BBNI
Bayesian Inference of Boolean Genetic Networks
Implements a fully Bayesian Markov chain Monte Carlo (MCMC) approach for inferring the topology and Boolean logic transition functions of gene regulatory networks from noisy, binary time-series expression data. Network structure and Boolean rules are sampled jointly from their posterior distribution, providing principled uncertainty quantification rather than a single point estimate. Method described in Han et al. (2014) <doi:10.1371/journal.pone.0115806>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.1 |
rolling linux/jammy R-4.5 | BBNI_0.1.1.tar.gz |
127.4 KiB |
0.1.1 |
rolling linux/noble R-4.5 | BBNI_0.1.1.tar.gz |
127.3 KiB |
0.1.1 |
rolling source/ R- | BBNI_0.1.1.tar.gz |
66.0 KiB |
0.1.1 |
latest linux/jammy R-4.5 | BBNI_0.1.1.tar.gz |
127.4 KiB |
0.1.1 |
latest linux/noble R-4.5 | BBNI_0.1.1.tar.gz |
127.3 KiB |
0.1.1 |
latest source/ R- | BBNI_0.1.1.tar.gz |
66.0 KiB |
0.1.1 |
2026-04-23 source/ R- | BBNI_0.1.1.tar.gz |
0 B |