c(person("Rina", "Foygel Barber", role = c("ctb"),
comment="Development of the original Fixed-X Knockoffs"),
person("Emmanuel", "Candes", role = c("ctb"),
comment="Development of Model-X Knockoffs and original Fixed-X Knockoffs"),
person("Lucas", "Janson", role = c("ctb"),
comment="Development of Model-X Knockoffs"),
person("Evan", "Patterson", role = c("aut"),
comment="Earlier R package for the original Fixed-X Knockoffs"),
person("Matteo", "Sesia", role = c("aut","cre"),
comment="R package for Model-X Knockoffs", email="sesia@marshall.usc.edu"))
The knockoff filter is a general procedure for controlling the false discovery rate (FDR)
when performing variable selection.
For more information, see the website below and the accompanying paper: Candes et al.,
"Panning for gold: model-X knockoffs for high-dimensional controlled variable selection",
J. R. Statist. Soc. B (2018) 80, 3, pp. 551-577.
knitr, testthat, rmarkdown, lars, ranger, stabs, RPtests,
doMC, parallel
Rina Foygel Barber [ctb] (Development of the original Fixed-X
Knockoffs),
Emmanuel Candes [ctb] (Development of Model-X Knockoffs and original
Fixed-X Knockoffs),
Lucas Janson [ctb] (Development of Model-X Knockoffs),
Evan Patterson [aut] (Earlier R package for the original Fixed-X
Knockoffs),
Matteo Sesia [aut, cre] (R package for Model-X Knockoffs)

knitr, testthat, rmarkdown, lars, ranger, stabs, RPtests,
doParallel, parallel

Non-Uniform Memory Access ('NUMA') Optimized, Parallel K-Means
The k-means 'NUMA' Optimized Routine library or 'knor' is a highly optimized and fast library for computing k-means in parallel with accelerations for Non-Uniform Memory Access ('NUMA') architectures.

c(person("Disa", "Mhembere", role = c("aut", "cre"), email = "disa@jhu.edu"))

c(person("Disa", "Mhembere", role = c("aut", "cre"), email = "disa@jhu.edu"), person("Neurodata (https://neurodata.io)", role="cph"))

c(person("Disa", "Mhembere", role = c("aut", "cre"), email ="disa@cs.jhu.edu"), person("Neurodata (https://neurodata.io)", role="cph"))
The k-means 'NUMA' Optimized Routine library or 'knor' is a highly optimized and fast library for computing k-means in parallel with accelerations for Non-Uniform Memory Access ('NUMA') architectures. Disa Mhembere, Da Zheng, Carey E. Priebe, Joshua T. Vogelstein, Randal Burns (2017) <arXiv:1606.08905>.