cli (>= 3.0.0), curl, glue, magrittr, openssl, R6, rappdirs,
rlang (>= 1.0.0), withr

askpass, bench, clipr, covr, docopt, httpuv, jose, jsonlite,
knitr, purrr, rmarkdown, testthat (>= 3.0.0), tibble, webfakes,
xml2

Get Data from the French National Database on Water 'Hub'Eau'
c(
person("David", "Dorchies", , "david.dorchies@inrae.fr", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-6595-7984")),
person("Pascal", "Irz", , "pascal.irz@ofb.gouv.fr", role = "ctb")
)
Collection of functions to help retrieving data from 'Hub'Eau' the
free and public French National APIs on water <https://hubeau.eaufrance.fr/>.
David Dorchies [aut, cre] (<https://orcid.org/0000-0002-6595-7984>),
Pascal Irz [ctb]

Providing Data from the US Department of Housing and Urban
Development
Provides functions to access data from the US Department of Housing and Urban Development <https://www.huduser.gov/portal/dataset/fmr-api.html>.

Distinct Colours Palettes Based on 'iwanthue'
Creating effective colour palettes for figures is
challenging. This package generates and plot palettes of optimally
distinct colours in perceptually uniform colour space, based on
'iwanthue' <http://tools.medialab.sciences-po.fr/iwanthue/>.
This is done through k-means clustering of CIE Lab colour space,
according to user-selected constraints on hue, chroma, and
lightness.
person("John", "Baumgartner", email = "johnbaums@gmail.com",
role = c("aut", "cre"))

Distinct Colour Palettes Based on 'iwanthue'
c(
person("John", "Baumgartner", email = "johnbaums@gmail.com",
role = c("aut", "cre"), comment = c(ORCID = "0000-0002-8898-0300")),
person("Russell", "Dinnage", email = "r.dinnage@gmail.com",
role = "aut")
)
John Baumgartner [aut, cre] (<https://orcid.org/0000-0002-8898-0300>),
Russell Dinnage [aut]

Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, Larry
Wasserman
Tuo Zhao <tourzhao@gmail.com>, Han Liu <hanliu@cs.jhu.edu>
The package "huge" provides a general framework for
high-dimensional undirected graph estimation. The package
integrates data preprocessing (Gaussianization), graph
screening, graph estimation, and model selection techniques
into a pipeline. The nonparanormal transformation is applied to
preprocess the data and helps relax the normality assumption.
The graph screening subroutine preselects the graph
neighborhood of each variable. In the graph estimation stage,
the structure of either the whole graph or a pre-specified
sub-graph is estimated by the Meinshausen & Buhlmann Graph
Estimation via Lasso (GEL) strategy on the pre-screened data.
In the case d << n, the computation is memory optimized and is
targeted on larger-sclae problems (with d>3000). We also
provide another efficient method, Graph Estimation via
Correlation Approximation (GECA). Two regularization parameter
selection methods are included in this package: (1) StARS:
stability approach for regularization selection (2) extended
Bayesian information criterion (BIC) based on
pseudo-likelihood.