Package: MFKnockoffs
Type: Package
Title: Model-Free Knockoff Filter for Controlled Variable Selection
Version: 0.9
Date: 2017-07-07
Authors@R: c(person("Rina", "Foygel Barber", role = c("ctb"),
comment="Development of the original fixed-design Knockoffs"),
person("Emmanuel", "Candes", role = c("ctb"),
comment="Development of Model-Free Knockoffs and original fixed-design Knockoffs"),
person("Lucas", "Janson", role = c("ctb"),
comment="Development of Model-Free Knockoffs"),
person("Evan", "Patterson", role = c("aut"),
comment="Original R package for the original fixed-design Knockoffs"),
person("Matteo", "Sesia", role = c("aut","cre"),
comment="R package for Model-Free Knockoffs", email="msesia@stanford.edu"))
Description: Model-free knockoffs are a general procedure for controlling the false
discovery rate (FDR) when performing variable selection.
License: GPL-3
URL: https://statweb.stanford.edu/~candes/MF_Knockoffs/index.html
Depends: methods, stats
Imports: scs, Matrix, matrixcalc, corpcor
Suggests: knitr, testthat, glmnet, lars, ranger, stabs, flare, rARPACK,
RSpectra, rmarkdown, gtools, doMC, parallel
RoxygenNote: 6.0.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2017-07-08 04:48:10 UTC; msesia
Author: Rina Foygel Barber [ctb] (Development of the original fixed-design
Knockoffs),
Emmanuel Candes [ctb] (Development of Model-Free Knockoffs and original
fixed-design Knockoffs),
Lucas Janson [ctb] (Development of Model-Free Knockoffs),
Evan Patterson [aut] (Original R package for the original fixed-design
Knockoffs),
Matteo Sesia [aut, cre] (R package for Model-Free Knockoffs)
Maintainer: Matteo Sesia <msesia@stanford.edu>
Repository: CRAN
Date/Publication: 2017-07-08 05:15:46 UTC

Title: Maximal Fat Oxidation and Kinetics Calculation
Description: Calculate the maximal fat oxidation, the exercise intensity that elicits the
maximal fat oxidation and the SIN model to represent the fat oxidation kinetics.
Three variables can be obtained from the SIN model: dilatation, symmetry and translation.
Examples of these methods can be found in Montes de Oca et al (2021) <doi:10.1080/17461391.2020.1788650>
Imports: stats, dplyr, ggplot2, magrittr, tibble, minpack.lm, openxlsx,
Jesus G Ponce-Gonzalez [ctb] (<https://orcid.org/0000-0002-5982-7761>),