Identification of Functionally Relevant Sub-Graphs
person(given = "Davis",
family = "Weaver",
role = c("aut", "cre"),
email = "davis.weaver@case.edu",
comment = c(orcid = "0000-0003-3086-497X"))
Provides a general framework for the identification of nodes that
are functionally related to a set of seeds in graph structured data.
In addition to being optimized for use with generic graphs,
We also provides support to analyze protein-protein
interactions networks from online repositories. Our methods are similar to
those described in Nibbe et.al (2010). <doi:10.1371/journal.pcbi.1000639>.
curl (>= 4.2), rlang, stats, magrittr, withr, readr, dplyr,
stringr, tidyr, tibble, igraph, Matrix, ensembldb, foreach,
doParallel, ggplot2, EnsDb.Hsapiens.v79
tidygraph, ggraph, testthat (>= 2.0.0), knitr, rmarkdown,
covr

Analysis of Graph-Structured Data with a Focus on
Protein-Protein Interaction Networks
Provides a general framework for the identification of nodes that are functionally related to a set of seeds in graph structured data. In addition to being optimized for use with generic graphs, we also provides support to analyze protein-protein interactions networks from online repositories. For more details on core method, refer to Nibbe et al. (2010) <doi:10.1371/journal.pcbi.1000639>.
rlang, stats, magrittr, withr, readr, dplyr, stringr, tidyr,
tibble, igraph (>= 1.2.0), Matrix, ensembldb, foreach,
doParallel, ggplot2, EnsDb.Hsapiens.v79, STRINGdb
tidygraph, ggraph, testthat (>= 2.0.0), knitr, rmarkdown

Cross-Fitting for Doubly Robust Evaluation of High-Dimensional
Surrogate Markers
c(person("Denis", "Agniel", role = c("aut", "cre"),
email = "dagniel@rand.org"),
person("Boris P.", "Hejblum", role = c("aut"),
email = "boris.hejblum@u-bordeaux.fr")
)
Doubly robust methods for evaluating surrogate markers as outlined in: Agniel D, Hejblum BP, Thiebaut R & Parast L (2022).
"Doubly robust evaluation of high-dimensional surrogate markers", Biostatistics <doi:10.1093/biostatistics/kxac020>. You can use these methods to determine how much of the overall treatment effect is explained by a (possibly high-dimensional) set of surrogate markers.
dplyr, gbm, glmnet, glue, parallel, pbapply, purrr, ranger,
RCAL, rlang, SIS, stats, SuperLearner, tibble, tidyr
Denis Agniel [aut, cre],
Boris P. Hejblum [aut]