diff --git a/lib/node_modules/@stdlib/ml/cluster/strided/dkmeansld/lib/closest_centroid.js b/lib/node_modules/@stdlib/ml/cluster/strided/dkmeansld/lib/closest_centroid.js
new file mode 100644
index 000000000000..1e0cc282cad3
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/cluster/strided/dkmeansld/lib/closest_centroid.js
@@ -0,0 +1,62 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2026 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';
+
+// MODULES //
+
+
+// MAIN //
+
+/**
+* Find closest centroid.
+* @private
+* @param {Function} dist - distance method.
+* @param {PositiveInteger} N - number of features.
+* @param {PositiveInteger} k - number of clusters.
+* @param {Float64Array} X - input strided matrix.
+* @param {integer} sx - stride length of X.
+* @param {integer} ox - starting index of X.
+* @param {Float64Array} c - strided array centroid locations.
+* @param {integer} sc1 - stride of the third dimension.
+* @param {integer} sc2 - stride of second dimension.
+* @param {integer} oc - initial index of centroids.
+* @returns {NonNegativeInteger} closest centroid.
+*/
+function closestCentroid( dist, N, k, X, sx, ox, c, sc1, sc2, oc ) {
+	var bestDist;
+	var best;
+	var d;
+	var i;
+
+	best = 0;
+	bestDist = dist( N, X, sx, ox, c, sc2, oc );
+
+	oc += sc1; // move to the next centroid
+	for ( i = 1; i < k; i++ ) {
+		d = dist( N, X, sx, ox, c, sc2, oc );
+		if ( d < bestDist ) {
+			bestDist = d;
+			best = i;
+		}
+		oc += sc1;
+	}
+	return best;
+}
+
+module.exports = closestCentroid;
diff --git a/lib/node_modules/@stdlib/ml/cluster/strided/dkmeansld/lib/compute_inertia.js b/lib/node_modules/@stdlib/ml/cluster/strided/dkmeansld/lib/compute_inertia.js
new file mode 100644
index 000000000000..058f9b27b881
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/cluster/strided/dkmeansld/lib/compute_inertia.js
@@ -0,0 +1,64 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2026 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';
+
+// MODULES //
+
+var drss = require( '@stdlib/blas/ext/base/drss' ).ndarray;
+
+
+// MAIN //
+
+/**
+* Compute inertia.
+* @private
+* @param {PositiveInteger} M - number of samples.
+* @param {PositiveInteger} N - number of features.
+* @param {Float64Array} X - input strided matrix.
+* @param {integer} strideX1 - stride length of first dimension of X.
+* @param {integer} strideX2 - stride length of second dimension of X.
+* @param {integer} offsetX - starting index of X.
+* @param {Float64Array} centroids - strided array centroid locations.
+* @param {integer} strideC1 - stride length of first dimension of c.
+* @param {integer} strideC2 - stride length of second dimension of c.
+* @param {integer} offsetC - initial index of centroids.
+* @param {Int32Array} labels - labels array.
+* @returns {number} inertia.
+*/
+function computeInertia( M, N, X, strideX1, strideX2, offsetX, centroids, strideC1, strideC2, offsetC, labels ) { // eslint-disable-line max-len, max-params
+	var inertia;
+	var ox;
+	var oc;
+	var d;
+	var c;
+	var i;
+
+	inertia = 0.0;
+	ox = offsetX;
+	for ( i = 0; i < M; i++ ) {
+		c = labels[ i ];
+		oc = offsetC + ( c * strideC1 );
+		d = drss( N, X, strideX2, ox, centroids, strideC2, oc );
+		inertia += d;
+		ox += strideX1;
+	}
+	return inertia;
+}
+
+module.exports = computeInertia;
diff --git a/lib/node_modules/@stdlib/ml/cluster/strided/dkmeansld/lib/index.js b/lib/node_modules/@stdlib/ml/cluster/strided/dkmeansld/lib/index.js
new file mode 100644
index 000000000000..d8a0c5ecc49e
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/cluster/strided/dkmeansld/lib/index.js
@@ -0,0 +1,40 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2026 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';
+
+/**
+* Compute fitted cluster results using Lloyd algorithm.
