The R package BiDAG implements Markov chain Monte Carlo (MCMC) methods for structure learning and sampling of Bayesian networks. The package includes tools to search for a maximum a posteriori (MAP) graph and to sample graphs from the posterior distribution given the data. A new hybrid approach to structure learning enables inference in large graphs. In the first step, we define a reduced search space by means of the PC algorithm or based on prior knowledge. In the second step, an iterative order MCMC scheme proceeds to optimize within the restricted search space and estimate the MAP graph. Sampling from the posterior distribution is implemented using either order or partition MCMC. The models and algorithms can handle both discrete and continuous data. The BiDAG package also provides an implementation of MCMC schemes for structure learning and sampling of dynamic Bayesian networks.

翻译：BiDAG软件包安装了Markov链Monte Carlo (MCMC) 结构学习和取样Bayesian网络的方法,包括搜索根据数据在后端分布的后端图(MAP)和样本图的工具。一种新的结构学习混合方法可以在大图中进行推断。在第一步,我们通过PC算法或先前的知识来界定一个缩小的搜索空间。在第二步,一个迭代顺序MC方案在限制搜索空间范围内进行优化,并估计MAP图。从远端分布中采集的样本使用命令或分区MCMC。模型和算法可以同时处理离散和连续数据。BiDAG软件包还提供实施结构学习和抽样的MC方案,用于动态Bayesian网络的结构学习和取样。