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,
 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: 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, the graphical lasso, or the TIGER
 (tuning-insensitive graph estimation and regression) method, and the first
 two can be further accelerated by the lossy screening rule preselecting
 the neighborhood of each variable by correlation thresholding. We target
 on high-dimensional data analysis usually d >> n, and the computation
 is memory-optimized using the sparse matrix output. We also provide a
 computationally efficient approach, correlation thresholding graph estimation.
 Three regularization/thresholding parameter selection methods are included
 in this package: (1)stability approach for regularization selection (2)
 rotation information criterion (3) extended Bayesian information criterion
 which is only available for the graphical lasso.