diff --git a/lib/node_modules/@stdlib/ml/base/loss/float64/modified-huber-gradient/README.md b/lib/node_modules/@stdlib/ml/base/loss/float64/modified-huber-gradient/README.md
index 044a3fbe80a5..7b733e8df658 100644
--- a/lib/node_modules/@stdlib/ml/base/loss/float64/modified-huber-gradient/README.md
+++ b/lib/node_modules/@stdlib/ml/base/loss/float64/modified-huber-gradient/README.md
@@ -24,7 +24,7 @@ limitations under the License.
 
 <section class="intro">
 
-The [modified huber loss gradient][modified-huber-loss-gradient] is defined as
+The [modified Huber loss gradient][modified-huber-loss-gradient] is defined as
 
 <!-- <equation class="equation" label="eq:modified_huber_loss_gradient" align="center" raw="
 \frac{\partial \ell}{\partial w_i} = \begin{cases} -4yx_i & \text{if } yp < -1 \\ -2y(1 - yp)x_i & \text{if } -1 \le yp \le 1 \\ 0 & \text{if } yp > 1 \end{cases}" alt="Equation for the modified Huber loss gradient."> -->
@@ -73,12 +73,15 @@ The function accepts the following arguments:
 If any argument is `NaN`, the function returns `NaN`.
 
 ```javascript
-var v = modifiedHuberGradient( 2.0, NaN, 0.782 );
+var v = modifiedHuberGradient( NaN, 1.0, 0.782 );
 // returns NaN
 
 v = modifiedHuberGradient( 1.0, NaN, 0.782 );
 // returns NaN
 
+v = modifiedHuberGradient( 1.0, 1.0, NaN );
+// returns NaN
+
 v = modifiedHuberGradient( NaN, NaN, NaN );
 // returns NaN
 ```
diff --git a/lib/node_modules/@stdlib/stats/base/dists/anglit/pdf/test/test.factory.js b/lib/node_modules/@stdlib/stats/base/dists/anglit/pdf/test/test.factory.js
index 8c38761b5c06..4d3002b675b6 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/anglit/pdf/test/test.factory.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/anglit/pdf/test/test.factory.js
@@ -130,12 +130,12 @@ tape( 'the created function evaluates the pdf for `x` given `mu` and `sigma`', f
 	sigma = data.sigma;
 
 	for ( i = 0; i < x.length; i++ ) {
-		pdf = factory( mu[i], sigma[i] );
-		y = pdf( x[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual(y, expected[i], 'x: '+x[i]+', mu: '+mu[i]+', sigma: '+sigma[i]+', y: '+y+', expected: '+expected[i]);
+		pdf = factory( mu[ i ], sigma[ i ] );
+		y = pdf( x[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', mu: '+mu[ i ]+', sigma: '+sigma[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 1 ), 'within tolerance. x: '+x[i]+'. mu: '+mu[i]+'. sigma: '+sigma[i]+'. y: '+y+'. expected: '+expected[i]+'.' );
+			t.ok( isAlmostSameValue( y, expected[ i ], 1 ), 'within tolerance. x: '+x[ i ]+'. mu: '+mu[ i ]+'. sigma: '+sigma[ i ]+'. y: '+y+'. expected: '+expected[ i ]+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/anglit/pdf/test/test.main.js b/lib/node_modules/@stdlib/stats/base/dists/anglit/pdf/test/test.main.js
index f81edc7c26b0..f181782776dc 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/anglit/pdf/test/test.main.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/anglit/pdf/test/test.main.js
@@ -95,11 +95,11 @@ tape( 'the function evaluates the pdf for `x` given `mu` and `sigma`', function
 	sigma = data.sigma;
 
