Package: rpc 2.0.3
Somak Dutta
rpc: Ridge Partial Correlation
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.
Authors:
rpc_2.0.3.tar.gz
rpc_2.0.3.tar.gz(r-4.5-noble)rpc_2.0.3.tar.gz(r-4.4-noble)
rpc_2.0.3.tgz(r-4.4-emscripten)rpc_2.0.3.tgz(r-4.3-emscripten)
rpc.pdf |rpc.html✨
rpc/json (API)
| # Install 'rpc' in R: |
| install.packages('rpc', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 hours agofrom:c472cb36e6. Checks:3 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 22 2025 |
| R-4.5-linux-x86_64 | OK | Mar 22 2025 |
| R-4.4-linux-x86_64 | OK | Mar 22 2025 |
Exports:eBICrpcXXt.compute
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Model selection using extended BIC | eBIC |
| Ridge Partial Correlations | rpc |
| XXt Computation | XXt.compute |
