Migrate to @cross/boxframe: update package name and repository references

Author: Pinta365

.github/workflows/deploy-pages.yml

@@ -0,0 +1,59 @@
+name: Deploy Lumocs Docs with GitHub Pages
+
+on:
+    # Runs on pushes targeting the main branch
+    push:
+        branches:
+            - main
+
+    # Allows you to run this workflow manually from the Actions tab
+    workflow_dispatch:
+
+# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
+permissions:
+    contents: read
+    pages: write
+    id-token: write
+
+# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued.
+# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete.
+concurrency:
+    group: "pages"
+    cancel-in-progress: false
+
+jobs:
+    # Build job
+    build:
+        runs-on: ubuntu-latest
+        steps:
+            - name: Checkout
+              uses: actions/checkout@v4
+
+            - name: Setup Pages
+              uses: actions/configure-pages@v5
+
+            - name: Install Deno
+              uses: denoland/setup-deno@v2
+              with:
+                  deno-version: v2.x
+
+            - name: Run Lume
+              run: deno task lume
+              working-directory: ./docs
+
+            - name: Upload artifact
+              uses: actions/upload-pages-artifact@v3
+              with:
+                  path: ./docs/_site
+
+    # Deployment job
+    deploy:
+        environment:
+            name: github-pages
+            url: ${{ steps.deployment.outputs.page_url }}
+        runs-on: ubuntu-latest
+        needs: build
+        steps:
+            - name: Deploy to GitHub Pages
+              id: deployment
+              uses: actions/deploy-pages@v4

docs/src/_data.json

@@ -11,12 +11,12 @@
         {
             "icon": "fab fa-github",
             "title": "GitHub Repository",
-            "url": "https://github.com/pinta365/boxframe"
+            "url": "https://github.com/cross-org/boxframe"
         },
         {
             "icon": "fas fa-box",
             "title": "JSR Package",
-            "url": "https://jsr.io/@pinta365/boxframe"
+            "url": "https://jsr.io/@cross/boxframe"
         }
     ],
     "nav_links": [
@@ -26,7 +26,7 @@
         },
         {
             "title": "GitHub",
-            "url": "https://github.com/pinta365/boxframe"
+            "url": "https://github.com/cross-org/boxframe"
         }
     ]
 }

docs/src/api/csv-parser.md

@@ -39,7 +39,7 @@ type DType = "int32" | "float64" | "string" | "bool" | "datetime";
 ### Basic Usage
 
 ```typescript
-import { parseCsv, parseCsvFromFile } from "@pinta365/boxframe";
+import { parseCsv, parseCsvFromFile } from "@cross/boxframe";
 
 // Parse CSV string with automatic type inference
 const df1 = parseCsv("name,age,salary,hire_date\nAlice,25,50000.50,2023-01-15\nBob,30,75000,2022-06-10");
@@ -129,7 +129,7 @@ const df9 = parseCsv(csvContent, {
 ### Streaming Large CSVs
 
 ```typescript
-import { parseCsvBatchedStream, parseCsvStream } from "@pinta365/boxframe";
+import { parseCsvBatchedStream, parseCsvStream } from "@cross/boxframe";
 
 // 1) Batched streaming with callbacks
 await parseCsvBatchedStream("/path/to/large.csv", {
@@ -159,7 +159,7 @@ console.log("total rows:", df.shape[0]);
 For very large CSV files, you can enable worker-based parallel parsing to leverage multiple CPU cores:
 
 ```typescript
-import { parseCsvBatchedStream, parseCsvStream, analyzeCsv } from "@pinta365/boxframe";
+import { parseCsvBatchedStream, parseCsvStream, analyzeCsv } from "@cross/boxframe";
 
 // Enable workers for parallel processing (recommended for files > 100MB)
 await parseCsvBatchedStream("/path/to/very-large.csv", {
@@ -214,7 +214,7 @@ await parseCsvBatchedStream("/path/to/very-large.csv", {
 ### Quick File Analysis (Sampling)
 
