feat: add C implementation for math/base/special/gammainc

lib/node_modules/@stdlib/math/base/special/gammainc/README.md

@@ -174,6 +174,98 @@ for ( i = 0; i < 100; i++ ) {
 
 <!-- /.examples -->
 
+<!-- C interface documentation. -->
+
+* * *
+
+<section class="c">
+
+## C APIs
+
+<!-- Section to include introductory text. Make sure to keep an empty line after the intro `section` element and another before the `/section` close. -->
+
+<section class="intro">
+
+</section>
+
+<!-- /.intro -->
+
+<!-- C usage documentation. -->
+
+<section class="usage">
+
+### Usage
+
+```c
+#include "stdlib/math/base/special/gammainc.h"
+```
+
+#### stdlib_base_gammainc( x, s, regularized, upper )
+
+Computes the regularized lower [incomplete gamma function][incomplete-gamma-function] for inputs `x` and `s`. .
+
+```c
+double out = stdlib_base_gammainc( 1.0, 2.0, true, true );
+// returns ~0.7358
+```
+
+The function accepts the following arguments:
+
+-   **x**: `[in] double` input value.
+-   **s**: `[in] double` input value.
+-   **regularized**: `[in] bool` indicating if the function should evaluate the regularized or non-regularized incomplete gamma functions.
+-   **upper**: `[in] bool`  indicating if the function should return the upper tail of the incomplete gamma function
+
+```c
+double stdlib_base_gammainc( double x, double a, bool regularized, bool upper );
+```
+
+</section>
+
+<!-- /.usage -->
+
+<!-- C API usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
+
+<section class="notes">
+
+</section>
+
+<!-- /.notes -->
+
+<!-- C API usage examples. -->
+
+<section class="examples">
+
+### Examples
+
+```c
+#include "stdlib/math/base/special/gammainc.h"
+#include <stdio.h>
+#include <stdbool.h>
+
+int main( void ) {
+    const double x[] = { 6.0, 1.0, 7.0, 7.0, 0.0/0.0, 6.0 };
+    const double a[] = { 2.0, 2.0, 5.0, 5.0, 2.0, 0.0/0.0 };
+    const bool regularized[] = { true, true, true, false, true, true };
+    const bool upper[] = { false, true, false, false, false, false };
+
+    double y;
+    int i;
+    for ( i = 0; i <6; i++ ) {
+        y = stdlib_base_gammainc( x[ i ], a[ i ], regularized[ i ], upper[ i ] );
+        printf( "gammainc( %lf, %lf, %s, %s ) = %lf\n", x[ i ], a[ i ], regularized[ i ] ? "true" : "false", upper[ i ] ? "true" : "false", y );
+    }
+}
+```
+
+</section>
+
+<!-- /.examples -->
+
+</section>
+
+<!-- /.c -->
+
 <!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
 
 <section class="related">

lib/node_modules/@stdlib/math/base/special/gammainc/benchmark/benchmark.js

@@ -21,13 +21,20 @@
 // MODULES //
 
 var bench = require( '@stdlib/bench' );
-var randu = require( '@stdlib/random/base/randu' );
+var uniform = require( '@stdlib/random/array/uniform' );
 var isnan = require( '@stdlib/math/base/assert/is-nan' );
 var EPS = require( '@stdlib/constants/float64/eps' );
 var pkg = require( './../package.json' ).name;
 var gammainc = require( './../lib' );
 
 
+// VARIABLES //
+
+var options = {
+	'dtype': 'float64'
+};
+
+
 // MAIN //
 
 bench( pkg, function benchmark( b ) {
@@ -36,11 +43,12 @@ bench( pkg, function benchmark( b ) {
 	var z;
 	var i;
 
+	x = uniform( b.iterations, 10, 1000, options );
+	y = uniform( b.iterations, EPS, 1000, options );
+
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		x = ( randu()*1000.0 ) - 0.0;
-		y = ( randu()*1000.0 ) + EPS;
-		z = gammainc( x, y );
+		z = gammainc( x[ i ], y[ i ] );
 		if ( isnan( z ) ) {
 			b.fail( 'should not return NaN' );
 		}
@@ -59,11 +67,12 @@ bench( pkg+':regularized=true', function benchmark( b ) {
 	var z;
 	var i;
 
+	x = uniform( b.iterations, 0, 1000, options );
+	y = uniform( b.iterations, EPS, 1000, options );
+
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		x = ( randu()*1000.0 ) - 0.0;
-		y = ( randu()*1000.0 ) + EPS;
-		z = gammainc( x, y, true );
+		z = gammainc( x[ i ], y[ i ], true );
 		if ( isnan( z ) ) {
 			b.fail( 'should not return NaN' );
 		}
@@ -82,11 +91,12 @@ bench( pkg+':regularized=false', function benchmark( b ) {
 	var z;
 	var i;
 
+	x = uniform( b.iterations, 0, 1000, options );
+	y = uniform( b.iterations, EPS, 1000, options );
+
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		x = ( randu()*1000.0 ) - 0.0;
-		y = ( randu()*1000.0 ) + EPS;
-		z = gammainc( x, y, false );
+		z = gammainc( x[ i ], y[ i ], false );
 		if ( isnan( z ) ) {
 			b.fail( 'should not return NaN' );
 		}
@@ -105,11 +115,12 @@ bench( pkg+':regularized=true,upper=true', function benchmark( b ) {
 	var z;
 	var i;
 
+	x = uniform( b.iterations, 0, 1000, options );
+	y = uniform( b.iterations, EPS, 1000, options );
+
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		x = ( randu()*1000.0 ) - 0.0;
-		y = ( randu()*1000.0 ) + EPS;
-		z = gammainc( x, y, true, true );
+		z = gammainc( x[ i ], y[ i ], true, true );
 		if ( isnan( z ) ) {
 			b.fail( 'should not return NaN' );
 		}
@@ -128,11 +139,12 @@ bench( pkg+':regularized=true,upper=false', function benchmark( b ) {
 	var z;
 	var i;
 
+	x = uniform( b.iterations, 0, 1000, options );
+	y = uniform( b.iterations, EPS, 1000, options );
+
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		x = ( randu()*1000.0 ) - 0.0;
-		y = ( randu()*1000.0 ) + EPS;
-		z = gammainc( x, y, true, false );
+		z = gammainc( x[ i ], y[ i ], true, false );
 		if ( isnan( z ) ) {
 			b.fail( 'should not return NaN' );
 		}
@@ -151,11 +163,12 @@ bench( pkg+':regularized=false,upper=true', function benchmark( b ) {
 	var z;
 	var i;
 
+	x = uniform( b.iterations, 0, 1000, options );
+	y = uniform( b.iterations, EPS, 1000, options );
+
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		x = ( randu()*1000.0 ) - 0.0;
-		y = ( randu()*1000.0 ) + EPS;
-		z = gammainc( x, y, false, true );
+		z = gammainc( x[ i ], y[ i ], false, true );
 		if ( isnan( z ) ) {
 			b.fail( 'should not return NaN' );
 		}
@@ -174,11 +187,12 @@ bench( pkg+':regularized=false,upper=false', function benchmark( b ) {
 	var z;
 	var i;
 
+	x = uniform( b.iterations, 0, 1000, options );
+	y = uniform( b.iterations, EPS, 1000, options );
+
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		x = ( randu()*1000.0 ) - 0.0;
-		y = ( randu()*1000.0 ) + EPS;
-		z = gammainc( x, y, false, false );
+		z = gammainc( x[ i ], y[ i ], false, false );
 		if ( isnan( z ) ) {
 			b.fail( 'should not return NaN' );
 		}