| 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 | Variable selection for continuous data using the FBED algorithm |
| cor2pcor | Partial correlation matrix from correlation or covariance matrix |
| corpairs | Correlation between pairs of variables |
| correls | Correlation between a vector and a set of variables |
| cortest | Correlation significance testing using Fisher's z-transformation |
| dcor.fedhc.skel | The skeleton of a Bayesian network produced by the MMHC or the FEDHC algorithm using the distance correlation |
| dcor.mmhc.skel | The skeleton of a Bayesian network produced by the MMHC or the FEDHC algorithm using the distance correlation |
| fedhc | The FEDHC and FEDTABU Bayesian network learning algorithms |
| fedhc.boot | Bootstrapping the FEDHC and FEDTABU Bayesian network learning algorithms |
| 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 |
| fedtabu | The FEDHC and FEDTABU Bayesian network learning algorithms |
| fedtabu.boot | Bootstrapping the FEDHC and FEDTABU Bayesian network learning algorithms |
| 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 and MMTABU Bayesian network learning algorithms |
| mmhc.boot | Bootstrapping the MMHC and MMTABU Bayesian network learning algorithms |
| 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 | Variable selection for continuous data using the MMPC algorithm |
| mmtabu | The MMHC and MMTABU Bayesian network learning algorithms |
| mmtabu.boot | Bootstrapping the MMHC and MMTABU Bayesian network learning algorithms |
| pc.sel | Variable selection for continuous data using the PC-simple algorithm |
| pchc | The PCHC and PCTABU Bayesian network learning algorithms |
| pchc.boot | Bootstrapping the PCHC and PCTABU Bayesian network learning algorithms |
| 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 |
| pctabu | The PCHC and PCTABU Bayesian network learning algorithms |
| pctabu.boot | Bootstrapping the PCHC and PCTABU Bayesian network learning algorithms |
| 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 |
| topological_sort | Topological sort of a Bayesian network |