+*
+* @module @stdlib/ml/cluster/strided/dkmeansld
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+* var ndarray = require( '@stdlib/ndarray/ctor' );
+* var kmeans = require( '@stdlib/ml/cluster/strided/dkmeansld' );
+*
+*/
+
+// MAIN //
+
+var main = require( './main.js' );
+
+
+// EXPORTS //
+
+module.exports = main;
diff --git a/lib/node_modules/@stdlib/ml/cluster/strided/dkmeansld/lib/main.js b/lib/node_modules/@stdlib/ml/cluster/strided/dkmeansld/lib/main.js
new file mode 100644
index 000000000000..3a9915150b91
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/cluster/strided/dkmeansld/lib/main.js
@@ -0,0 +1,239 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2026 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';
+
+// MODULES //
+
+var Int32Array = require( '@stdlib/array/int32' );
+var Float64Array = require( '@stdlib/array/float64' );
+var isEqualArray = require( '@stdlib/assert/is-equal-array' );
+var gfill = require( '@stdlib/blas/ext/base/gfill' ).ndarray;
+var gcopy = require( '@stdlib/blas/base/gcopy' ).ndarray;
+var dlacpy = require( '@stdlib/lapack/base/dlacpy' ).ndarray;
+var isnan = require( '@stdlib/assert/is-nan' );
+var deuclidean = require( '@stdlib/stats/strided/distances/deuclidean' ).ndarray;
+var dcosine = require( '@stdlib/stats/strided/distances/dcosine-distance' ).ndarray;
+var dcityblock = require( '@stdlib/stats/strided/distances/dcityblock' ).ndarray;
+var closestCentroid = require( './closest_centroid.js' );
+var computeInertia = require( './compute_inertia.js' );
+
+
+// VARIABLES //
+
+var METHOD = 'lloyd';
+
+
+// MAIN //
+
+/**
+* Compute fitted cluster results using Lloyd algorithm.
+* @param {PositiveInteger} M - number of samples
+* @param {PositiveInteger} N - number of features
+* @param {PositiveInteger} k - number of clusters
+* @param {NonNegativeInteger} replicates - number of times to repeat clustering with different centroids.
+* @param {String} metric - distance metric
+* @param {NonNegativeInteger} maxIter - maximum number of iterations.
+* @param {integer} tol - relative tolerance before declaring convergence.
+* @param {Float64Array} X - input strided matrix
+* @param {integer} strideX1 - stride of the first dimension.
+* @param {integer} strideX2 - stride of the second dimension.
+* @param {integer} offsetX - starting index.
+* @param {Float64Array} init - strided array containing initial centroid locations.
+* @param {integer} strideInit1 - stride of first dimension.
+* @param {integer} strideInit2 - stride of second dimension.
+* @param {integer} strideInit3 - stride of the third dimension.
+* @param {integer} offsetInit - initial index.
+* @param {Float64Array} bestCentroids - strided array buffer for final centroid locations.
+* @param {integer} strideC1 - stride of first dimensions of C.
+* @param {integer} strideC2 - stride of second dimensions of C.
+* @param {integer} offsetC - initial index.
+* @param {Float64Array} statistics - strided array buffer for cluster stats.
+* @param {integer} strideS1 - stride of first dimensions of S.
+* @param {integer} strideS2 - stride of second dimensions of S.
+* @param {integer} offsetS - initial index.
+* @param {Int32Array} bestLabels - strided array buffer for sample labels.
+* @param {integer} strideL - stride of first dimensions of labels.
+* @param {integer} offsetL - stride of second dimensions of labels.
+* @param {Object} out - output results object.
+*/
+function dkmeansld( M, N, k, replicates, metric, maxIter, tol, X, strideX1, strideX2, offsetX, init, strideInit1, strideInit2, strideInit3, offsetInit, bestCentroids, strideC1, strideC2, offsetC, statistics, strideS1, strideS2, offsetS, bestLabels, strideL, offsetL, out ) { // eslint-disable-line max-len, max-params, max-statements
+	var centroidsNew;
+	var bestInertia;
+	var strictConv;
+	var labelsOld;
+	var centroids;
+	var bestIter;
+	var inertia;
+	var labels;
+	var counts;
+	var bestR;
+	var shift;
+	var dist;
+	var best;
+	var iter;
+	var ocj;
+	var oij;
+	var oxj;
+	var si;
+	var oc;
+	var ox;
+	var oi;
+	var r;
+	var i;
+	var j;
+	var c;
+	var d;
+
+	if ( metric === 'euclidean' ) {
+		dist = deuclidean; // TODO: change it to dsquared-euclidean once implemented
+	} else if ( metric === 'cosine' ) {
+		dist = dcosine;    // TODO: change it to dsquared-cosine once implemented
+	} else if ( metric === 'cityblock' ) {
+		dist = dcityblock;
+	}
+
+	labelsOld = new Int32Array( M );
+	labels = new Int32Array( M );
+	counts = new Float64Array( k ); // q: sklearn supports sample_weights, should we do the same? if yes, change it to Float64Array
+	centroidsNew = new Float64Array( k*N );
+	centroids = new Float64Array( k*N );
+
+	oi = offsetInit; // start from the offset
+	bestInertia = NaN;
+	for ( r = 0; r < replicates; r++ ) {
+		gfill( M, -1, labels, 1, 0 );
+		gfill( M, -1, labelsOld, 1, 0 );
+
+		// initCentroids( N, k, X, init, si1, strideInit2, strideInit3, offsetInit ); // TODO
+
+		strictConv = false;
+		for ( iter = 1; iter <= maxIter; iter++ ) {
+			gfill( k, 0, counts, 1, 0 ); // How do I fill it with int32 values?
