diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/docs/types/index.d.ts b/lib/node_modules/@stdlib/stats/base/ndarray/docs/types/index.d.ts
index ae0e35c635c8..bfabec9bee7e 100644
--- a/lib/node_modules/@stdlib/stats/base/ndarray/docs/types/index.d.ts
+++ b/lib/node_modules/@stdlib/stats/base/ndarray/docs/types/index.d.ts
@@ -80,6 +80,11 @@ import dstdevtk = require( '@stdlib/stats/base/ndarray/dstdevtk' );
 import dstdevwd = require( '@stdlib/stats/base/ndarray/dstdevwd' );
 import dstdevyc = require( '@stdlib/stats/base/ndarray/dstdevyc' );
 import dvariance = require( '@stdlib/stats/base/ndarray/dvariance' );
+import dvariancech = require( '@stdlib/stats/base/ndarray/dvariancech' );
+import dvariancepn = require( '@stdlib/stats/base/ndarray/dvariancepn' );
+import dvariancetk = require( '@stdlib/stats/base/ndarray/dvariancetk' );
+import dvariancewd = require( '@stdlib/stats/base/ndarray/dvariancewd' );
+import dvarianceyc = require( '@stdlib/stats/base/ndarray/dvarianceyc' );
 import dztest = require( '@stdlib/stats/base/ndarray/dztest' );
 import dztest2 = require( '@stdlib/stats/base/ndarray/dztest2' );
 import max = require( '@stdlib/stats/base/ndarray/max' );
@@ -1694,6 +1699,151 @@ interface Namespace {
 	*/
 	dvariance: typeof dvariance;
 
+	/**
+	* Computes the variance of a one-dimensional double-precision floating-point ndarray using a one-pass trial mean algorithm.
+	*
+	* ## Notes
+	*
+	* -   The function expects the following ndarrays:
+	*
+	*     -   a one-dimensional input ndarray.
+	*     -   a zero-dimensional ndarray specifying the degrees of freedom adjustment.
+	*
+	* @param arrays - array-like object containing ndarrays
+	* @returns variance
+	*
+	* @example
+	* var Float64Vector = require( '@stdlib/ndarray/vector/float64' );
+	* var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+	*
+	* var opts = {
+	*     'dtype': 'float64'
+	* };
+	*
+	* var x = new Float64Vector( [ 1.0, -2.0, 2.0 ] );
+	* var correction = scalar2ndarray( 1.0, opts );
+	*
+	* var v = ns.dvariancech( [ x, correction ] );
+	* // returns ~4.3333
+	*/
+	dvariancech: typeof dvariancech;
+
+	/**
+	* Computes the variance of a one-dimensional double-precision floating-point ndarray using a two-pass algorithm.
+	*
+	* ## Notes
+	*
+	* -   The function expects the following ndarrays:
+	*
+	*     -   a one-dimensional input ndarray.
+	*     -   a zero-dimensional ndarray specifying the degrees of freedom adjustment.
+	*
+	* @param arrays - array-like object containing ndarrays
+	* @returns variance
+	*
+	* @example
+	* var Float64Vector = require( '@stdlib/ndarray/vector/float64' );
+	* var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+	*
+	* var opts = {
+	*     'dtype': 'float64'
+	* };
+	*
+	* var x = new Float64Vector( [ 1.0, -2.0, 2.0 ] );
+	* var correction = scalar2ndarray( 1.0, opts );
+	*
+	* var v = ns.dvariancepn( [ x, correction ] );
+	* // returns ~4.3333
+	*/
+	dvariancepn: typeof dvariancepn;
+
+	/**
+	* Computes the variance of a one-dimensional double-precision floating-point ndarray using a one-pass textbook algorithm.
+	*
+	* ## Notes
+	*
+	* -   The function expects the following ndarrays:
+	*
+	*     -   a one-dimensional input ndarray.
+	*     -   a zero-dimensional ndarray specifying the degrees of freedom adjustment.
+	*
+	* @param arrays - array-like object containing ndarrays
+	* @returns variance
+	*
+	* @example
+	* var Float64Vector = require( '@stdlib/ndarray/vector/float64' );
+	* var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+	*
+	* var opts = {
+	*     'dtype': 'float64'
+	* };
+	*
+	* var x = new Float64Vector( [ 1.0, -2.0, 2.0 ] );
+	* var correction = scalar2ndarray( 1.0, opts );
+	*
+	* var v = ns.dvariancetk( [ x, correction ] );
+	* // returns ~4.3333
+	*/
+	dvariancetk: typeof dvariancetk;
+
+	/**
+	* Computes the variance of a one-dimensional double-precision floating-point ndarray using Welford's algorithm.
+	*
+	* ## Notes
+	*
+	* -   The function expects the following ndarrays:
+	*
+	*     -   a one-dimensional input ndarray.
+	*     -   a zero-dimensional ndarray specifying the degrees of freedom adjustment.
+	*
+	* @param arrays - array-like object containing ndarrays
+	* @returns variance
+	*
+	* @example
+	* var Float64Vector = require( '@stdlib/ndarray/vector/float64' );
+	* var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+	*
+	* var opts = {
+	*     'dtype': 'float64'
+	* };
+	*
+	* var x = new Float64Vector( [ 1.0, -2.0, 2.0 ] );
+	* var correction = scalar2ndarray( 1.0, opts );
+	*
+	* var v = ns.dvariancewd( [ x, correction ] );
+	* // returns ~4.3333
+	*/
+	dvariancewd: typeof dvariancewd;
+
+	/**
+	* Computes the variance of a one-dimensional double-precision floating-point ndarray using a one-pass algorithm proposed by Youngs and Cramer.
+	*
+	* ## Notes
+	*
+	* -   The function expects the following ndarrays:
+	*
+	*     -   a one-dimensional input ndarray.
+	*     -   a zero-dimensional ndarray specifying the degrees of freedom adjustment.
+	*
+	* @param arrays - array-like object containing ndarrays
+	* @returns variance
+	*
+	* @example
+	* var Float64Vector = require( '@stdlib/ndarray/vector/float64' );
+	* var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+	*
+	* var opts = {
+	*     'dtype': 'float64'
+	* };
+	*
+	* var x = new Float64Vector( [ 1.0, -2.0, 2.0 ] );
+	* var correction = scalar2ndarray( 1.0, opts );
+	*
+	* var v = ns.dvarianceyc( [ x, correction ] );
+	* // returns ~4.3333
+	*/
+	dvarianceyc: typeof dvarianceyc;
+
 	/**
 	* Computes a one-sample Z-test for a one-dimensional double-precision floating-point ndarray.
 	*