BBNI 0.2.2
- 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
- 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.