Title: Biological Entity Dictionary (BED)
Description: An interface for the 'Neo4j' database providing
mapping between different identifiers of biological entities.
has been developed to address three main challenges.
The first one is related to the completeness of identifier mappings.
Indeed, direct mapping information provided by the different systems
are not always complete and can be enriched by mappings provided by other
More interestingly, direct mappings not identified by any of these
resources can be indirectly inferred by using mappings to a third reference.
For example, many human Ensembl gene ID are not directly mapped to any
Entrez gene ID but such mappings can be inferred using respective mappings
to HGNC ID. The second challenge is related to the mapping of deprecated
identifiers. Indeed, entity identifiers can change from one resource
release to another. The identifier history is provided by some resources,
such as Ensembl or the NCBI, but it is generally not used by mapping tools.
The third challenge is related to the automation of the mapping process
according to the relationships between the biological entities of interest.
Indeed, mapping between gene and protein ID scopes should not be done
the same way than between two scopes regarding gene ID.
Also, converting identifiers from different organisms should be possible
Depends: R (>= 3.6), neo2R (>= 2.1.0), visNetwork
Imports: dplyr, readr, stringr, utils, shiny (>= 0.13), DT, miniUI (>=
Suggests: knitr, rmarkdown, biomaRt, GEOquery, base64enc, htmltools,

Imports: dplyr, readr, stringr, utils, shiny (>= 0.13), DT, htmltools,
Suggests: knitr, rmarkdown, biomaRt, GEOquery

Suggests: knitr, rmarkdown, biomaRt, GEOquery, base64enc, webshot

Title: Disentangles the effects of geographic and ecological isolation
Description: This package provides functions that allow users to quantify the relative
contributions of geographic and ecological distances to empirical patterns of genetic
differentiation on a landscape.  Specifically, the package contains a function that
calls a custom Markov chain Monte Carlo (MCMC) algorithm, which is used to estimate
the parameters of the inference model, as well as functions for performing MCMC