Cross-Platform File System Operations Based on 'libuv'
c(
person("Jim", "Hester", email = "james.f.hester@gmail.com", role = c("aut", "cre")),
person("Hadley", "Wickham", , "hadley@rstudio.com", "aut"),
person("libuv project contributors", role = "cph", comment = "libuv library"),
person("Joyent, Inc. and other Node contributors", role = "cph", comment = "libuv library"),
person("RStudio", role = c("cph", "fnd"))
)
A cross-platform interface to file system operations, built on
top of the 'libuv' C library.
testthat, covr, pillar (>= 1.0.0), crayon, withr
Jim Hester [aut, cre],
Hadley Wickham [aut],
libuv project contributors [cph] (libuv library),
Joyent, Inc. and other Node contributors [cph] (libuv library),
RStudio [cph, fnd]

testthat, covr, pillar (>= 1.0.0), crayon, rmarkdown, knitr,
withr,

testthat, covr, pillar (>= 1.0.0), crayon, rmarkdown, knitr,
withr, spelling

testthat, covr, pillar (>= 1.0.0), tibble (>= 1.1.0), crayon,
rmarkdown, knitr, withr, spelling

testthat, covr, pillar (>= 1.0.0), tibble (>= 1.1.0), crayon,
rmarkdown, knitr, withr, spelling, vctrs (>= 0.3.0)

c(
person("Jim", "Hester", role = "aut"),
person("Hadley", "Wickham", , "hadley@rstudio.com", "aut"),
person("Gábor", "Csárdi", , "csardi.gabor@gmail.com", role = c("aut", "cre")),
person("libuv project contributors", role = "cph", comment = "libuv library"),
person("Joyent, Inc. and other Node contributors", role = "cph", comment = "libuv library"),
person("RStudio", role = c("cph", "fnd"))
)
Jim Hester [aut],
Hadley Wickham [aut],
Gábor Csárdi [aut, cre],
libuv project contributors [cph] (libuv library),
Joyent, Inc. and other Node contributors [cph] (libuv library),
RStudio [cph, fnd]

Feature Selection for Multivariate Time Series
person("Dmitry", "Pavlyuk", email = "Dmitry.Pavlyuk@tsi.lv",comment = c(ORCID = "0000-0003-3710-9678"), role = c("aut", "cre"))
Implements feature selection routines for multivariate time series (MTS).
The list of implemented algorithms includes:
own lags (independent MTS components),
distance-based (using external structure, e.g. Pfeifer and Deutsch (1980) <doi:10.2307/1268381>),
cross-correlation (see  Schelter et al. (2006, ISBN:9783527406234)),
graphical LASSO (see Haworth and Cheng (2014) <https://www.gla.ac.uk/media/Media_401739_smxx.pdf>),
random forest (see Pavlyuk (2020) "Random Forest Variable Selection for Sparse Vector Autoregressive Models" in Contributions to Statistics, in production),
least angle regression (see Gelper and Croux (2008) <https://lirias.kuleuven.be/retrieve/16024>),
mutual information (see  Schelter et al. (2006, ISBN:9783527406234), Liu et al. (2016) <doi:10.1109/ChiCC.2016.7554480>),
and partial spectral coherence (see Davis et al.(2016) <doi:10.1080/10618600.2015.1092978>).
In addition, the package implements functions for ensemble feature selection (using feature ranking and majority voting).
The package is implemented within Dmitry Pavlyuk's research project No. 1.1.1.2/VIAA/1/16/112 "Spatiotemporal urban traffic modelling using big data".
knitr, rmarkdown, sparsevar, plot.matrix, svMisc, MTS
Dmitry Pavlyuk [aut, cre] (<https://orcid.org/0000-0003-3710-9678>)