chore: update dependencies

eslint.config.mjs

@@ -0,0 +1,14 @@
+import cheminfo from 'eslint-config-cheminfo-typescript';
+import globals from 'globals';
+
+export default [
+  ...cheminfo,
+  {
+    languageOptions: {
+      globals: {
+        ...globals.node,
+      },
+    },
+    rules: {}
+  }
+]

package.json

@@ -42,15 +42,15 @@
   },
   "homepage": "https://github.com/mljs/fcnnls#readme",
   "devDependencies": {
-    "@vitest/coverage-v8": "^0.34.5",
-    "eslint": "^8.50.0",
-    "eslint-config-cheminfo-typescript": "^12.0.4",
-    "prettier": "^3.0.3",
-    "rimraf": "^5.0.5",
-    "typescript": "^5.2.2",
-    "vitest": "^0.34.5"
+    "@vitest/coverage-v8": "^2.1.3",
+    "eslint": "^9.12.0",
+    "eslint-config-cheminfo-typescript": "^16.0.0",
+    "prettier": "^3.3.3",
+    "rimraf": "^6.0.1",
+    "typescript": "^5.6.3",
+    "vitest": "^2.1.3"
   },
   "dependencies": {
-    "ml-matrix": "^6.10.5"
+    "ml-matrix": "^6.11.1"
   }
 }

src/__tests__/fcnnls.test.ts

@@ -1,5 +1,5 @@
-import { readFileSync } from 'fs';
-import { join } from 'path';
+import { readFileSync } from 'node:fs';
+import { join } from 'node:path';
 
 import { Matrix } from 'ml-matrix';
 import { it, describe, expect } from 'vitest';
@@ -8,31 +8,31 @@ import { fcnnls } from '../fcnnls';
 
 import { assertResult } from './assertResult';
 
-const concentration = readFileSync(join(__dirname, 'data/matrix.txt'), 'utf-8');
+const concentration = readFileSync(join(__dirname, 'data/matrix.txt'), 'utf8');
 const linesA = concentration.split(/[\r\n]+/);
 const A: number[][] = [];
 for (const line of linesA) {
-  A.push(line.split(',').map((value) => Number(value)));
+  A.push(line.split(',').map(Number));
 }
 
 let matrix = new Matrix(A);
 
 matrix = matrix.transpose();
 
-const proportion = readFileSync(join(__dirname, 'data/x_fcnnls.txt'), 'utf-8');
+const proportion = readFileSync(join(__dirname, 'data/x_fcnnls.txt'), 'utf8');
 const linesk = proportion.split(/[\r\n]+/);
 const k: number[][] = [];
 for (const line of linesk) {
-  k.push(line.split(',').map((value) => Number(value)));
+  k.push(line.split(',').map(Number));
 }
 k.splice(133, 1);
 const answer = new Matrix(k);
 
-const observation = readFileSync(join(__dirname, 'data/target.txt'), 'utf-8');
+const observation = readFileSync(join(__dirname, 'data/target.txt'), 'utf8');
 const lines = observation.split(/[\r\n]+/);
 const b: number[][] = [];
 for (const line of lines) {
-  b.push(line.split(',').map((value) => Number(value)));
+  b.push(line.split(',').map(Number));
 }
 
 let target = new Matrix(b);

src/cssls.ts

@@ -12,7 +12,12 @@ import { sortCollectionSet } from './util/sortCollectionSet';
  * Solves XtX*K = XtY for the variables in Pset
  * if XtX (or XtX(vars,vars)) is singular, performs the svd and find pseudo-inverse, otherwise (even if ill-conditioned) finds inverse with LU decomposition and solves the set of equations
  * it is consistent with matlab results for ill-conditioned matrices (at least consistent with test 'ill-conditioned square X rank 2, Y 3x1' in cssls.test)
- * @param Cssls object, @see {@link Cssls}
+ * @param options - @see {@link Cssls}
+ * @param options.XtX
+ * @param options.XtY
+ * @param options.Pset
+ * @param options.nColsX
+ * @param options.nColsY
  */
 export function cssls({ XtX, XtY, Pset, nColsX, nColsY }: Cssls): Matrix {
   let K = Matrix.zeros(nColsX, nColsY);

src/fcnnls.ts

@@ -31,7 +31,7 @@ export interface FcnnlsOptions<T extends boolean | undefined> {
  * Fast Combinatorial Non-negative Least Squares with multiple Right Hand Side
  * @param X - The data/input/predictors matrix
  * @param Y - The response matrix
- * @param options {@link FcnnlsOptions}
+ * @param options - {@link FcnnlsOptions}
  * @returns By default, the object with the matrix of coefficients K. Please see {@link FcnnlsOutput} for more information.
  */
 export function fcnnls(
@@ -175,10 +175,7 @@ export function fcnnls<T extends boolean | undefined>(
         }
 
         for (let j = 0; j < m; j++) {
-          Pset[Hset[j]].splice(
-            Pset[Hset[j]].findIndex((item) => item === minIdx[j]),
-            1,
-          );
+          Pset[Hset[j]].splice(Pset[Hset[j]].indexOf(minIdx[j]), 1);
         }
 