 ```typescript
-import { analyzeCsv } from "@pinta365/boxframe";
+import { analyzeCsv } from "@cross/boxframe";
 
 const analysis = await analyzeCsv("/path/to/large.csv", { sampleLines: 1000 });
 console.log(analysis.columns);      // inferred column names

docs/src/api/google-sheets.md

@@ -25,7 +25,7 @@ Provides methods to read data from public Google Sheets.
 ## Example
 
 ```typescript
-import { GoogleSheets } from "@pinta365/boxframe";
+import { GoogleSheets } from "@cross/boxframe";
 
 // From spreadsheet ID
 const df = await GoogleSheets.readSheet("1ABC123...");

docs/src/api/utilities.md

@@ -22,7 +22,7 @@ Helper functions for data type inference, conversion, and validation.
 ## Examples
 
 ```typescript
-import { inferDType, isDType, isWasmEngineEnabled } from "@pinta365/boxframe";
+import { inferDType, isDType, isWasmEngineEnabled } from "@cross/boxframe";
 
 // Type inference
 const dtype = inferDType(42);        // "int32"

docs/src/examples.md

@@ -13,7 +13,7 @@ This page contains practical examples using BoxFrame.
 ### Sales Data Analysis
 
 ```typescript
-import { DataFrame } from "@pinta365/boxframe";
+import { DataFrame } from "@cross/boxframe";
 
 // Sample sales data
 const salesData = new DataFrame({
@@ -42,7 +42,7 @@ console.log(productSummary);
 ### Employee Data Processing
 
 ```typescript
-import { DataFrame } from "@pinta365/boxframe";
+import { DataFrame } from "@cross/boxframe";
 
 // Employee dataset
 const employees = new DataFrame({
@@ -75,7 +75,7 @@ const salaryAnalysis = employees.assign({
 ### Cleaning Messy Data
 
 ```typescript
-import { DataFrame } from "@pinta365/boxframe";
+import { DataFrame } from "@cross/boxframe";
 
 // Messy dataset with missing values and inconsistencies
 const messyData = new DataFrame({
@@ -136,7 +136,7 @@ console.log(normalized.toString());
 ### Time Series Analysis
 
 ```typescript
-import { DataFrame } from "@pinta365/boxframe";
+import { DataFrame } from "@cross/boxframe";
 
 // Stock price data
 const stockData = new DataFrame({
@@ -174,7 +174,7 @@ const withMA = withReturns.assign({
 Customer segmentation helps identify distinct groups based on demographics and behavior patterns. This example creates age and income groups, then analyzes spending patterns across segments.
 
 ```typescript
-import { DataFrame } from "@pinta365/boxframe";
+import { DataFrame } from "@cross/boxframe";
 
 // Customer data with diverse age/income combinations
 const customers = new DataFrame({
@@ -229,7 +229,7 @@ Senior|Low    2000          4              8000
 ### Large Dataset Processing
 
 ```typescript
-import { DataFrame } from "@pinta365/boxframe";
+import { DataFrame } from "@cross/boxframe";
 
 // For large datasets, use appropriate operations
 const largeDataset = new DataFrame({
@@ -253,7 +253,7 @@ console.log("Processed", results.shape[0], "groups");
 ### Streaming Large CSV Files
 
 ```typescript
-import { parseCsvBatchedStream } from "@pinta365/boxframe";
+import { parseCsvBatchedStream } from "@cross/boxframe";
 
 // Stream a large CSV in batches and write to a database incrementally
 await parseCsvBatchedStream("/data/huge.csv", {
@@ -288,7 +288,7 @@ await parseCsvBatchedStream("/data/very-large.csv", {
 ### Working with APIs
 
 ```typescript
-import { BoxFrame } from "@pinta365/boxframe";
+import { BoxFrame } from "@cross/boxframe";
 
 // Fetch data from API and process
 async function processAPIData() {

docs/src/getting-started.md

@@ -14,37 +14,37 @@ BoxFrame is available on JSR (JavaScript Registry) and can be installed with you
 
