standard deviation
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/lib/native.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/lib/native.js
index 8c025a95b5b3..ad276032e5e3 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/lib/native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/lib/native.js
@@ -30,7 +30,7 @@ var addon = require( './../src/addon.node' );
 *
 * @private
 * @param {PositiveNumber} alpha - shape parameter
-* @param {PositiveNumber} beta - rate parameter
+* @param {PositiveNumber} beta - scale parameter
 * @returns {PositiveNumber} standard deviation
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/src/main.c b/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/src/main.c
index 7a3ac3ea0c9f..59bad72f99e8 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/src/main.c
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/src/main.c
@@ -23,7 +23,7 @@
 * Returns the standard deviation of an inverse gamma distribution.
 *
 * @param alpha    shape parameter
-* @param beta     rate parameter
+* @param beta     scale parameter
 * @return         standard deviation
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/test/fixtures/julia/runner.jl b/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/test/fixtures/julia/runner.jl
index 2c8438583478..46a19a8af12d 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/test/fixtures/julia/runner.jl
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/test/fixtures/julia/runner.jl
@@ -27,7 +27,7 @@ Generate fixture data and write to file.
 # Arguments
 
 * `alpha`: shape parameter
-* `beta`: rate parameter
+* `beta`: scale parameter
 * `name::AbstractString`: output filename
 
 # Examples
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/test/test.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/test/test.js
index 1db6ca5f0e52..8936a6dc86e1 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/test/test.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/test/test.js
@@ -113,13 +113,13 @@ tape( 'the function returns the standard deviation of an inverse gamma distribut
 	alpha = data.alpha;
 	beta = data.beta;
 	for ( i = 0; i < expected.length; i++ ) {
-		y = stdev( alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = stdev( alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'alpha: '+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 2.0 * 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+'.' );
+			t.ok( delta <= tol, 'within tolerance. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/test/test.native.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/test/test.native.js
index 2379f4288f59..bf61910c092e 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/test/test.native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/stdev/test/test.native.js
@@ -122,13 +122,13 @@ tape( 'the function returns the standard deviation of an inverse gamma distribut
 	alpha = data.alpha;
 	beta = data.beta;
 	for ( i = 0; i < expected.length; i++ ) {
-		y = stdev( alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = stdev( alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'alpha: '+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 2.0 * 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+'.' );
+			t.ok( delta <= tol, 'within tolerance. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/README.md b/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/README.md
index abd42731f769..7370f7d07204 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/README.md
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/README.md
@@ -26,7 +26,7 @@ limitations under the License.
 
 <section class="intro">
 
-The [variance][variance] for an [inverse gamma][invgamma-distribution] random variable with shape parameter `α` and rate parameter `β` is
+The [variance][variance] for an [inverse gamma][invgamma-distribution] random variable with shape parameter `α` and scale parameter `β` is
 
 <!-- <equation class="equation" label="eq:invgamma_variance" align="center" raw="\operatorname{Var}\left( X \right) = \frac{\beta^{2}}{(\alpha-1)^{2}(\alpha-2)}" alt="Variance for an inverse gamma distribution."> -->
 
@@ -59,7 +59,7 @@ var variance = require( '@stdlib/stats/base/dists/invgamma/variance' );
 
 #### variance( alpha, beta )
 
-Returns the [variance][variance] of a [inverse gamma][invgamma-distribution] distribution with parameters `alpha` (shape parameter) and `beta` (rate parameter).
+Returns the [variance][variance] of a [inverse gamma][invgamma-distribution] distribution with parameters `alpha` (shape parameter) and `beta` (scale parameter).
 
 ```javascript
 var v = variance( 7.0, 7.0 );
@@ -179,7 +179,7 @@ double out = stdlib_base_dists_invgamma_variance( 3.0, 5.0 );
 The function accepts the following arguments:
 
 -   **alpha**: `[in] double` shape parameter.
--   **beta**: `[in] double` rate parameter.
+-   **beta**: `[in] double` scale parameter.
 
