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meanpn
Calculate the arithmetic mean of a strided array using a twopass error correction algorithm.
The arithmetic mean is defined as
Installation
npm install @stdlib/statsbasemeanpn
Usage
var meanpn = require( '@stdlib/statsbasemeanpn' );
meanpn( N, x, stride )
Computes the arithmetic mean of a strided array x
using a twopass error correction algorithm.
var x = [ 1.0, 2.0, 2.0 ];
var N = x.length;
var v = meanpn( N, x, 1 );
// returns ~0.3333
The function has the following parameters:
 N: number of indexed elements.

x: input
Array
ortyped array
. 
stride: index increment for
x
.
The N
and stride
parameters determine which elements in x
are accessed at runtime. For example, to compute the arithmetic mean of every other element in x
,
var floor = require( '@stdlib/mathbasespecialfloor' );
var x = [ 1.0, 2.0, 2.0, 7.0, 2.0, 3.0, 4.0, 2.0 ];
var N = floor( x.length / 2 );
var v = meanpn( N, x, 2 );
// returns 1.25
Note that indexing is relative to the first index. To introduce an offset, use typed array
views.
var Float64Array = require( '@stdlib/arrayfloat64' );
var floor = require( '@stdlib/mathbasespecialfloor' );
var x0 = new Float64Array( [ 2.0, 1.0, 2.0, 2.0, 2.0, 2.0, 3.0, 4.0 ] );
var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var N = floor( x0.length / 2 );
var v = meanpn( N, x1, 2 );
// returns 1.25
meanpn.ndarray( N, x, stride, offset )
Computes the arithmetic mean of a strided array using a twopass error correction algorithm and alternative indexing semantics.
var x = [ 1.0, 2.0, 2.0 ];
var N = x.length;
var v = meanpn.ndarray( N, x, 1, 0 );
// returns ~0.33333
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 arithmetic mean for every other value in x
starting from the second value
var floor = require( '@stdlib/mathbasespecialfloor' );
var x = [ 2.0, 1.0, 2.0, 2.0, 2.0, 2.0, 3.0, 4.0 ];
var N = floor( x.length / 2 );
var v = meanpn.ndarray( N, x, 2, 1 );
// returns 1.25
Notes
Examples
var randu = require( '@stdlib/randombaserandu' );
var round = require( '@stdlib/mathbasespecialround' );
var Float64Array = require( '@stdlib/arrayfloat64' );
var meanpn = require( '@stdlib/statsbasemeanpn' );
var x;
var i;
x = new Float64Array( 10 );
for ( i = 0; i < x.length; i++ ) {
x[ i ] = round( (randu()*100.0)  50.0 );
}
console.log( x );
var v = meanpn( x.length, x, 1 );
console.log( v );
References
 Neely, Peter M. 1966. "Comparison of Several Algorithms for Computation of Means, Standard Deviations and Correlation Coefficients." Communications of the ACM 9 (7). Association for Computing Machinery: 496–99. doi:10.1145/365719.365958.
 Schubert, Erich, and Michael Gertz. 2018. "Numerically Stable Parallel Computation of (Co)Variance." In Proceedings of the 30th International Conference on Scientific and Statistical Database Management. New York, NY, USA: Association for Computing Machinery. doi:10.1145/3221269.3223036.
See Also

@stdlib/statsbase/dmeanpn
: calculate the arithmetic mean of a doubleprecision floatingpoint strided array using a twopass error correction algorithm. 
@stdlib/statsbase/mean
: calculate the arithmetic mean of a strided array. 
@stdlib/statsbase/nanmeanpn
: calculate the arithmetic mean of a strided array, ignoring NaN values and using a twopass error correction algorithm. 
@stdlib/statsbase/smeanpn
: calculate the arithmetic mean of a singleprecision floatingpoint strided array using a twopass error correction algorithm.
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
License
See LICENSE.
Copyright
Copyright © 20162023. The Stdlib Authors.