 #### Deno
 ```bash
-deno add jsr:@pinta365/boxframe
+deno add jsr:@cross/boxframe
 ```
 
 #### Node.js
 ```bash
-npx jsr add @pinta365/boxframe
+npx jsr add @cross/boxframe
 ```
 
 #### Bun
 ```bash
-bunx jsr add @pinta365/boxframe
+bunx jsr add @cross/boxframe
 ```
 
 #### Other Package Managers
 ```bash
 # pnpm
-pnpm i jsr:@pinta365/boxframe
+pnpm i jsr:@cross/boxframe
 
 # yarn
-yarn add jsr:@pinta365/boxframe
+yarn add jsr:@cross/boxframe
 
 # vlt
-vlt install jsr:@pinta365/boxframe
+vlt install jsr:@cross/boxframe
 ```
 
 #### Browser (ESM)
 For browser usage, you can import BoxFrame directly from esm.sh:
 
 ```html
 <script type="module">
-import { DataFrame } from "https://esm.sh/jsr/@pinta365/boxframe@0.0.1";
+import { DataFrame } from "https://esm.sh/jsr/@cross/boxframe@0.0.1";
 
 // Use BoxFrame in your browser application
 const df = new DataFrame({
@@ -63,7 +63,7 @@ console.log(df.toString());
 Let's create a simple DataFrame to get started:
 
 ```typescript
-import { DataFrame } from "@pinta365/boxframe";
+import { DataFrame } from "@cross/boxframe";
 
 // Create a DataFrame from an object
 const df = new DataFrame({
@@ -196,7 +196,7 @@ const names = df.get("name");
 A Series is a one-dimensional array with labels:
 
 ```typescript
-import { Series } from "@pinta365/boxframe";
+import { Series } from "@cross/boxframe";
 
 // Create a Series
 const ages = new Series([25, 30, 35, 28], { name: "age" });
@@ -278,7 +278,7 @@ const df7 = await BoxFrame.readGoogleSheetFromUrl(
 When working with very large CSV files, consider using the streaming APIs to reduce memory usage and process data incrementally:
 
 ```typescript
-import { parseCsvBatchedStream } from "@pinta365/boxframe";
+import { parseCsvBatchedStream } from "@cross/boxframe";
 
 await parseCsvBatchedStream("/path/to/large.csv", {
     hasHeader: true,

docs/src/index.md

@@ -14,7 +14,7 @@ from Rust for performance, it provides an intuitive API for data manipulation, a
 ## Quick Start
 
 ```typescript
-import { DataFrame } from "@pinta365/boxframe";
+import { DataFrame } from "@cross/boxframe";
 
 // Sales data analysis
 const sales = new DataFrame({
@@ -49,37 +49,37 @@ BoxFrame is available on JSR (JavaScript Registry) and can be installed with you
 
 #### Deno
 ```bash
-deno add jsr:@pinta365/boxframe
+deno add jsr:@cross/boxframe
 ```
 
 #### Node.js
 ```bash
-npx jsr add @pinta365/boxframe
+npx jsr add @cross/boxframe
 ```
 
 #### Bun
 ```bash
-bunx jsr add @pinta365/boxframe
+bunx jsr add @cross/boxframe
 ```
 
 #### Other Package Managers
 ```bash
 # pnpm
-pnpm i jsr:@pinta365/boxframe
+pnpm i jsr:@cross/boxframe
 
 # yarn
-yarn add jsr:@pinta365/boxframe
+yarn add jsr:@cross/boxframe
 
 # vlt
-vlt install jsr:@pinta365/boxframe
+vlt install jsr:@cross/boxframe
 ```
 
 #### Browser (ESM)
 For browser usage, you can import BoxFrame directly from esm.sh:
 
 ```html
 <script type="module">
-import { DataFrame } from "https://esm.sh/jsr/@pinta365/boxframe@0.0.1";
+import { DataFrame } from "https://esm.sh/jsr/@cross/boxframe@0.0.1";
 
 // Use BoxFrame in your browser application
 const df = new DataFrame({