Package: HEMDAG 2.7.4
Marco Notaro
HEMDAG: Hierarchical Ensemble Methods for Directed Acyclic Graphs
An implementation of several Hierarchical Ensemble Methods (HEMs) for Directed Acyclic Graphs (DAGs). 'HEMDAG' package: 1) reconciles flat predictions with the topology of the ontology; 2) can enhance the predictions of virtually any flat learning methods by taking into account the hierarchical relationships between ontology classes; 3) provides biologically meaningful predictions that always obey the true-path-rule, the biological and logical rule that governs the internal coherence of biomedical ontologies; 4) is specifically designed for exploiting the hierarchical relationships of DAG-structured taxonomies, such as the Human Phenotype Ontology (HPO) or the Gene Ontology (GO), but can be safely applied to tree-structured taxonomies as well (as FunCat), since trees are DAGs; 5) scales nicely both in terms of the complexity of the taxonomy and in the cardinality of the examples; 6) provides several utility functions to process and analyze graphs; 7) provides several performance metrics to evaluate HEMs algorithms. (Marco Notaro, Max Schubach, Peter N. Robinson and Giorgio Valentini (2017) <doi:10.1186/s12859-017-1854-y>).
Authors:
HEMDAG_2.7.4.tar.gz
HEMDAG_2.7.4.zip(r-4.5)HEMDAG_2.7.4.zip(r-4.4)HEMDAG_2.7.4.zip(r-4.3)
HEMDAG_2.7.4.tgz(r-4.4-x86_64)HEMDAG_2.7.4.tgz(r-4.4-arm64)HEMDAG_2.7.4.tgz(r-4.3-x86_64)HEMDAG_2.7.4.tgz(r-4.3-arm64)
HEMDAG_2.7.4.tar.gz(r-4.5-noble)HEMDAG_2.7.4.tar.gz(r-4.4-noble)
HEMDAG_2.7.4.tgz(r-4.4-emscripten)HEMDAG_2.7.4.tgz(r-4.3-emscripten)
HEMDAG.pdf |HEMDAG.html✨
HEMDAG/json (API)
NEWS
| # Install 'HEMDAG' in R: |
| install.packages('HEMDAG', repos = c('https://marconotaro.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/marconotaro/hemdag/issues
- L - Small real example datasets
- S - Small real example datasets
- W - Small real example datasets
- g - Small real example datasets
- test.index - Small real example datasets
Last updated 3 years agofrom:0ed094abc6. Checks:OK: 1 ERROR: 1 NOTE: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 10 2024 |
| R-4.5-win-x86_64 | NOTE | Sep 10 2024 |
| R-4.5-linux-x86_64 | ERROR | Sep 10 2024 |
| R-4.4-win-x86_64 | NOTE | Sep 10 2024 |
| R-4.4-mac-x86_64 | NOTE | Sep 10 2024 |
| R-4.4-mac-aarch64 | NOTE | Sep 10 2024 |
| R-4.3-win-x86_64 | NOTE | Sep 10 2024 |
| R-4.3-mac-x86_64 | NOTE | Sep 10 2024 |
| R-4.3-mac-aarch64 | NOTE | Sep 10 2024 |
Exports:adj.upper.triauprc.single.classauprc.single.over.classesauroc.single.classauroc.single.over.classesbuild.ancestorsbuild.ancestors.bottom.upbuild.ancestors.per.levelbuild.childrenbuild.children.bottom.upbuild.children.top.downbuild.consistent.graphbuild.descendantsbuild.descendants.bottom.upbuild.descendants.per.levelbuild.edges.from.hpo.obobuild.parentsbuild.parents.bottom.upbuild.parents.top.downbuild.parents.topological.sortingbuild.scores.matrix.from.listbuild.scores.matrix.from.tuplabuild.subgraphbuild.submatrixcheck.annotation.matrix.integritycheck.dag.integritycheck.hierarchycheck.hierarchy.single.samplecompute.flipped.graphcompute.fmaxconstraints.matrixcreate.stratified.fold.dfdistances.from.leavesF.measure.multilabelfind.best.ffind.leavesfull.annotation.matrixgpavgpav.holdoutgpav.over.examplesgpav.parallelgpav.vanillagraph.levelshtdhtd.holdouthtd.vanillalexicographical.topological.sortnormalize.maxobozinski.andobozinski.holdoutobozinski.maxobozinski.methodsobozinski.orprecision.at.all.recall.levels.single.classprecision.at.given.recall.levels.over.classesread.graphread.undirected.graphroot.nodescores.normalizationspecific.annotation.listspecific.annotation.matrixstratified.cv.data.over.classesstratified.cv.data.single.classtpr.dagtpr.dag.cvtpr.dag.holdouttransitive.closure.annotationstupla.matrixunstratified.cv.dataweighted.adjacency.matrixwrite.graph
