HEMDAG: Hierarchical Ensemble Methods for Directed Acyclic Graphs

An implementation of Hierarchical Ensemble Methods for Directed Acyclic Graphs (DAGs). The 'HEMDAG' package can be used to enhance the predictions of virtually any flat learning methods, by taking into account the hierarchical nature of the classes of a bio-ontology. 'HEMDAG' 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 it can be also safely applied to tree-structured taxonomies (as FunCat), since trees are DAGs. 'HEMDAG' scale nicely both in terms of the complexity of the taxonomy and in the cardinality of the examples. (Marco Notaro, Max Schubach, Peter N. Robinson and Giorgio Valentini (2017) <doi:10.1186/s12859-017-1854-y>).

Version: 2.1.3
Depends: R (≥ 2.10)
Imports: graph, RBGL, PerfMeas, precrec, preprocessCore, methods
Suggests: Rgraphviz
Published: 2018-05-21
Author: Marco Notaro [aut, cre] and Giorgio Valentini [aut] (AnacletoLab, Dipartimento di Informatica, Universita' degli Studi di Milano)
Maintainer: Marco Notaro <marco.notaro at unimi.it>
License: GPL (≥ 3)
NeedsCompilation: no
Citation: HEMDAG citation info
Materials: ChangeLog
CRAN checks: HEMDAG results


Reference manual: HEMDAG.pdf
Package source: HEMDAG_2.1.3.tar.gz
Windows binaries: r-devel: HEMDAG_2.1.3.zip, r-release: HEMDAG_2.1.3.zip, r-oldrel: HEMDAG_2.1.3.zip
OS X binaries: r-release: HEMDAG_2.1.3.tgz, r-oldrel: HEMDAG_2.1.3.tgz
Old sources: HEMDAG archive


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