Rcpp (>= 1.0.9), methods, stats, sparseinv, fields, Matrix(>=
1.5.1), parallel, GpGp, FNN

Creating Continuously Integrated Package Repositories from
Manifests
Gabriel Becker[aut,cre], Cory Barr [cre,ctb]
Repository based tools for department and analysis level
reproducibility. 'GRANBase' allows creation of custom branched,
continuous integration-ready R repositories, including incremental testing of only packages which have changed versions since the last repository build.

Repository based tools for department and analysis level
reproducibility. 'GRANBase' allows creation of custom branched, continuous
integration-ready R repositories, including incremental testing of only packages
which have changed versions since the last repository build.

Gabriel Becker[aut,cre], Cory Barr [cre,ctb], Dinakar Kulkarni [aut,ctb]
tools, utils, htmlTable, dplyr, sendmailR, covr, RCurl,
jsonlite, stringi, hexSticker, stats

tools, utils, htmlTable (>= 1.11.0), dplyr, sendmailR, covr,
RCurl, jsonlite, stringi, hexSticker, stats

tools, utils, htmlTable (>= 1.11.0), dplyr, sendmailR, covr,
RCurl, jsonlite, stringi, stats, markdown

tools, utils, htmlTable (>= 1.11.0), dplyr, sendmailR, covr,
RCurl, jsonlite, stringi, stats, markdown, desc
parallel, rmarkdown, hexSticker, knitr, DT (>= 0.2)

Gabriel Becker[aut,cre], Dinakar Kulkarni [aut,ctb]
Provides the classes and methods for GRANRepository
objects that are used within the 'GRAN' build framework for R packages.
This is primarily used by the 'GRANBase' package and repositories
that are created by it.

Gene-Ranking Analysis of Pathway Expression
Gene-Ranking Analysis of Pathway Expression (GRAPE) is a tool for
summarizing the consensus behavior of biological pathways in the form of a
template, and for quantifying the extent to which individual samples deviate
from the template. GRAPE templates are based only on the relative rankings
of the genes within the pathway and can be used for classification of tissue
types or disease subtypes. GRAPE can be used to represent gene-expression
samples as vectors of pathway scores, where each pathway score indicates the
departure from a given collection of reference samples. The resulting pathway-
space representation can be used as the feature set for various applications,
including survival analysis and drug-response prediction.
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GRAPE is a generalization and extension of DIRAC, originally implemented in:
Eddy, J.A., et al. (2010) <DOI:10.1371/journal.pcbi.1000792>. As a result, some
of the software below may have been previously published in Matlab by the DIRAC
authors and can be found at:
https://price.systemsbiology.org/pricelab-resources/software/.