npm package: @stdlib/stats-base-ndarray-dminabs
Description: Compute the minimum absolute value of a one-dimensional double-precision floating-point ndarray.
Author: The Stdlib Authors
Latest version: 0.1.1

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# dminabs

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> Compute the minimum absolute value of a one-dimensional double-precision floating-point ndarray.

<section class="intro">

</section>

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<section class="installation">

## Installation

```bash
npm install @stdlib/stats-base-ndarray-dminabs
```

</section>

<section class="usage">

## Usage

```javascript
var dminabs = require( '@stdlib/stats-base-ndarray-dminabs' );
```

#### dminabs( arrays )

Computes the minimum absolute value of a one-dimensional double-precision floating-point ndarray.

```javascript
var Float64Array = require( '@stdlib/array-float64' );
var ndarray = require( '@stdlib/ndarray-base-ctor' );

var xbuf = new Float64Array( [ -1.0, 3.0, -4.0, 2.0 ] );
var x = new ndarray( 'float64', xbuf, [ 4 ], [ 1 ], 0, 'row-major' );

var v = dminabs( [ x ] );
// returns 1.0
```

The function has the following parameters:

-   **arrays**: array-like object containing a one-dimensional input ndarray.

</section>

<!-- /.usage -->

<section class="notes">

## Notes

-   If provided an empty one-dimensional ndarray, the function returns `NaN`.

</section>

<!-- /.notes -->

<section class="examples">

## Examples

<!-- eslint no-undef: "error" -->

```javascript
var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var ndarray = require( '@stdlib/ndarray-base-ctor' );
var ndarray2array = require( '@stdlib/ndarray-to-array' );
var dminabs = require( '@stdlib/stats-base-ndarray-dminabs' );

var xbuf = discreteUniform( 10, -50, 50, {
    'dtype': 'float64'
});
var x = new ndarray( 'float64', xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );
console.log( ndarray2array( x ) );

var v = dminabs( [ x ] );
console.log( v );
```

</section>

<!-- /.examples -->

<!-- C interface documentation. -->

* * *

<section class="c">

## C APIs

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<section class="intro">

</section>

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<!-- C usage documentation. -->

<section class="usage">

### Usage

```c
#include "stdlib/stats/base/ndarray/dminabs.h"
```

#### stdlib_stats_dminabs( arrays )

Computes the minimum absolute value of a one-dimensional double-precision floating-point ndarray.

```c
#include "stdlib/ndarray/ctor.h"
#include "stdlib/ndarray/dtypes.h"
#include "stdlib/ndarray/index_modes.h"
#include "stdlib/ndarray/orders.h"
#include "stdlib/ndarray/base/bytes_per_element.h"
#include <stdint.h>

// Create an ndarray:
const double data[] = { 1.0, 2.0, 3.0, 4.0 };
int64_t shape[] = { 4 };
int64_t strides[] = { STDLIB_NDARRAY_FLOAT64_BYTES_PER_ELEMENT };
int8_t submodes[] = { STDLIB_NDARRAY_INDEX_ERROR };

struct ndarray *x = stdlib_ndarray_allocate( STDLIB_NDARRAY_FLOAT64, (uint8_t *)data, 1, shape, strides, 0, STDLIB_NDARRAY_ROW_MAJOR, STDLIB_NDARRAY_INDEX_ERROR, 1, submodes );

// Compute the minimum absolute value:
const struct ndarray *arrays[] = { x };
double v = stdlib_stats_dminabs( arrays );
// returns 1.0

// Free allocated memory:
stdlib_ndarray_free( x );
```

The function accepts the following arguments:

-   **arrays**: `[in] struct ndarray**` list containing a one-dimensional input ndarray.

```c
double stdlib_stats_dminabs( const struct ndarray *arrays[] );
```

</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/stats/base/ndarray/dminabs.h"
#include "stdlib/ndarray/ctor.h"
#include "stdlib/ndarray/dtypes.h"
#include "stdlib/ndarray/index_modes.h"
#include "stdlib/ndarray/orders.h"
#include "stdlib/ndarray/base/bytes_per_element.h"
#include <stdint.h>
#include <stdlib.h>
#include <stdio.h>

int main( void ) {
   // Create a data buffer:
   const double data[] = { 1.0, -2.0, 3.0, -4.0, 5.0, -6.0, 7.0, -8.0 };

   // Specify the number of array dimensions:
   const int64_t ndims = 1;

   // Specify the array shape:
   int64_t shape[] = { 4 };

   // Specify the array strides:
   int64_t strides[] = { 2*STDLIB_NDARRAY_FLOAT64_BYTES_PER_ELEMENT };

   // Specify the byte offset:
   const int64_t offset = 0;

   // Specify the array order:
   const enum STDLIB_NDARRAY_ORDER order = STDLIB_NDARRAY_ROW_MAJOR;

   // Specify the index mode:
   const enum STDLIB_NDARRAY_INDEX_MODE imode = STDLIB_NDARRAY_INDEX_ERROR;

   // Specify the subscript index modes:
   int8_t submodes[] = { STDLIB_NDARRAY_INDEX_ERROR };
   const int64_t nsubmodes = 1;

   // Create an ndarray:
   struct ndarray *x = stdlib_ndarray_allocate( STDLIB_NDARRAY_FLOAT64, (uint8_t *)data, ndims, shape, strides, offset, order, imode, nsubmodes, submodes );
   if ( x == NULL ) {
      fprintf( stderr, "Error allocating memory.\n" );
      exit( 1 );
   }

   // Define a list of ndarrays:
   const struct ndarray *arrays[] = { x };

   // Compute the minimum absolute value:
   double v = stdlib_stats_dminabs( arrays );

   // Print the result:
   printf( "min: %lf\n", v );

   // Free allocated memory:
   stdlib_ndarray_free( x );
}
```

</section>

<!-- /.examples -->

</section>

<!-- /.c -->

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<section class="related">

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* * *

## Notice

This package is part of [stdlib][stdlib], a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

For more information on the project, filing bug reports and feature requests, and guidance on how to develop [stdlib][stdlib], see the main project [repository][stdlib].

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## License

See [LICENSE][stdlib-license].


## Copyright

Copyright &copy; 2016-2026. The Stdlib [Authors][stdlib-authors].

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