Biometric Exploratory Analysis Creation House
c(person("Danni Yu", role=c("aut", "cre"), email="danni.yu@gmail.com"),
person("Michael Man", role=c("ctb"), email="mmz2001@gmail.com"  ) )
A platform is provided for interactive analyses with a goal of totally easy to develop, deploy, interact, and explore (TEDDIE). Using this package, users can create customized analyses and make them available to end users who can perform interactive analyses and save analyses to RTF or HTML files. It allows developers to focus on R code for analysis, instead of dealing with html or shiny code.

c(person("Danni Yu", role=c("aut", "cre"), email="danni.yu@gmail.com"),
person("Michael Man", role=c("aut"), email="mmz2001@gmail.com"  ),
person("ChenChen Yu", role=c("com") ) )
Danni Yu [aut, cre],
Michael Man [aut],
ChenChen Yu [com]

Bayesian Estimate of Age-specific Nest Survival Probabilities
Computing age specific nest survival rates based on a Bayesian hierarchical model. This package can handle unknown nest age, irregular visiting schedule and nest-specific covariates.

c(
person(
"Patrice", "Godard",
role = c("aut", "cre","cph"),
email = "patrice.godard@gmail.com"
)
)
An interface for the 'Neo4j' database providing
mapping between different identifiers of biological entities.
This Biological Entity Dictionary (BED)
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
resources.
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
using gene orthologs information.
A ready to use database is provided as
a 'Docker' image <https://hub.docker.com/r/patzaw/bed-ucb-human/>.
The method has been published by
Godard and van Eyll (2018) <doi:10.12688/f1000research.13925.3>.
dplyr, readr, stringr, utils, shiny (>= 0.13), DT, miniUI (>=
0.1.1), rstudioapi (>= 0.5)
knitr, rmarkdown, biomaRt, GEOquery, base64enc, htmltools,
webshot, RCurl