         L = cssls({

src/initialisation.ts

@@ -13,8 +13,13 @@ interface Initialisation {
 /**
  * Solves OLS problem,  overwriting the negative values of K with 0.
  * It also pre-computes part of the pseudo-inverse used to solve Least Squares.
- * @param XtX - input data matrix
- * @param XtY - output data matrix
+ * @param options
+ * @param options.XtX - input data matrix
+ * @param options.XtY - output data matrix
+ * @param options.nRowsX
+ * @param options.nColsX
+ * @param options.nRowsY
+ * @param options.nColsY
  * @returns initial values for the algorithm (including the solution K to least squares, overwriting of negative values with 0)
  */
 export function initialisation({

src/optimality.ts

@@ -4,7 +4,20 @@ import { setDifference } from './util/setDifference';
 
 /**
  * Checks whether the solution has converged
- * @param see {@link OptimalityParams} for a description.
+ * {@link OptimalityParams} for a description.
+ * @param options
+ * @param options.iter
+ * @param options.maxIter
+ * @param options.XtX
+ * @param options.XtY
+ * @param options.Fset
+ * @param options.Pset
+ * @param options.W
+ * @param options.K
+ * @param options.l
+ * @param options.p
+ * @param options.D
+ * @param options.gradientTolerance
  * @returns Pset, Fset, W
  */
 export function optimality({
@@ -51,7 +64,7 @@ export function optimality({
   Fset = setDifference(Fset, Jset);
 
   // For non-optimal solutions, add the appropriate variables to Pset
-  if (Fset.length !== 0) {
+  if (Fset.length > 0) {
     for (let j = 0; j < Fset.length; j++) {
       for (let i = 0; i < l; i++) {
         if (Pset[Fset[j]].includes(i)) W.set(i, Fset[j], -Infinity);

src/util/diff.ts

@@ -1,6 +1,6 @@
 /**
  * Computes an array containing the difference of consecutive numbers
- * @param Array v from which it computes the difference
+ * @param v - v from which it computes the difference
  * @returns - Array of consecutive differences
  */
 export function diff(v: number[]) {

src/util/getRSE.ts

@@ -2,7 +2,11 @@ import { Matrix } from 'ml-matrix';
 
 /**
  * Return the root square error of the solution.
- * @param object with X, K, Y, and error @see {@link GetRSEInput}
+ * @param object - with X, K, Y, and error @see {@link GetRSEInput}
+ * @param object.X
+ * @param object.K
+ * @param object.Y
+ * @param object.error
  * @returns the root squared error array.
  */
 export function getRSE({ X, K, Y, error }: GetRSEInput) {

src/util/selection.ts

@@ -1,6 +1,7 @@
 /**
  * Returns a new array based on extraction of specific indices of an array
- * @param collection or array
+ * @param collection - or array
+ * @param vector
  * @param indices
  */
 export function selection<T extends number[] | number[][]>(

src/util/setDifference.ts

@@ -1,7 +1,7 @@
 /**
  * Computes the set difference A\B
- * @param set A as an array
- * @param set B as an array
+ * @param A - First array of numbers
+ * @param B - Second array of numbers
  * @returns Elements of A that are not in B
  */
 export function setDifference(A: number[], B: number[]) {

src/util/sortArray.ts

@@ -1,7 +1,7 @@
 /**
  * Sorts an array and returns an object with the sorted array and the corresponding indices.
  * @param array
- * @returns {values, indices}
+ * @returns
  */
 export function sortArray(array: number[]) {
   const v = array.map((value, index) => {

src/util/sortCollectionSet.ts

@@ -1,7 +1,6 @@
 /**
  * From an array of arrays it constructs a unique key for each array,
  * given by its values, such that same arrays have same keys.
- *
  * @param collection - Array of arrays
  * @returns - Array of objects with the original array, its index and its key
  */
@@ -10,7 +9,9 @@ function addUniqueKeyToColumns(collection: number[][]) {
     //indices of positive values within the column. (Pset)
     let key = BigInt(0);
     // items will be the indexes of Pset, so it's always an integer.
-    positiveRows.forEach((item) => (key |= BigInt(1) << BigInt(item)));
+    for (const item of positiveRows) {
+      key |= BigInt(1) << BigInt(item);
+    }
     return { positiveRows, columnIndexInK, key };
   });
 }
@@ -38,7 +39,7 @@ export function sortCollectionSet(collection: number[][]) {
       indices.push([]);
       sorted.push(set.positiveRows);
     }
-    indices[indices.length - 1].push(set.columnIndexInK);
+    indices.at(-1).push(set.columnIndexInK);
   }
 
   const result = {