 	for ( i = 0; i < x.length; i++ ) {
-		y = pdf( x[i], mu[i], sigma[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', mu: '+mu[i]+', sigma: '+sigma[i]+', y: '+y+', expected: '+expected[i] );
+		y = pdf( x[ i ], mu[ i ], sigma[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', mu: '+mu[ i ]+', sigma: '+sigma[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 1 ), 'within tolerance. x: '+x[i]+'. mu: '+mu[i]+'. sigma: '+sigma[i]+'. y: '+y+'. expected: '+expected[i]+'.' );
+			t.ok( isAlmostSameValue( y, expected[ i ], 1 ), 'within tolerance. x: '+x[ i ]+'. mu: '+mu[ i ]+'. sigma: '+sigma[ i ]+'. y: '+y+'. expected: '+expected[ i ]+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/anglit/pdf/test/test.native.js b/lib/node_modules/@stdlib/stats/base/dists/anglit/pdf/test/test.native.js
index b3e29011fe83..1e24e35f3479 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/anglit/pdf/test/test.native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/anglit/pdf/test/test.native.js
@@ -104,11 +104,11 @@ tape( 'the function evaluates the pdf for `x` given `mu` and `sigma`', opts, fun
 	sigma = data.sigma;
 
 	for ( i = 0; i < x.length; i++ ) {
-		y = pdf( x[i], mu[i], sigma[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'x: '+x[i]+', mu: '+mu[i]+', sigma: '+sigma[i]+', y: '+y+', expected: '+expected[i] );
+		y = pdf( x[ i ], mu[ i ], sigma[ i ] );
+		if ( y === expected[ i ] ) {
+			t.strictEqual( y, expected[ i ], 'x: '+x[ i ]+', mu: '+mu[ i ]+', sigma: '+sigma[ i ]+', y: '+y+', expected: '+expected[ i ] );
 		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 1 ), 'within tolerance. x: '+x[i]+'. mu: '+mu[i]+'. sigma: '+sigma[i]+'. y: '+y+'. expected: '+expected[i]+'.' );
+			t.ok( isAlmostSameValue( y, expected[ i ], 1 ), 'within tolerance. x: '+x[ i ]+'. mu: '+mu[ i ]+'. sigma: '+sigma[ i ]+'. y: '+y+'. expected: '+expected[ i ]+'.' );
 		}
 	}
 	t.end();
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/include/stdlib/stats/base/dists/invgamma/quantile.h b/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/include/stdlib/stats/base/dists/invgamma/quantile.h
index b6562517bcfa..16b45a906e60 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/include/stdlib/stats/base/dists/invgamma/quantile.h
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/include/stdlib/stats/base/dists/invgamma/quantile.h
@@ -27,7 +27,7 @@ extern "C" {
 #endif
 
 /**
-* Evaluates the quantile function for an inverse gamma distribution.
+* Evaluates the quantile function for an inverse gamma distribution with shape parameter `alpha` and scale parameter `beta` at a probability `p`.
 */
 double stdlib_base_dists_invgamma_quantile( const double p, const double alpha, const double beta );
 
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/src/main.c b/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/src/main.c
index 87102cdbe13b..ccdd302eb156 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/src/main.c
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/src/main.c
@@ -21,7 +21,7 @@
 #include "stdlib/math/base/assert/is_nan.h"
 
 /**
-* Evaluates the quantile function for an inverse gamma distribution.
+* Evaluates the quantile function for an inverse gamma distribution with shape parameter `alpha` and scale parameter `beta` at a probability `p`.
 *
 * @param p        input value
 * @param alpha    shape parameter
diff --git a/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/test/test.native.js b/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/test/test.native.js
index 4c0f2d68cc46..da74540e3eb5 100644
--- a/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/test/test.native.js
+++ b/lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/test/test.native.js
@@ -140,13 +140,13 @@ tape( 'the function evaluates the quantile for `x` given large parameters `alpha
 	beta = bothLarge.beta;
 	p = bothLarge.p;
 	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();
@@ -167,19 +167,19 @@ tape( 'the function evaluates the quantile for `x` given large shape parameter `
 	beta = largeShape.beta;
 	p = largeShape.p;
 	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`', opts, function test( t ) {
+tape( 'the function evaluates the quantile for `x` given large scale parameter `beta`', opts, function test( t ) {
 	var expected;
 	var delta;
 	var alpha;
@@ -194,13 +194,13 @@ tape( 'the function evaluates the quantile for `x` given large rate parameter `b
 	beta = largeRate.beta;
 	p = largeRate.p;
 	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();