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irt_ruby

v0.3.0RubyGems· Ruby

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.

The verdict
Aging — last published 12 months ago — check before adopting. Check the repo for activity before adopting.
Check the repo for activity before adopting.
Live from the RubyGems registry · derived rules, not AI
How it scores
MaintenanceAging
PopularityNiche
SecurityClean
LicensePermissive
DepsZero deps
Maintenance
Last published 12 months ago — check before adopting.
Popularity
8 downloads / week
Security
No known advisories for this version (OSV).
License
MIT
Dependencies
No runtime dependencies
Recent releases
  • 0.3.012 months ago
  • 0.2.0over a year ago
  • 0.1.0over a year ago
irt_ruby — 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. (Ruby / RubyGems) · Modules