recipe r-hemdag

a collection of Hierarchical Ensemble Methods (HEMs) for Directed Acyclic Graphs (DAGs).

Homepage:

https://CRAN.R-project.org/package=HEMDAG

Documentation:

https://hemdag.readthedocs.io

Developer docs:

https://github.com/marconotaro/hemdag

License:

GPL3 / GPL-3.0-or-later

Recipe:

/r-hemdag/meta.yaml

[](https://hemdag.readthedocs.io/en/latest/?badge=latest)

HEMDAG library: * implements several Hierarchical Ensemble Methods (HEMs) for Directed Acyclic Graphs (DAGs); * reconciles flat predictions with the topology of the ontology; * can enhance predictions of virtually any flat learning methods by taking into account the hierarchical relationships between ontology classes; * provides biologically meaningful predictions that always obey the true-path-rule, the biological and logical rule that governs the internal coherence of biomedical ontologies; * 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 (e.g. FunCat), since trees are DAGs; * scales nicely both in terms of the complexity of the taxonomy and in the cardinality of the examples; * provides several utility functions to process and analyze graphs; * provides several performance metrics to evaluate HEMs algorithms.

package r-hemdag

Versions:

2.7.4-8,  2.7.4-7,  2.7.4-6,  2.7.4-5,  2.7.4-4,  2.7.4-3,  2.7.4-2,  2.7.4-1,  2.7.4-0, 

2.7.4-8,  2.7.4-7,  2.7.4-6,  2.7.4-5,  2.7.4-4,  2.7.4-3,  2.7.4-2,  2.7.4-1,  2.7.4-0,  2.7.3-2,  2.7.3-0,  2.6.1-1,  2.6.1-0,  2.6.0-0,  2.5.9-0,  2.4.8-0,  2.4.7-1,  2.4.7-0,  2.2.5-1,  2.2.5-0,  2.1.3-0,  2.1.2-0,  2.0.1-0

Depends:

• on bioconductor-graph >=1.84.0,<1.85.0a0

• on bioconductor-preprocesscore >=1.68.0,<1.69.0a0

• on bioconductor-rbgl >=1.82.0,<1.83.0a0

• on libgcc >=13

• on libstdcxx >=13

• on r-base >=4.4,<4.5.0a0

• on r-doparallel

• on r-foreach

• on r-plyr

• on r-precrec

Additional platforms:

linux-aarch64

Installation

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install r-hemdag

to add into an existing workspace instead, run:

pixi add r-hemdag

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install r-hemdag

Alternatively, to install into a new environment, run:

conda create -n envname r-hemdag

with envname being the name of the desired environment.

Container

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

docker pull quay.io/biocontainers/r-hemdag:<tag>

(see r-hemdag/tags for valid values for <tag>).

Integrated deployment

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats

Link to this page

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/r-hemdag/README.html)