# @stdlib/stats-base-dnanmin Release 0.0.7

Calculate the minimum value of a double-precision floating-point strided array, ignoring NaN values.

Keywords
stdlib, stdmath, statistics, stats, mathematics, math, minimum, min, range, extremes, domain, extent, strided, strided array, typed, array, float64, double, float64array, javascript, node, node-js, nodejs, strided-array
Apache-2.0
Install
``` npm install @stdlib/stats-base-dnanmin@0.0.7 ```

# dnanmin

Calculate the minimum value of a double-precision floating-point strided array, ignoring `NaN` values.

## Installation

`npm install @stdlib/stats-base-dnanmin`

## Usage

`var dnanmin = require( '@stdlib/stats-base-dnanmin' );`

#### dnanmin( N, x, stride )

Computes the minimum value of a double-precision floating-point strided array `x`, ignoring `NaN` values.

```var Float64Array = require( '@stdlib/array-float64' );

var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );
var N = x.length;

var v = dnanmin( N, x, 1 );
// returns -2.0```

The function has the following parameters:

The `N` and `stride` parameters determine which elements in `x` are accessed at runtime. For example, to compute the minimum value of every other element in `x`,

```var Float64Array = require( '@stdlib/array-float64' );
var floor = require( '@stdlib/math-base-special-floor' );

var x = new Float64Array( [ 1.0, 2.0, -7.0, -2.0, 4.0, 3.0, NaN, NaN ] );
var N = floor( x.length / 2 );

var v = dnanmin( N, x, 2 );
// returns -7.0```

Note that indexing is relative to the first index. To introduce an offset, use `typed array` views.

```var Float64Array = require( '@stdlib/array-float64' );
var floor = require( '@stdlib/math-base-special-floor' );

var x0 = new Float64Array( [ 2.0, 1.0, -2.0, -2.0, 3.0, 4.0, NaN, NaN ] );
var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

var N = floor( x0.length / 2 );

var v = dnanmin( N, x1, 2 );
// returns -2.0```

#### dnanmin.ndarray( N, x, stride, offset )

Computes the minimum value of a double-precision floating-point strided array, ignoring `NaN` values and using alternative indexing semantics.

```var Float64Array = require( '@stdlib/array-float64' );

var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );
var N = x.length;

var v = dnanmin.ndarray( N, x, 1, 0 );
// returns -2.0```

The function has the following additional parameters:

• offset: starting index for `x`.

While `typed array` views mandate a view offset based on the underlying `buffer`, the `offset` parameter supports indexing semantics based on a starting index. For example, to calculate the minimum value for every other value in `x` starting from the second value

```var Float64Array = require( '@stdlib/array-float64' );
var floor = require( '@stdlib/math-base-special-floor' );

var x = new Float64Array( [ 2.0, 1.0, -2.0, -2.0, 3.0, 4.0, NaN, NaN ] );
var N = floor( x.length / 2 );

var v = dnanmin.ndarray( N, x, 2, 1 );
// returns -2.0```

## Notes

• If `N <= 0`, both functions return `NaN`.

## Examples

```var randu = require( '@stdlib/random-base-randu' );
var round = require( '@stdlib/math-base-special-round' );
var Float64Array = require( '@stdlib/array-float64' );
var dnanmin = require( '@stdlib/stats-base-dnanmin' );

var x;
var i;

x = new Float64Array( 10 );
for ( i = 0; i < x.length; i++ ) {
if ( randu() < 0.2 ) {
x[ i ] = NaN;
} else {
x[ i ] = round( (randu()*100.0) - 50.0 );
}
}
console.log( x );

var v = dnanmin( x.length, x, 1 );
console.log( v );```

## Notice

This package is part of 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, see the main project repository.

#### Community 