Changes in version 1.0.2 (2025-06-18)                  

Minor Updates

  - Fixed title case in DESCRIPTION file for CRAN submission
  - Updated from "Re-estimation" to "Re-Estimation" as requested by CRAN

                        Changes in version 1.0.1                        

Minor Updates

  - Function Removal: Removed ClopperPearsonCI() function as it was not
    being used in the main BSSR functionality
  - Documentation Updates: Updated all documentation to reflect the
    removal of confidence interval functionality
  - Package Optimization: Streamlined package to focus on core BSSR
    methods

                        Changes in version 1.0.0                        

Initial Release

This is the first release of bbssr, a comprehensive R package for
blinded sample size re-estimation (BSSR) in two-arm clinical trials with
binary endpoints.

Main Features

  - Blinded Sample Size Re-estimation: Implement adaptive trial designs
    with BinaryPowerBSSR()
  - Multiple Exact Statistical Tests: Support for 5 different exact
    tests:
      - Pearson chi-squared test ('Chisq')
      - Fisher exact test ('Fisher')
      - Fisher mid-p test ('Fisher-midP')
      - Z-pooled exact unconditional test ('Z-pool')
      - Boschloo exact unconditional test ('Boschloo')
  - Flexible Design Options: Choose between restricted, unrestricted,
    and weighted BSSR approaches
  - Traditional Methods: Calculate power (BinaryPower()) and sample
    sizes (BinarySampleSize()) for fixed designs
  - Exact Confidence Intervals: Clopper-Pearson confidence intervals
    (ClopperPearsonCI())
  - Rejection Regions: Compute exact rejection regions (BinaryRR())

Design Approaches

  - Restricted Design: Conservative approach ensuring final sample size
    ≥ initial sample size
  - Unrestricted Design: Flexible approach allowing both sample size
    increases and decreases
  - Weighted Design: Advanced approach using weighted averaging across
    interim scenarios

Documentation

  - Comprehensive documentation with examples for all functions
  - Detailed vignettes explaining methodology and usage:
      - vignette("bbssr-introduction") - Getting started guide
      - vignette("bbssr-statistical-methods") - Statistical methodology
  - Complete README with practical examples

Statistical Validity

  - All methods maintain exact Type I error control at specified α level
  - Exact statistical tests rather than asymptotic approximations
  - Suitable for small to moderate sample sizes common in clinical
    trials

Dependencies

  - Base R (≥ 3.5.0)
  - fpCompare for robust floating-point comparisons
  - stats for statistical functions

Development

  - Package follows R package development best practices
  - Comprehensive documentation with roxygen2
  - Ready for CRAN submission