+			gfill( k*N, 0, centroidsNew, 1, 0 );
+
+			ox = offsetX;
+			for ( i = 0; i < M; i++ ) {
+				best = closestCentroid( dist, N, k, X, strideX2, ox, init, strideInit2, strideInit3, oi ); // eslint-disable-line max-len
+
+				labels[ i ] = best;
+				counts[ best ] += 1;
+				ox += strideX1; // move to the next sample
+			}
+
+			ox = offsetX;
+			for ( i = 0; i < M; i++ ) {
+				c = labels[ i ];
+				oc = c * N; // move to starting position of respective centroid in buf
+				oxj = ox;
+				for ( j = 0; j < N; j++ ) {
+					centroidsNew[ oc ] += X[ oxj ]; // accumulate sum of each sample in the cluster
+					oc += 1; // move to the next feature of the respective centroid
+					oxj += strideX2; // move to the next feature of the respective sample
+				}
+				ox += strideX1;
+			}
+
+			oc = 0;
+			si = offsetInit;
+			for ( c = 0; c < k; c++ ) {
+				if ( counts[ c ] > 0 ) {
+					ocj = oc;
+					for ( j = 0; j < N; j++ ) {
+						centroidsNew[ ocj ] /= counts[ c ];
+						ocj += 1; // move to the next feature of the respective centroid
+					}
+				} else { // if empty cluster, retain the old features
+					ocj = oc;
+					oij = si;
+					for ( j = 0; j < N; j++ ) {
+						centroidsNew[ ocj ] = init[ oij ];
+						ocj += 1; // move to the next feature of the respective centroid
+						oij += strideInit3; // move to the next feature of the respective centroid's old feature set
+					}
+				}
+				oc += N; // move to the next centroid
+				si += strideInit2; // move to the next centroid's old feature set
+			}
+
+			oc = 0;
+			shift = 0.0;
+			si = offsetInit;
+			for ( c = 0; c < k; c++ ) {
+				oij = si;
+				ocj = oc;
+				for ( j = 0; j < N; j++ ) {
+					d = centroidsNew[ ocj ] - init[ oij ];
+					shift += d * d;
+					init[ oij ] = centroidsNew[ ocj ];  // update init for next iteration
+					ocj += 1;
+					oij += strideInit3;
+				}
+				oc += N;
+				si += strideInit2;
+			}
+			dlacpy( 'all', k, N, centroidsNew, N, 1, 0, centroids, N, 1, 0 );
+
+			// We know stride for labels is `1` and offset is `0`, so this method for similarity checking works
+			if ( shift < tol || isEqualArray( labels, labelsOld ) ) {
+				strictConv = true;
+				break;
+			}
+			gcopy( M, labels, 1, 0, labelsOld, 1, 0 );
+		}
+
+		// re-run E-step
+		if (!strictConv) {
+			ox = offsetX;
+			for ( i = 0; i < M; i++ ) {
+				best = closestCentroid( dist, N, k, X, strideX2, ox, init, strideInit2, strideInit3, oi ); // eslint-disable-line max-len
+				labels[ i ] = best;
+				ox += strideX1; // move to the next sample
+			}
+		}
+
+		inertia = computeInertia( M, N, X, strideX1, strideX2, offsetX, centroids, N, 1, 0, labels ); // eslint-disable-line max-len
+		if ( isnan( bestInertia ) || inertia < bestInertia ) {
+			gcopy( M, labels, 1, 0, bestLabels, strideL, offsetL);
+			dlacpy( 'all', k, N, centroids, N, 1, 0, bestCentroids, strideC1, strideC2, offsetC );
+			bestInertia = inertia;
+			bestR = r;
+			if ( iter > maxIter ) {
+				bestIter = maxIter;
+			} else {
+				bestIter = iter;
+			}
+		}
+		oi += strideInit1; // move to the next replicate
+	}
+
+	out.replicates = replicates;
+	out.replicate = bestR;
+	out.metric = metric;
+	out.iterations = bestIter;
+	out.method = METHOD;
+	out.inertia = bestInertia;
+	out.k = k;
+	out.samples = M;
+	out.features = N;
+}
+
+
+// EXPORTS //
+
+module.exports = dkmeansld;