person(c("Boris", "P"), "Hejblum", role = c("aut", "cre"),
person(c("Jennifer", "A"), "Sinnot", role = c("aut"),
Description: Kernel Machine Score Test for Pathway Analysis in the Presence of
Semi-Competing Risks. Method is detailed in: Neykov, Hejblum & Sinnott (2018)
Packaged: 2019-08-20 17:00:22 UTC; borishejblum

Title: Spatio-Temporal Adaptive Kernel Estimators for Intensities
person("Jonatan A", "González", , "jonathan.gonzalez@kaust.edu.sa",
Maintainer: Jonatan A González <jonathan.gonzalez@kaust.edu.sa>
Description: 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>.
Imports: misc3d, sparr, spatstat.core, spatstat.geom, spatstat.random,
Author: Jonatan A González [aut, cre] (<https://orcid.org/0000-0002-2296-5271>),

Imports: misc3d, sparr, spatstat.explore, spatstat.geom,

Title: Visualization of the KESER Network
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('Verity Research', role = c('aut')),
Description: 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>.
Imports: config (>= 0.3.1), data.table, dplyr, DT, ggplot2, golem (>=
0.3.1), plotly, reactable, rintrojs, rlang, shinycssloaders,
Depends: R (>= 3.5.0), shinyBS, shiny, htmltools
Suggests: rmarkdown, knitr, shinytest, testthat (>= 3.0.0)

Title: Matrix Exponential using Krylov Subspace Routines
Author: Meabh G. McCurdy  <mmccurdy01@qub.ac.uk>
Maintainer: Meabh G. McCurdy  <mmccurdy01@qub.ac.uk>
Copyright: See kexpmv/inst/notes/LAPACK_LICENSE.txt for src/lapack.f.
Description: Implements functions from 'EXPOKIT'
Includes functions for small dense matrices along with functions
for large sparse matrices. The functions for large sparse matrices
implement Krylov subspace methods which help minimise the
computational complexity for matrix exponentials. 'Kexpmv' can be
utilised to calculate both the matrix exponential in isolation
along with the product of the matrix exponential and a vector.