fix: add missing API_SUFFIX wrappers to internal _ndarray calls

Propagates fixes from a65e1fd ("fix: add suffix wrappers and replace
inline NaN literal") and 8516750 ("fix: add missing suffix wrappers")
to sibling packages whose `API_SUFFIX`-wrapped routines call other
`API_SUFFIX`-wrapped strided routines without the macro.

Affected packages:

- `blas/ext/base/snansumpw`
- `blas/ext/base/ssumpw`
- `stats/strided/dsempn`
- `stats/strided/dsmeanpn`
- `stats/strided/dsvariancepn`
- `stats/strided/dztest`
- `stats/strided/dztest2`
- `stats/strided/sztest`
- `stats/strided/sztest2`

Author: claude

lib/node_modules/@stdlib/blas/ext/base/snansumpw/src/main.c

@@ -149,5 +149,5 @@ float API_SUFFIX(stdlib_strided_snansumpw_ndarray)( const CBLAS_INT N, const flo
 	// Recurse by dividing by two, but avoiding non-multiples of unroll factor...
 	n = N / 2;
 	n -= n % 8;
-	return stdlib_strided_snansumpw_ndarray( n, X, strideX, ix ) + stdlib_strided_snansumpw_ndarray( N-n, X, strideX, ix+(n*strideX) );
+	return API_SUFFIX(stdlib_strided_snansumpw_ndarray)( n, X, strideX, ix ) + API_SUFFIX(stdlib_strided_snansumpw_ndarray)( N-n, X, strideX, ix+(n*strideX) );
 }

lib/node_modules/@stdlib/blas/ext/base/ssumpw/src/main.c

@@ -128,5 +128,5 @@ float API_SUFFIX(stdlib_strided_ssumpw_ndarray)( const CBLAS_INT N, const float
 	// Recurse by dividing by two, but avoiding non-multiples of unroll factor...
 	n = N / 2;
 	n -= n % 8;
-	return stdlib_strided_ssumpw_ndarray( n, X, strideX, ix ) + stdlib_strided_ssumpw_ndarray( N-n, X, strideX, ix+(n*strideX) );
+	return API_SUFFIX(stdlib_strided_ssumpw_ndarray)( n, X, strideX, ix ) + API_SUFFIX(stdlib_strided_ssumpw_ndarray)( N-n, X, strideX, ix+(n*strideX) );
 }

lib/node_modules/@stdlib/stats/strided/dsempn/src/main.c

@@ -47,5 +47,5 @@ double API_SUFFIX(stdlib_strided_dsempn)( const CBLAS_INT N, const double correc
 * @return             output value
 */
 double API_SUFFIX(stdlib_strided_dsempn_ndarray)( const CBLAS_INT N, const double correction, const double *X, const CBLAS_INT strideX, const CBLAS_INT offsetX ) {
-	return stdlib_base_sqrt( stdlib_strided_dvariancepn_ndarray( N, correction, X, strideX, offsetX ) / (double)N );
+	return stdlib_base_sqrt( API_SUFFIX(stdlib_strided_dvariancepn_ndarray)( N, correction, X, strideX, offsetX ) / (double)N );
 }

lib/node_modules/@stdlib/stats/strided/dsmeanpn/src/main.c

@@ -67,11 +67,11 @@ double API_SUFFIX(stdlib_strided_dsmeanpn_ndarray)( const CBLAS_INT N, const flo
 	dN = (double)N;
 
 	// Compute an estimate for the mean:
-	mu = stdlib_strided_dssum_ndarray( N, X, strideX, offsetX );
+	mu = API_SUFFIX(stdlib_strided_dssum_ndarray)( N, X, strideX, offsetX );
 	mu /= dN;
 
 	// Compute an error term:
-	c = stdlib_strided_dsapxsum_ndarray( N, -mu, X, strideX, offsetX );
+	c = API_SUFFIX(stdlib_strided_dsapxsum_ndarray)( N, -mu, X, strideX, offsetX );
 	c /= dN;
 
 	return mu + c;

lib/node_modules/@stdlib/stats/strided/dsvariancepn/src/main.c

@@ -73,7 +73,7 @@ double API_SUFFIX(stdlib_strided_dsvariancepn_ndarray)( const CBLAS_INT N, const
 		return 0.0;
 	}
 	// Compute an estimate for the mean:
-	mu = stdlib_strided_dssum_ndarray( N, X, strideX, offsetX ) / dN;
+	mu = API_SUFFIX(stdlib_strided_dssum_ndarray)( N, X, strideX, offsetX ) / dN;
 
