Contains the MultiFractal Detrended Fluctuation Analysis (MFDFA),
MultiFractal Detrended Cross-Correlation Analysis (MFXDFA), and the Multiscale
Multifractal Analysis (MMA). The MFDFA() function proposed in this package was
used in Laib et al. (<doi:10.1016/j.chaos.2018.02.024> and <doi:10.1063/1.5022737>).
See references for more information. Interested users can find a parallel version of
the MFDFA() function on GitHub.

Multivariate functional halfspace depth and median for two-dimensional functional data.

Model-Free Knockoff Filter for Controlled Variable Selection
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"))
Model-free knockoffs are 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-free Knockoffs for High-dimensional Controlled Variable Selection",
2016, <arXiv:1610.02351>.
Rdsdp, Matrix, corpcor, glmnet, RSpectra, gtools
knitr, testthat, rmarkdown, lars, ranger, stabs, flare, doMC,
parallel
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)

Model-free knockoffs are a general procedure for controlling the false
discovery rate (FDR) when performing variable selection.
knitr, testthat, glmnet, lars, ranger, stabs, flare, rARPACK,
RSpectra, rmarkdown, gtools, doMC, parallel