text: /**
  * @license Apache-2.0
  *
  * Copyright (c) 2020 The Stdlib Authors.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
  * You may obtain a copy of the License at
  *
  *    http://www.apache.org/licenses/LICENSE-2.0
  *
  * Unless required by applicable law or agreed to in writing, software
  * distributed under the License is distributed on an "AS IS" BASIS,
  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
  
  'use strict';
  
  // MAIN //
  
  /**
  * Computes the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values, using a two-pass error correction algorithm with extended accumulation, and returning an extended precision result.
  *
  * ## Method
  *
  * -   This implementation uses a two-pass approach, as suggested by Neely (1966).
  *
  * ## 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](https://doi.org/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](https://doi.org/10.1145/3221269.3223036).
  *
  * @param {PositiveInteger} N - number of indexed elements
  * @param {Float32Array} x - input array
  * @param {integer} stride - stride length
  * @param {NonNegativeInteger} offset - starting index
  * @returns {number} arithmetic mean
  *
  * @example
  * var Float32Array = require( '@stdlib/array-float32' );
  * var floor = require( '@stdlib/math-base-special-floor' );
  *
  * var x = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ] );
  * var N = floor( x.length / 2 );
  *
  * var v = dsnanmeanpn( N, x, 2, 1 );
  * // returns 1.25
  */
  function dsnanmeanpn( N, x, stride, offset ) {
  	var ix;
  	var v;
  	var s;
  	var t;
  	var n;
  	var i;
  
  	if ( N <= 0 ) {
  		return NaN;
  	}
  	if ( N === 1 || stride === 0 ) {
  		return x[ offset ];
  	}
  	ix = offset;
  
  	// Compute an estimate for the mean...
  	s = 0.0;
  	n = 0;
  	for ( i = 0; i < N; i++ ) {
  		v = x[ ix ];
  		if ( v === v ) {
  			n += 1;
  			s += v;
  		}
  		ix += stride;
  	}
  	if ( n === 0 ) {
  		return NaN;
  	}
  	s /= n;
  
  	// Compute an error term...
  	ix = offset;
  	t = 0.0;
  	for ( i = 0; i < N; i++ ) {
  		v = x[ ix ];
  		if ( v === v ) {
  			t += v - s;
  		}
  		ix += stride;
  	}
  	return s + (t/n);
  }
  
  
  // EXPORTS //
  
  module.exports = dsnanmeanpn;
  