Dependencies:assertthatBHBiocGenericsclicodetoolscolorspacedata.tabledoParallelfansifarverforeachggplot2gluegraphgridExtragtableisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrprecrecpreprocessCoreR6RBGLRColorBrewerRcpprlangscalestibbleutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| HEMDAG: Hierarchical Ensemble Methods for Directed Acyclic Graphs | HEMDAG-package HEMDAG |
| Binary upper triangular adjacency matrix | adj.upper.tri |
| AUPRC measures | auprc auprc.single.class auprc.single.over.classes |
| AUROC measures | auroc auroc.single.class auroc.single.over.classes |
| Build ancestors | build.ancestors build.ancestors.bottom.up build.ancestors.per.level |
| Build children | build.children build.children.bottom.up build.children.top.down |
| Build consistent graph | build.consistent.graph |
| Build descendants | build.descendants build.descendants.bottom.up build.descendants.per.level |
| Parse an HPO obo file | build.edges.from.hpo.obo |
| Build parents | build.parents build.parents.bottom.up build.parents.top.down build.parents.topological.sorting |
| Build score matrix | build.scores.matrix build.scores.matrix.from.list build.scores.matrix.from.tupla |
| Build subgraph | build.subgraph |
| Build submatrix | build.submatrix |
| Annotation matrix checker | check.annotation.matrix.integrity |
| DAG checker | check.dag.integrity |
| Flip graph | compute.flipped.graph |
| Constraints matrix | constraints.matrix |
| DataFrame for stratified cross validation | create.stratified.fold.df |
| Distances from leaves | distances.from.leaves |
| Small real example datasets | example.datasets g L S test.index W |
| Best hierarchical F-score | find.best.f |
| Leaves | find.leaves |
| Compute Fmax | compute.fmax fmax |
| Full annotation matrix | full.annotation.matrix |
| Generalized Pool-Adjacent Violators (GPAV) | gpav |
| GPAV holdout | gpav.holdout |
| GPAV over examples | gpav.over.examples |
| GPAV over examples - parallel implementation | gpav.parallel |
| GPAV vanilla | gpav.vanilla |
| Build graph levels | graph.levels |
| Hierarchical constraints checker | check.hierarchy check.hierarchy.single.sample hierarchical.checkers |
| HTD-DAG | htd |
| HTD-DAG holdout | htd.holdout |
| HTD-DAG vanilla | htd.vanilla |
| Lexicographical topological sorting | lexicographical.topological.sort |
| multilabel F-measure | F.measure.multilabel F.measure.multilabel,matrix,matrix-method multilabel.F.measure |
| Max normalization | normalize.max |
| Obozinski heuristic methods | obozinski.and obozinski.heuristic.methods obozinski.max obozinski.or |
| Obozinski's heuristic methods - holdout | obozinski.holdout |
| Obozinski's heuristic methods calling | obozinski.methods |
| Precision-Recall curves | precision.at.all.recall.levels.single.class precision.at.given.recall.levels.over.classes pxr |
| Read a directed graph from a file | read.graph |
| Read an undirected graph from a file | read.undirected.graph |
| Root node | root.node |
| Scores normalization function | scores.normalization |
| Specific annotations list | specific.annotation.list |
| Specific annotation matrix | specific.annotation.matrix |
| Stratified cross validation | stratified.cross.validation stratified.cv.data.over.classes stratified.cv.data.single.class |
| TPR-DAG ensemble variants | tpr.dag |
| TPR-DAG cross-validation experiments | tpr.dag.cv |
| TPR-DAG holdout experiments | tpr.dag.holdout |
| Transitive closure of annotations | transitive.closure.annotations |
| Tupla matrix | tupla.matrix |
| Unstratified cross validation | unstratified.cv.data |
| Weighted adjacency matrix | weighted.adjacency.matrix |
| Write a directed graph on file | write.graph |