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chore: follow-up fixes for commits merged to develop in the last 24 hours #13653

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feec12b
docs: fix NaN examples and capitalization in `ml/base/loss/float64/mo…
claude Jul 25, 2026
4219f3f
chore: fix doc summaries, test description, and formatting in `stats/…
claude Jul 25, 2026
a542a76
style: use consistent spacing around array indices in `stats/base/dis…
claude Jul 25, 2026
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7 changes: 5 additions & 2 deletions lib/node_modules/@stdlib/ml/base/loss/float64/modified-huber-gradient/README.md
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Expand Up @@ -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."> -->
Expand Down Expand Up @@ -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
```
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10 changes: 5 additions & 5 deletions lib/node_modules/@stdlib/stats/base/dists/anglit/pdf/test/test.factory.js
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Expand Up @@ -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();
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8 changes: 4 additions & 4 deletions lib/node_modules/@stdlib/stats/base/dists/anglit/pdf/test/test.main.js
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Expand Up @@ -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();
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8 changes: 4 additions & 4 deletions lib/node_modules/@stdlib/stats/base/dists/anglit/pdf/test/test.native.js
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Expand Up @@ -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();
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2 changes: 1 addition & 1 deletion ...ib/stats/base/dists/invgamma/quantile/include/stdlib/stats/base/dists/invgamma/quantile.h
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Expand Up @@ -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 );

Expand Down
2 changes: 1 addition & 1 deletion lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/src/main.c
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Expand Up @@ -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
Expand Down
26 changes: 13 additions & 13 deletions lib/node_modules/@stdlib/stats/base/dists/invgamma/quantile/test/test.native.js
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Expand Up @@ -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();
Expand All @@ -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;
Expand All @@ -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();
Expand Down