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Source: r-cran-huge
Standards-Version: 4.7.3
Maintainer: Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
Uploaders:
Andreas Tille <tille@debian.org>,
Joost van Baal-Ilić <joostvb@debian.org>,
Section: gnu-r
Testsuite: autopkgtest-pkg-r
Build-Depends:
debhelper-compat (= 13),
dh-r,
r-base-dev,
r-cran-matrix,
r-cran-igraph,
r-cran-mass,
r-cran-rcpp,
r-cran-rcppeigen,
architecture-is-64-bit,
architecture-is-little-endian,
Vcs-Browser: https://salsa.debian.org/r-pkg-team/r-cran-huge
Vcs-Git: https://salsa.debian.org/r-pkg-team/r-cran-huge.git
Homepage: https://cran.r-project.org/package=huge
Rules-Requires-Root: no
Package: r-cran-huge
Architecture: any
Depends:
${R:Depends},
${shlibs:Depends},
${misc:Depends},
Recommends:
${R:Recommends},
Suggests:
${R:Suggests},
Description: GNU R high-dimensional undirected graph estimation
Provides a general framework for high-dimensional undirected graph
estimation. It integrates data preprocessing, neighborhood screening,
graph estimation, and model selection techniques into a pipeline. In
preprocessing stage, the nonparanormal(npn) transformation is applied to
help relax the normality assumption. In the graph estimation stage, the
graph structure is estimated by Meinshausen-Buhlmann graph estimation or
the graphical lasso, and both methods can be further accelerated by the
lossy screening rule preselecting the neighborhood of each variable by
correlation thresholding.
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