HierDAG is an R software library implementing two hierarchical ensemble methods for Directed Acyclic Graphs (DAGs):
1. Hierarchical Top-Down for DAG (HTD-DAG);
2. True-Path-Rule for DAG (TPR-DAG);
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Functions implementing Hierarchical Ensemble Methods for DAGs:
- htd.R: implementation of the HTD algorithm;
- tpr.R: implementation of the TPR algorithm and its variants;
- Do.HTD.R: high level functions to compute the HTD-DAG algorithm;
- Do.TPR.R: high level functions to compute TPR-DAG algorithm and its variants;
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Utility Functions:
- graph.utils.R: utility functions to process and analyze a graph;
- IOgraph.R: IO functions to store and build graph both in plain text and in rda compressed format;
- flat.score.norm.R: function to normalize the flat scores according to the maximum score of each class;
- Do.full.annotations.table.R: high level functions to compute the full annotation table;
- Do.best.F.score: high level functions to select the best F-score by choosing an appropriate threshold in a scores matrix;
- Do.FLAT.scores.normalization: high level functions to normalize flat scores matrix w.r.t. MaxNorm or Qnorm;
- F.hier.R: function to compute precision, recall, F-measure, specificity and accuracy for multi-class multi-label classification task (Kiritchenko-like multi-label F-scores)
- AUPROC.R: function to compute AUROC and AUPRC through the R package precrec
Even if HTD-DAG and TPR-DAG algorithm depend on several other source codes, loading them in the R environment is straightforward. For instance to load the high-level function computing the HTD-DAG algorithm, just open the R environment in the same folder where you download the HierDAG library and type:
source("Do.HTD.R");
NB: to rightly load the HierDAG library, the following R libraries are required:
library("graph");
library("RBGL");
library("Rgraphviz");
library("PerfMeas");
library("precrec");
library("preprocessCore");