| adjmxToSif | Create a .sif file from given adjacency matrix |
| calcPerfDiNet | Calculating performance metrics of the directed net 'inferredNet' w.r.t. the directed net 'targetNet'. |
| checkUnrolledDbn | Checks whether the given unrolled DBN follows 1st Markov order or not |
| CompareNet | Checks if 'di.net.adj.matrix' = 'cmi.net.adj.matrix' |
| computeCmi | Compute Conditional Mutual Infortion (CMI) |
| ComputeCmiPcaCmi | Compute Conditional Mutual Information (CMI) the way it is done in the implementation of the PCA-CMI algo |
| ComputEntropy | Compute Entropy matrix from the input data |
| ConvertDinetToUndinet | Given a directed network, convert it into an undirected network |
| CountFeedFwdEdgesUndi | Count the number of feed-forward edges in a given undirected network. |
| discretizeData.2L.Tesla | Discretize input data into 2 levels. |
| discretizeData.2L.wt.l | Discretizes input data into two levels. |
| discretizeData.2L.wt.le | Discretizes input data into two levels. |
| discretizeData.3L.wt | Discretizes input data into three levels, given a tolerance. |
| discretizeData.5L.wt | Discretizes input data into five levels. |
| eval.wrt.known.gene.ias | Accuracy of predicted directed gene reuglatory network adjacency matrix |
| GenTrueAdjMatrix | Generates True net adjacency matrix and save as an R object |
| LearnClr2NetMfi | Learns CLR2 network |
| LearnClr3NetMfi | Learn CLR3 network |
| LearnClrNetFromDiscrData | Learns CLR network from a given discretized dataset. |
| LearnClrNetMfi | Learns CLR network |
| LearnClrNetMfiVer2.1 | Learn CLR2.1 network |
| learnCmiNetStruct | Learns the CMI structure |
| learnDbnStructLayer3dParDeg1 | Unrolled DBN structure learning with Markov Order 0 and 1. |
| LearnDbnStructMo1Clr3Ser | Learns DBN structure of Markov order 1 where candidate parents are selected using the CLR3 algo. |
| learnDbnStructMo1Layer3dParDeg1 | Goal: Unrolled DBN structure learning with Markov Order 1. |
| learnDbnStructMo1Layer3dParDeg1_v2 | Goal: Unrolled DBN structure learning with Markov Order 1. |
| LearnMiNetStructClr | Learns the CLR network Replaces all non-zero edge weights with 1. |
| LearnMiNetStructRowMedian | Learn the mi network structure |
| LearnMiNetStructZstat | Learn the mi network structure |
| LearnTgs | Implementing the TGS Algorithm. |
| Print.common.di.edges | Given two di network adjacency matrices, it prints the common edges in an output file |
| reachable.nodes | Returns all the nodes reachable from the given node in the directed adjacency matrix |
| rollDbn | Convert a given unrolled Dynamic Bayesian Network (DBN) into a rolled DBN using different rolling methods Rolls time-varying networks into a single time-invariant network. This function is compatible with the time-varying networks learnt through learnDbnStruct3dParDeg1.R::learnDbnStructMo1Layer3dParDeg1(). |
| rollDbn_v2 | Convert a given unrolled Dynamic Bayesian Network (DBN) into a rolled DBN using different rolling methods Rolls time-varying networks into a single time-invariant network. This function is compatible with the time-varying networks learnt through learnDbnStruct3dParDeg1.R::learnDbnStructMo1Layer3dParDeg1_v2(). |