Author: Wenxuan Zhong <wenxuan@stat.harvard.edu>, Ping Ma
Description: This is the package for doing model based fucntional

Author: Wenxuan Zhong <wenxuan@illinois.edu>, Ping Ma
Description: This is the package for doing model based functional

Title: Modeling Functional Data in Finance
Suggests: odesolve, R.matlab, quadprog, DierckxSpline, nlme
Description: The package contains functions designed to modeling or
analyzing the functional data arising in financial research and
practice, as well as some interesting data sets.

Title: MultiFractal Detrended Fluctuation Analysis
Description: Applies the MultiFractal Detrended Fluctuation Analysis (MFDFA) to time
series. The MFDFA() function proposed in this package was used in Laib et al.
Note: The originale code was in matlab, see details below.

Description: 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
See references for more information. Interested users can find a parallel version of

Title: Multivariate Functional Halfspace Depth
Imports: deldir, depth, depthTools, matrixStats
Description: Multivariate functional halfspace depth and median for two-dimensional functional data.

Package: MFKnockoffs
Type: Package
Title: Model-Free Knockoff Filter for Controlled Variable Selection
Version: 0.9.1
Date: 2017-09-27
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. 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>.
License: GPL-3
URL: https://statweb.stanford.edu/~candes/MF_Knockoffs/index.html
Depends: methods, stats
Imports: Rdsdp, Matrix, corpcor, glmnet, RSpectra, gtools
Suggests: knitr, testthat, rmarkdown, lars, ranger, stabs, flare, doMC,
parallel
RoxygenNote: 6.0.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2017-09-29 00:41:19 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: 2017-09-29 08:47:26 UTC