 	// Compute the variance...
 	ix = offsetX;

lib/node_modules/@stdlib/stats/strided/dztest/src/main.c

@@ -98,7 +98,7 @@ void API_SUFFIX(stdlib_strided_dztest_ndarray)( const CBLAS_INT N, const enum ST
 	stderr = sigma / stdlib_base_sqrt( (double)N );
 
 	// Compute the arithmetic mean of the input array:
-	xmean = stdlib_strided_dmean_ndarray( N, X, strideX, offsetX );
+	xmean = API_SUFFIX(stdlib_strided_dmean_ndarray)( N, X, strideX, offsetX );
 
 	// Compute the test statistic (i.e., the z-score, which is the distance of the sample mean from the population mean in units of standard error):
 	stat = ( xmean - mu ) / stderr;

lib/node_modules/@stdlib/stats/strided/dztest2/src/main.c

@@ -117,8 +117,8 @@ void API_SUFFIX(stdlib_strided_dztest2_ndarray)( const CBLAS_INT NX, const CBLAS
 	stderr = stdlib_base_sqrt( ( xvar / (double)NX ) + ( yvar / (double)NY ) );
 
 	// Compute the arithmetic means of the input arrays:
-	xmean = stdlib_strided_dmean_ndarray( NX, X, strideX, offsetX );
-	ymean = stdlib_strided_dmean_ndarray( NY, Y, strideY, offsetY );
+	xmean = API_SUFFIX(stdlib_strided_dmean_ndarray)( NX, X, strideX, offsetX );
+	ymean = API_SUFFIX(stdlib_strided_dmean_ndarray)( NY, Y, strideY, offsetY );
 
 	// Compute the test statistic (i.e., the z-score, which is the standardized difference between the sample means of x and y, adjusted by the hypothesized difference, in units of the standard error):
 	stat = ( xmean - ymean - diff ) / stderr;

lib/node_modules/@stdlib/stats/strided/sztest/src/main.c

@@ -98,7 +98,7 @@ void API_SUFFIX(stdlib_strided_sztest_ndarray)( const CBLAS_INT N, const enum ST
 	stderr = sigma / stdlib_base_sqrt( (double)N ); // note: intentionally evaluated in double-precision to avoid `N` exceeding max safe float32 integer
 
 	// Compute the arithmetic mean of the input array:
-	xmean = stdlib_strided_smean_ndarray( N, X, strideX, offsetX );
+	xmean = API_SUFFIX(stdlib_strided_smean_ndarray)( N, X, strideX, offsetX );
 
 	// Compute the test statistic (i.e., the z-score, which is the distance of the sample mean from the population mean in units of standard error):
 	stat = ( (double)xmean - (double)mu ) / stderr;

lib/node_modules/@stdlib/stats/strided/sztest2/src/main.c

@@ -117,8 +117,8 @@ void API_SUFFIX(stdlib_strided_sztest2_ndarray)( const CBLAS_INT NX, const CBLAS
 	stderr = stdlib_base_sqrt( ( xvar / (double)NX ) + ( yvar / (double)NY ) ); // note: intentionally evaluated in double-precision to avoid `NX` and `NY` exceeding max safe float32 integer
 
 	// Compute the arithmetic means of the input arrays:
-	xmean = stdlib_strided_smean_ndarray( NX, X, strideX, offsetX );
-	ymean = stdlib_strided_smean_ndarray( NY, Y, strideY, offsetY );
+	xmean = API_SUFFIX(stdlib_strided_smean_ndarray)( NX, X, strideX, offsetX );
+	ymean = API_SUFFIX(stdlib_strided_smean_ndarray)( NY, Y, strideY, offsetY );
 
 	// Compute the test statistic (i.e., the z-score, which is the standardized difference between the sample means of x and y, adjusted by the hypothesized difference, in units of the standard error):
 	stat = ( (double)xmean - (double)ymean - (double)diff ) / stderr;