chore: minor updates

Author: Neerajpathak07

lib/node_modules/@stdlib/stats/base/dists/beta/logpdf/README.md

@@ -197,7 +197,7 @@ for ( i = 0; i < 10; i++ ) {
 
 #### stdlib_base_dists_beta_logpdf( x, alpha, beta )
 
-Evaluates the natural logarithm of the probability density function (logPDF) for a beta distribution with first shape parameter `alpha` and second shape parameter `beta` at a value `x`.
+Evaluates the natural logarithm of the probability density function (logPDF) for a beta distribution with first shape parameter `alpha` and second shape parameter `beta`.
 
 ```c
 double y = stdlib_base_dists_beta_logpdf( 0.5, 1.0, 1.0 );

lib/node_modules/@stdlib/stats/base/dists/beta/logpdf/benchmark/benchmark.js

@@ -42,20 +42,20 @@ bench( pkg, function benchmark( b ) {
 	len = 100;
 	alpha = new Float64Array( len );
 	for ( i = 0; i < len; i++ ) {
-		alpha[ i ] = uniform( EPS, 2.0 );
+		alpha[ i ] = uniform( EPS, 100.0 );
 	}
 	beta = new Float64Array( len );
 	for ( i = 0; i < len; i++ ) {
 		beta[ i ] = uniform( EPS, 100.0 );
 	}
 	x = new Float64Array( len );
 	for ( i = 0; i < len; i++ ) {
-		x[ i ] = uniform( EPS, 100.0 );
+		x[ i ] = uniform( EPS, 2.0 );
 	}
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		y = logpdf( x[ i%100 ], alpha[ i%100 ], beta[ i%100 ] );
+		y = logpdf( x[ i % len ], alpha[ i % len ], beta[ i % len ] );
 		if ( isnan( y ) ) {
 			b.fail( 'should not return NaN' );
 		}
@@ -89,7 +89,7 @@ bench( pkg+':factory', function benchmark( b ) {
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		y = mylogpdf( x[ i%100 ] );
+		y = mylogpdf( x[ i % len ] );
 		if ( isnan( y ) ) {
 			b.fail( 'should not return NaN' );
 		}

lib/node_modules/@stdlib/stats/base/dists/beta/logpdf/benchmark/benchmark.native.js

@@ -51,20 +51,20 @@ bench( pkg+'::native', opts, function benchmark( b ) {
 	len = 100;
 	alpha = new Float64Array( len );
 	for ( i = 0; i < len; i++ ) {
-		alpha[ i ] = uniform( EPS, 2.0 );
+		alpha[ i ] = uniform( EPS, 100.0 );
 	}
 	beta = new Float64Array( len );
 	for ( i = 0; i < len; i++ ) {
 		beta[ i ] = uniform( EPS, 100.0 );
 	}
 	x = new Float64Array( len );
 	for ( i = 0; i < len; i++ ) {
-		x[ i ] = uniform( EPS, 100.0 );
+		x[ i ] = uniform( EPS, 2.0 );
 	}
 
 	b.tic();
 	for ( i = 0; i < b.iterations; i++ ) {
-		y = logpdf( x[ i%100 ], alpha[ i%100 ], beta[ i%100 ] );
+		y = logpdf( x[ i % len ], alpha[ i % len ], beta[ i % len ] );
 		if ( isnan( y ) ) {
 			b.fail( 'should not return NaN' );
 		}

lib/node_modules/@stdlib/stats/base/dists/beta/logpdf/benchmark/c/benchmark.c

@@ -102,13 +102,13 @@ static double benchmark( void ) {
 	int i;
 
 	for ( i = 0; i < 100; i++ ) {
-		alpha[ i ] = random_uniform( STDLIB_CONSTANT_FLOAT64_EPS, 2.0 );
+		alpha[ i ] = random_uniform( STDLIB_CONSTANT_FLOAT64_EPS, 100.0 );
 	}
 	for ( i = 0; i < 100; i++ ) {
-		beta[ i ] = random_uniform( STDLIB_CONSTANT_FLOAT64_EPS, 10.0 );
+		beta[ i ] = random_uniform( STDLIB_CONSTANT_FLOAT64_EPS, 100.0 );
 	}
 	for ( i = 0; i < 100; i++ ) {
-		x[ i ] = random_uniform( STDLIB_CONSTANT_FLOAT64_EPS, 10.0 );
+		x[ i ] = random_uniform( STDLIB_CONSTANT_FLOAT64_EPS, 2.0 );
 	}
 
 	t = tic();

lib/node_modules/@stdlib/stats/base/dists/beta/logpdf/include/stdlib/stats/base/dists/beta/logpdf.h

@@ -27,7 +27,7 @@ extern "C" {
 #endif
 
 /**
-* Evaluates the natural logarithm of the probability density function (logPDF) for a beta distribution with first shape parameter `alpha` and second shape parameter `beta` at a value `x`.
+* Evaluates the natural logarithm of the probability density function (logPDF) for a beta distribution with first shape parameter `alpha` and second shape parameter `beta`.
 */
 double stdlib_base_dists_beta_logpdf( const double x, const double alpha, const double beta );
 

lib/node_modules/@stdlib/stats/base/dists/beta/logpdf/src/main.c

@@ -25,7 +25,7 @@
 #include "stdlib/constants/float64/ninf.h"
 
 /**
-* Evaluates the natural logarithm of the probability density function (logPDF) for a beta distribution with first shape parameter `alpha` and second shape parameter `beta` at a value `x`.
+* Evaluates the natural logarithm of the probability density function (logPDF) for a beta distribution with first shape parameter `alpha` and second shape parameter `beta`.
 *
 * @param x        input value
 * @param alpha    first shape parameter