 ```c
 double stdlib_base_dists_invgamma_variance( const double alpha, const double beta );
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/docs/types/index.d.ts b/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/docs/types/index.d.ts
index f5daf8cd394c..b4d2cfb40752 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/docs/types/index.d.ts
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/docs/types/index.d.ts
@@ -26,7 +26,7 @@
 * -   If `alpha <= 0` or `beta <= 0`, the function returns `NaN`.
 *
 * @param alpha - shape parameter
-* @param beta - rate parameter
+* @param beta - scale parameter
 * @returns variance
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/lib/main.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/lib/main.js
index d9665c01f3a9..57100ea73b3c 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/lib/main.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/lib/main.js
@@ -30,7 +30,7 @@ var pow = require( '@stdlib/math/base/special/pow' );
 * Returns the variance of an inverse gamma distribution.
 *
 * @param {PositiveNumber} alpha - shape parameter
-* @param {PositiveNumber} beta - rate parameter
+* @param {PositiveNumber} beta - scale parameter
 * @returns {PositiveNumber} variance
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/lib/native.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/lib/native.js
index 18211a201320..0ca1be8c8471 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/lib/native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/lib/native.js
@@ -30,7 +30,7 @@ var addon = require( './../src/addon.node' );
 *
 * @private
 * @param {PositiveNumber} alpha - shape parameter
-* @param {PositiveNumber} beta - rate parameter
+* @param {PositiveNumber} beta - scale parameter
 * @returns {PositiveNumber} variance
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/src/main.c b/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/src/main.c
index 35fcda9731ec..2833aa3c0742 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/src/main.c
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/src/main.c
@@ -23,7 +23,7 @@
 * Returns the variance of an inverse gamma distribution.
 *
 * @param alpha    shape parameter
-* @param beta     rate parameter
+* @param beta     scale parameter
 * @return         variance
 *
 * @example
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/test/fixtures/julia/runner.jl b/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/test/fixtures/julia/runner.jl
index ced6658c003b..2edfdae36203 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/test/fixtures/julia/runner.jl
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/test/fixtures/julia/runner.jl
@@ -27,7 +27,7 @@ Generate fixture data and write to file.
 # Arguments
 
 * `alpha`: shape parameter
-* `beta`: rate parameter
+* `beta`: scale parameter
 * `name::AbstractString`: output filename
 
 # Examples
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/test/test.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/test/test.js
index cbb1e9c250cf..bdfdd9a2f520 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/test/test.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/test/test.js
@@ -113,13 +113,13 @@ tape( 'the function returns the variance of an inverse gamma distribution', func
 	alpha = data.alpha;
 	beta = data.beta;
 	for ( i = 0; i < expected.length; i++ ) {
-		y = variance( alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = variance( alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'alpha: '+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 2.0 * 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+'.' );
+			t.ok( delta <= tol, 'within tolerance. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/test/test.native.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/test/test.native.js
index 7fa0fd64a265..0666f5984f08 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/test/test.native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/variance/test/test.native.js
@@ -122,13 +122,13 @@ tape( 'the function returns the variance of an inverse gamma distribution', opts
 	alpha = data.alpha;
 	beta = data.beta;
 	for ( i = 0; i < expected.length; i++ ) {
-		y = variance( alpha[i], beta[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'alpha: '+alpha[i]+', beta: '+beta[i]+', y: '+y+', expected: '+expected[i] );
+		y = variance( alpha[ i ], beta[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'alpha: '+alpha[ i ]+', beta: '+beta[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
 			delta = abs( y - expected[ i ] );
 			tol = 2.0 * 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+'.' );
+			t.ok( delta <= tol, 'within tolerance. alpha: '+alpha[ i ]+'. beta: '+beta[ i ]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
 		}
 	}
 	t.end();