方便灵活的导出插件,所见即所得
A powerful and flexible Excel manipulation library with cursor-based navigation
A flexible excel workbook manipulation, scraping and visualization library
Read `.xlsx` files in a web browser or in Node.js
[](https://www.npmjs.com/package/@aws-sdk/middleware-flexible-checksums) [. Times are handled internally as
Tokenize Excel formulas
An MCP server that reads and writes spreadsheet data to MS Excel file
The fast, flexible & elegant library for parsing and manipulating HTML and XML.
Convert Excel to JSON
A Complete Microsoft Excel-like JavaScript Spreadsheet for Enterprise Applications
Parse excel formula into a tree
It allows you to export an HTML table just by sending the table reference and the name with which you want the file to be saved
Provides mocking support for Office-js APIs
Samovar is a flexible option parser excellent support for sub-commands and help documentation.
Read and write Excel files
Importance is a Rails engine that allows users to upload Excel and CSV files and interactively map columns to model attributes. It handles files with arbitrary headers by letting users select which columns to import and which to ignore, with support for flexible attribute mapping, batch processing, error handling, and customizable import workflows.
Flexible approach to handling exceptions in ruby (for library writers, or consumers). Ispired by Avdi Grimm's excellent book 'Exceptional Ruby'.
RubyBHL is a simple but flexible request/response wrapper for the Biodiversity Heritage Libary API. It includes (some) validation for request formatting. It has excellent unit-test coverage.
The ExportManager allows users to export data from dynamic tables with dynamic columns. The system provides flexibility in selecting the data to be exported and generates a CSV, JSON, Excel, or XML file accordingly.
IrtRuby is a comprehensive Ruby library for Item Response Theory (IRT) analysis, commonly used in educational assessment, psychological testing, and survey research. Features three core IRT models: • Rasch Model (1PL) - Simple difficulty-only model • Two-Parameter Model (2PL) - Adds item discrimination • Three-Parameter Model (3PL) - Includes guessing parameter Key capabilities: • Robust gradient ascent optimization with adaptive learning rates • Flexible missing data strategies (ignore, treat as incorrect/correct) • Comprehensive performance benchmarking suite • Memory-efficient implementation with excellent scaling • Production-ready with extensive test coverage Perfect for researchers, data scientists, and developers working with educational assessments, psychological measurements, or any binary response data where item and person parameters need to be estimated simultaneously.
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