c(person("Matey", "Neykov", role = c("aut"),
email = "mneykov@gmail.com"),
person("Boris P.", "Hejblum", role = c("aut", "cre"),
email = "bhejblum@hsph.harvard.edu"),
person("Jennifer A.", "Sinnot", role = c("aut"),
email = "jsinnott@stat.osu.edu")
)
Matey Neykov [aut],
Boris P. Hejblum [aut, cre],
Jennifer A. Sinnot [aut]

c(person("Matey", "Neykov", role = c("aut"),
email = "mneykov@gmail.com"),
person(c("Boris", "P"), "Hejblum", role = c("aut", "cre"),
email = "boris.hejblum@u-bordeaux.fr"),
person(c("Jennifer", "A"), "Sinnot", role = c("aut"),
email = "jsinnott@stat.osu.edu")
)
Kernel Machine Score Test for Pathway Analysis in the Presence of
Semi-Competing Risks. Method is detailed in: Neykov, Hejblum & Sinnott (2018)
<doi: 10.1177/0962280216653427>.
Matey Neykov [aut],
Boris P Hejblum [aut, cre],
Jennifer A Sinnot [aut]

Spatio-Temporal Adaptive Kernel Estimators for Intensities
c(
person("Jonatan A", "González", , "jonathan.gonzalez@kaust.edu.sa",
role = c("aut", "cre"), comment = c(ORCID = "0000-0002-2296-5271")),
person("Paula", "Moraga", , "paula.moraga@kaust.edu.sa",
role = c("aut"), comment = c(ORCID = "0000-0001-5266-0201"))
)
Adaptive estimation of the first-order intensity function of a spatio-temporal point process using kernels and variable bandwidths. The methodology used for estimation is presented in González and Moraga (2022). <arXiv:2208.12026>.
misc3d, sparr, spatstat.core, spatstat.geom, spatstat.random,
spatstat.utils
Jonatan A González [aut, cre] (<https://orcid.org/0000-0002-2296-5271>),
Paula Moraga [aut] (<https://orcid.org/0000-0001-5266-0201>)

misc3d, sparr, spatstat.explore, spatstat.geom,
spatstat.random, spatstat.utils

c(
person("Su-Chun", "Cheng", email = "scheng@parse-health.org", role = c('cre', 'aut')),
person("PARSE LTD", email = "software@parse-health.org", role = c('aut')),
person('VA CIPHER', role = c('aut')),
person('Verity Research', role = c('aut')),
person('CELEHS', role = c('aut')))
A shiny app to visualize the knowledge networks for the code concepts. Using co-occurrence matrices of EHR codes from Veterans Affairs (VA) and Massachusetts General Brigham (MGB), the knowledge extraction via sparse embedding regression (KESER) algorithm was used to construct knowledge networks for the code concepts. Background and details about the method can be found at Chuan et al. (2021) <doi:10.1038/s41746-021-00519-z>.
config (>= 0.3.1), data.table, dplyr, DT, ggplot2, golem (>=
0.3.1), plotly, reactable, rintrojs, rlang, shinycssloaders,
shinydashboard, shinydashboardPlus, shinyhelper, shinyWidgets,
stringr, visNetwork, yaml
rmarkdown, knitr, shinytest, testthat (>= 3.0.0)
Su-Chun Cheng [cre, aut],
PARSE LTD [aut],
VA CIPHER [aut],
Verity Research [aut],
CELEHS [aut]