fastrerandomize 0.3

New Features

• Added diagnose_rerandomization() function for pre-analysis evaluation of rerandomization designs. This function helps researchers determine optimal acceptance thresholds by computing:

  • Expected number of acceptable randomizations
  • Minimum balance criterion values
  • Power analysis for different threshold choices

• Added fast_distance() function for hardware-accelerated pairwise distance computation supporting multiple metrics: Euclidean, Manhattan, Mahalanobis, cosine, and correlation-based distances.

• Added S3 methods (print, summary, plot) for fastrerandomize_diagnostic class to visualize and summarize diagnostic results.

Improvements

• Updated JAX backend to support CUDA 12 and CUDA 13.
• Improved documentation throughout the package.
• Various bug fixes and performance improvements.

fastrerandomize 0.2

Initial CRAN Release (2025-01-14)

First release on CRAN with core functionality:

• generate_randomizations(): Generate pools of acceptable randomizations based on covariate balance.
• generate_randomizations_exact(): Exact enumeration for small experiments.
• generate_randomizations_mc(): Monte Carlo sampling for larger experiments.
• randomization_test(): Permutation-based inference with optional fiducial intervals.
• build_backend(): Create conda environment with JAX and GPU support.
• check_jax_availability(): Verify JAX backend availability.
• Pure R fallback implementations (_R suffix functions) for environments without JAX.
• Support for CPU, CUDA, and METAL hardware acceleration frameworks.
• S3 classes with print, summary, and plot methods for results objects.
• Included datasets: QJEData and YOPData.