Package: Ohit 1.0.0
Hai-Tang Chiou
Ohit: OGA+HDIC+Trim and High-Dimensional Linear Regression Models
Ing and Lai (2011) <doi:10.5705/ss.2010.081> proposed a high-dimensional model selection procedure that comprises three steps: orthogonal greedy algorithm (OGA), high-dimensional information criterion (HDIC), and Trim. The first two steps, OGA and HDIC, are used to sequentially select input variables and determine stopping rules, respectively. The third step, Trim, is used to delete irrelevant variables remaining in the second step. This package aims at fitting a high-dimensional linear regression model via OGA+HDIC+Trim.
Authors:
Ohit_1.0.0.tar.gz
Ohit_1.0.0.tar.gz(r-4.5-noble)Ohit_1.0.0.tar.gz(r-4.4-noble)
Ohit_1.0.0.tgz(r-4.4-emscripten)Ohit_1.0.0.tgz(r-4.3-emscripten)
Ohit.pdf |Ohit.html✨
Ohit/json (API)
| # Install 'Ohit' in R: |
| install.packages('Ohit', 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 7 years agofrom:50d8222c85. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 19 2024 |
| R-4.5-linux | OK | Nov 19 2024 |
Exports:OGAOhitpredict_Ohit
Dependencies:
