A JavaScript library for escaping CSS strings and identifiers while generating the shortest possible ASCII-only output.
For ruby and ruby on rails
Ruby SemVer in TypeScript.
Convention over configuration for using Vite in Ruby apps
Like ruby's abbrev module, but in js
Ruby grammar for tree-sitter
prettier plugin for the Ruby programming language
WebSocket framework for Ruby on Rails.
bootstrap-sass is a Sass-powered version of Bootstrap 3, ready to drop right into your Sass powered applications.
Convention over configuration for using Vite in Rails apps
JavaScript client for graphql-ruby
realistic password strength estimation
A Stimulus Wrapper for Flatpickr library
Provide I18n to your React Native application
Prism Ruby parser
A pure JavaScript implementation of Sass.
## Installation
Ruby on Rails unobtrusive scripting adapter
A lightweight Sass tool set.
Subresource Integrity hashes for the Vite.js manifest.
Compass stylesheets
JavaScript and TypeScript SDK for sending errors, logs, metrics, transactions, spans, and check-ins to Logister.
JS lib with TS typings to manipulate strings according to the word parsing rules of the UNIX Bourne shell.
node-semver compatible API with RubyGems semantics
Ruby and Rails client for reporting errors, logs, metrics, transactions, spans, and check-ins to the Logister backend, including self-hosted installs.
Ruby API wrapper for RLM logistics
A ruby wrapper for interfacing with Seko Logistics' SupplySteam iHub API
This gem provides a simple client for Easyship, offering accessing to Easyship's shipping, tracking, and logistics services directly from Ruby applications.
Ruby SDK for the KiriminAja logistics API. Supports address lookup, coverage area pricing, order management (express & instant), courier services, pickup scheduling, and payments.
Library to integrate K+N logistic API to Ruby on Rails Applications.
Statsample-GLM is an extension to Statsample, an advance statistics suite in Ruby. This gem includes modules for Regression techniques such as Poisson and Logistic Regression using the IRLS algorithm and Logistic, Probit and Normal Regression using the Newton Raphson algorithm.
Rumale is a machine learning library in Ruby. Rumale provides machine learning algorithms with interfaces similar to Scikit-Learn in Python. Rumale supports Support Vector Machine, Logistic Regression, Ridge, Lasso, Multi-layer Perceptron, Naive Bayes, Decision Tree, Gradient Tree Boosting, Random Forest, K-Means, Gaussian Mixture Model, DBSCAN, Spectral Clustering, Mutidimensional Scaling, t-SNE, Fisher Discriminant Analysis, Neighbourhood Component Analysis, Principal Component Analysis, Non-negative Matrix Factorization, and many other algorithms.
A Ruby library for text classification featuring Naive Bayes, LSI (Latent Semantic Indexing), Logistic Regression, and k-Nearest Neighbors classifiers. Includes TF-IDF vectorization, streaming/incremental training, pluggable persistence backends, thread safety, and a native C extension for fast LSI operations.
Statsample-GLM is an extension to Statsample, an advance statistics suite in Ruby. This gem includes modules for Regression techniques such as Poisson Regression, Logistic Regression and Exponential Regression
SVMKit is a machine learninig library in Ruby. SVMKit provides machine learning algorithms with interfaces similar to Scikit-Learn in Python. SVMKit supports Linear / Kernel Support Vector Machine, Logistic Regression, Linear Regression, Ridge, Lasso, Factorization Machine, Naive Bayes, Decision Tree, AdaBoost, Random Forest, K-nearest neighbor algorithm, K-Means, DBSCAN, Principal Component Analysis, and Non-negative Matrix Factorization. Note that the SVMKit has been deprecated and has been renamed to Rumale.
This Ruby gem leverages Machine Learning(ML) techniques to make predictions(forecasts) and classifications in various applications. It provides capabilities such as predicting next month's billing, forecasting upcoming sales orders, identifying patient's potential findings(like Diabetes), determining user approval status, classifying text, generating similarity scores, and making recommendations. It uses Python3 under the hood, powered by popular machine learning techniques including NLP(Natural Language Processing), Decision Tree, K-Nearest Neighbors and Logistic Regression, Random Forest and Linear Regression algorithms.