A small utility for partitioning a sequence of items by enum discriminant
Procedural macro to add functions on enum types to get discrimnant value from variant or create unit variant from discriminant value.
A tiny crate to make working with discriminants easier.
A tiny crate to make working with discriminants easier.
Atlas Program Library 8-Byte Discriminator Management
Solana Program Library 8-Byte Discriminator Management
Solana Program Library 8-Byte Discriminator Management
Solarti Program Library 8-Byte Discriminator Management
Solana Program Library 8-Byte Discriminator Management
Trezoa Program Library 8-Byte Discriminator Management
proc_macros intended for use with split_by_discriminant
Procedural macros for 'enumcapsulate' crate
A Ruby gem that implements single-table inheritance (STI) for ActiveRecord models using string, integer and boolean column types.
A simple discriminant gem
Discriminate class type based on a model field
harlequin is a Ruby wrapper for linear and quadratic discriminant analysis in R for statistical classification. Also allows means testing to determine significance of discriminant variables.
Add MCP tool serving to any Rails app. Write @rbs type annotations with predicate tags (@requires, @feature, or custom) and the gem compiles per-user JSON Schema automatically — filtering fields by permissions, feature flags, and plan tiers at request time.
Rumale::MetricLearning provides metric learning algorithms, such as Fisher Discriminant Analysis and Neighboourhood Component Analysis with Rumale interface.
Flexible Argument Parse It allows a default setting to use discriminating argument types like 'a:42 -b c,3,4 delta:1..10'
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 lightweight Ruby gem providing generic Solana building blocks — JSON-RPC client with retry, Ed25519 keypair management, Borsh encoding/decoding, transaction builder with PDA derivation and Anchor discriminators, SPL Token instruction helpers, and a pure-Ruby wallet-signature verifier (Solana::AuthVerifier).
Validrb is a powerful Ruby schema validation library inspired by Pydantic and Zod. It provides type coercion, rich constraints, schema composition, union types, discriminated unions, custom validators, JSON Schema generation, and serialization.
swagger23 converts Swagger 2.0 (OAS 2) API specifications into OpenAPI 3.0.3 (OAS 3) specifications. Accepts JSON or YAML input, produces JSON or YAML output. Works as a Ruby library (Swagger23.convert) or a standalone CLI tool (swagger23). Handles paths, parameters, requestBody, components/schemas, securitySchemes, servers, $ref rewriting, collectionFormat, x-nullable, discriminator, OAuth2 flows, and file uploads. No external runtime dependencies. Safe for large specs.
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.