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Computes the ridge partial correlation coefficients in a high or ultra-high dimensional linear regression problem. An extended Bayesian information criterion is also implemented for variable selection. Users provide the matrix of covariates as a usual dense matrix or a sparse matrix stored in a compressed sparse column format. Detail of the method is given in the manual.
| Version: | 2.0.3 |
| Imports: | Rcpp (≥ 1.0.11), Matrix |
| LinkingTo: | Rcpp |
| Suggests: | MatrixExtra |
| Published: | 2025-03-22 |
| DOI: | 10.32614/CRAN.package.rpc |
| Author: | Somak Dutta [aut, cre, cph], An Nguyen [aut, ctb], Run Wang [ctb], Vivekananda Roy [ctb] |
| Maintainer: | Somak Dutta <somakd at iastate.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| CRAN checks: | rpc results |
| Reference manual: | rpc.pdf |
| Package source: | rpc_2.0.3.tar.gz |
| Windows binaries: | r-devel: rpc_2.0.3.zip, r-release: rpc_2.0.3.zip, r-oldrel: rpc_2.0.3.zip |
| macOS binaries: | r-devel (arm64): rpc_2.0.3.tgz, r-release (arm64): rpc_2.0.3.tgz, r-oldrel (arm64): rpc_2.0.3.tgz, r-devel (x86_64): rpc_2.0.3.tgz, r-release (x86_64): rpc_2.0.3.tgz, r-oldrel (x86_64): rpc_2.0.3.tgz |
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