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 variance of a strided array ignoring `NaN` values and using Welford's algorithm.
  *
  * ## References
  *
  * -   Welford, B. P. 1962. "Note on a Method for Calculating Corrected Sums of Squares and Products." _Technometrics_ 4 (3). Taylor & Francis: 419–20. doi:[10.1080/00401706.1962.10490022](https://doi.org/10.1080/00401706.1962.10490022).
  * -   van Reeken, A. J. 1968. "Letters to the Editor: Dealing with Neely's Algorithms." _Communications of the ACM_ 11 (3): 149–50. doi:[10.1145/362929.362961](https://doi.org/10.1145/362929.362961).
  *
  * @param {PositiveInteger} N - number of indexed elements
  * @param {number} correction - degrees of freedom adjustment
  * @param {NumericArray} x - input array
  * @param {integer} stride - stride length
  * @param {NonNegativeInteger} offset - starting index
  * @returns {number} variance
  *
  * @example
  * var floor = require( '@stdlib/math-base-special-floor' );
  *
  * var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN, NaN ];
  * var N = floor( x.length / 2 );
  *
  * var v = nanvariancewd( N, 1, x, 2, 1 );
  * // returns 6.25
  */
  function nanvariancewd( N, correction, x, stride, offset ) {
  	var delta;
  	var mu;
  	var M2;
  	var ix;
  	var nc;
  	var v;
  	var n;
  	var i;
  
  	if ( N <= 0 ) {
  		return NaN;
  	}
  	if ( N === 1 || stride === 0 ) {
  		v = x[ offset ];
  		if ( v === v && N-correction > 0.0 ) {
  			return 0.0;
  		}
  		return NaN;
  	}
  	ix = offset;
  	M2 = 0.0;
  	mu = 0.0;
  	n = 0;
  	for ( i = 0; i < N; i++ ) {
  		v = x[ ix ];
  		if ( v === v ) {
  			delta = v - mu;
  			n += 1;
  			mu += delta / n;
  			M2 += delta * ( v - mu );
  		}
  		ix += stride;
  	}
  	nc = n - correction;
  	if ( nc <= 0.0 ) {
  		return NaN;
  	}
  	return M2 / nc;
  }
  
  
  // EXPORTS //
  
  module.exports = nanvariancewd;
  