chore: update dependencies to latest

README.md

@@ -76,14 +76,30 @@ K = Matrix([
 3. Using the options
 
 ```js
-const K = fcnnls(X, Y, {
+const { K, info } = fcnnls(X, Y, {
   info: true, // returns the error/iteration.
   maxIterations: 5,
   gradientTolerance: 0,
 });
-/* same result than 2*/
+/* K is the same result as in 2 */
+/* info = { rse: [[...], [...], [...]], iterations: 3 } */
 ```
 
+### Options
+
+Both `fcnnls` and `fcnnlsVector` accept the same options.
+
+| Option              | Type      | Default         | Description                                                                                             |
+| ------------------- | --------- | --------------- | ------------------------------------------------------------------------------------------------------- |
+| `maxIterations`     | `number`  | `3 * X.columns` | Maximum number of iterations of the active-set loop.                                                    |
+| `gradientTolerance` | `number`  | `1e-5`          | Larger values (like `1e-4`) can help when the iteration limit is exceeded.                              |
+| `info`              | `boolean` | `false`         | When `true`, also returns `info` with the root squared error per column of `Y` and the iteration count. |
+| `interceptAtZero`   | `boolean` | `true`          | Set to `false` to add a column of ones to the left of `X`, fitting an intercept.                        |
+
+### Result
+
+`K` is always a `Matrix` of non-negative coefficients. When `info: true`, the result also carries `info.rse` (root squared error, one row per computation of `K`) and `info.iterations`.
+
 ## [API Documentation](https://mljs.github.io/fcnnls/)
 
 ## License

package.json

@@ -40,17 +40,17 @@
   },
   "homepage": "https://github.com/mljs/fcnnls#readme",
   "dependencies": {
-    "ml-matrix": "^6.13.0"
+    "ml-matrix": "^6.14.0"
   },
   "devDependencies": {
-    "@types/node": "^26.0.0",
-    "@vitest/coverage-istanbul": "^4.1.9",
+    "@types/node": "^26.1.2",
+    "@vitest/coverage-istanbul": "^4.1.10",
     "@zakodium/tsconfig": "^1.0.5",
     "eslint": "^9.39.4",
     "eslint-config-cheminfo-typescript": "^22.1.0",
-    "prettier": "^3.8.4",
+    "prettier": "^3.9.6",
     "rimraf": "^6.1.3",
     "typescript": "^6.0.3",
-    "vitest": "^4.1.9"
+    "vitest": "^4.1.10"
   }
 }

src/.npmignore

@@ -1,2 +1,3 @@
 __tests__
+.DS_Store
 .npmignore

src/fcnnls.ts

@@ -70,10 +70,10 @@ export function fcnnls<T extends boolean | undefined>(
   // pre-computes part of pseudo-inverse. XtX is the symmetric Gram matrix XᵀX;
   // gram() computes this bit-identically to Xt.mmul(X) but only fills the upper
   // triangle, skips zeros and never materializes the transpose, so it is much
-  // faster on the sparse predictor matrices common here.
-  const Xt = X.transpose();
+  // faster on the sparse predictor matrices common here. transposeMultiply()
+  // applies the same trick to XᵀY.
   const XtX = X.gram();
-  const XtY = Xt.mmul(Y);
+  const XtY = X.transposeMultiply(Y);
 
   const { columns: nColsY, rows: nRowsY } = Y;
   const { columns: nColsX, rows: nRowsX } = X;