Skip to contents

Changelog

Source: NEWS.md

BBNI 0.2.2

CRAN release: 2026-08-19

  • Fixed time-series effective sample size calculation and user-defined prop.ratio behavior
  • Implemented posterior thinning in run_bbni() to align with original paper methodology and added trace display thinning in plot_trace()
  • Removed bitops package dependency in favor of base R bitwise functions
  • Improved performance via vectorization in check_ances_matrix() and ProposalConstruction()
  • Fixed node/label scaling issues in plot_bbni() for custom gene names
  • Expanded vignette with reproducible yeast analysis and clarified model assumptions

BBNI 0.2.1

  • Major performance optimization: ~14x speedup via vectorization in Error_LLH and implementing repeated Boolean matrix squaring in update_ancestor_matrix, keeping strict numerical equivalence with v0.1.1
  • Vignette expanded and successfully compiled to demonstrate new independent (non-timeseries) mode and visualization features
  • Real-world yeast dataset application realized in the vignette
  • Minor code reformatting for readability

BBNI 0.2.0

  • Added new visualization functions: plot_bbni(), plot_trace(), and plot_network()
  • Enhanced plot_bbni() to compare inferred networks against true networks and fixed a reversed edge direction bug
  • Implemented independent (non-timeseries) mode across core algorithm and data generation functions
  • Upgraded run_bbni() with a progress bar, MCMC summary, burn-in parameters, and posterior edge probabilities
  • Optimized MCMC mixing with logic fixes to ProposalConstruction
  • Added default parameters for key user-facing functions
  • Significantly expanded documentation and examples across all primary functions
  • Included public yeast dataset from original paper for user testing and for vignette

BBNI 0.1.1

CRAN release: 2026-07-15

  • Rewrote documentation, vignette, and README for clarity
  • Reformatted code for readability
  • Removed unused/dead code/comments
  • Fixed spelling and minor typos

BBNI 0.1.0

  • Initial development version.
  • Refactored legacy Bayesian Boolean Network Inference code into a modular, documented R package.
  • Added run_bbni() as the primary user-facing function.
  • Added a vignette demonstrating network recovery from simulated data.
  • Added unit tests for core network-validity and likelihood functions.