| pchc-package | Bayesian Network Learning with the PCHC and Related Algorithms |
| auc | ROC and AUC |
| big_cor | Correlation matrix for FBM class matrices (big matrices) |
| big_read | Read big data or a big.matrix object |
| bn.skel.utils | Utilities for the skeleton of a (Bayesian) Network |
| bn.skel.utils2 | Utilities for the skeleton of a (Bayesian) Network |
| bnmat | Adjacency matrix of a Bayesian network |
| bnplot | Plot of a Bayesian network |
| cat.tests | Chi-square and G-square tests of (unconditional) indepdence |
| chi2test | G-square test of conditional indepdence |
| chi2test_univariate | All pairwise G-square and chi-square tests of indepedence |
| conf.edge.lower | Lower limit of the confidence of an edge |
| cor.fbed | FBED variable selection method using the correlation |
| correls | Correlation between a vector and a set of variables |
| cortest | Correlation significance testing using Fisher's z-transformation |
| fedhc | The FEDHC Bayesian network learning algorithm |
| fedhc.skel | The skeleton of a Bayesian network produced by the FEDHC algorithm |
| fedhc.skel.boot | Bootstrap versions of the skeleton of a Bayesian network |
| g2test | G-square test of conditional indepdence |
| g2test_perm | G-square test of conditional indepdence |
| g2test_univariate | All pairwise G-square and chi-square tests of indepedence |
| g2test_univariate_perm | All pairwise G-square and chi-square tests of indepedence |
| is.dag | Check whether a directed graph is acyclic |
| mb | Markov blanket of a node in a Bayesian network |
| mmhc | The MMHC Bayesian network learning algorithm |
| mmhc.skel | The skeleton of a Bayesian network learned with the MMHC algorithm |
| mmhc.skel.boot | Bootstrap versions of the skeleton of a Bayesian network |
| mmpc | Max-Min Parents and Children variable selection algorithm for continuous responses |
| pc.sel | Variable selection using the PC-simple algorithm |
| pchc | The PCHC Bayesian network learning algorithm |
| pchc.skel | The skeleton of a Bayesian network learned with the PC algorithm |
| pchc.skel.boot | Bootstrap versions of the skeleton of a Bayesian network |
| pcor | Partial correlation |
| pi0est | Estimation of the percentage of null p-values |
| rbn | Random values simulation from a Bayesian network |
| rbn2 | Continuous data simulation from a DAG. |
| rbn3 | Continuous data simulation from a DAG. |
| rmcd | Outliers free data via the reweighted MCD |