Package: sahpm 1.0.1
sahpm: Variable Selection using Simulated Annealing
Highest posterior model is widely accepted as a good model among available models. In terms of variable selection highest posterior model is often the true model. Our stochastic search process SAHPM based on simulated annealing maximization method tries to find the highest posterior model by maximizing the model space with respect to the posterior probabilities of the models. This package currently contains the SAHPM method only for linear models. The codes for GLM will be added in future.
Authors:
sahpm_1.0.1.tar.gz
sahpm_1.0.1.zip(r-4.5)sahpm_1.0.1.zip(r-4.4)sahpm_1.0.1.zip(r-4.3)
sahpm_1.0.1.tgz(r-4.5-any)sahpm_1.0.1.tgz(r-4.4-any)sahpm_1.0.1.tgz(r-4.3-any)
sahpm_1.0.1.tar.gz(r-4.5-noble)sahpm_1.0.1.tar.gz(r-4.4-noble)
sahpm_1.0.1.tgz(r-4.4-emscripten)sahpm_1.0.1.tgz(r-4.3-emscripten)
sahpm.pdf |sahpm.html✨
sahpm/json (API)
| # Install 'sahpm' in R: |
| install.packages('sahpm', repos = c('https://maitya02.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 years agofrom:b7b86c8412. Checks:1 OK, 7 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Feb 02 2025 |
| R-4.5-win | NOTE | Feb 02 2025 |
| R-4.5-mac | NOTE | Feb 02 2025 |
| R-4.5-linux | NOTE | Feb 02 2025 |
| R-4.4-win | NOTE | Feb 02 2025 |
| R-4.4-mac | NOTE | Feb 02 2025 |
| R-4.3-win | NOTE | Feb 02 2025 |
| R-4.3-mac | NOTE | Feb 02 2025 |
Exports:sahpmlm
Dependencies:mvtnorm
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| This implements the stochastic search based on Simulated Anneling strategy. | sahpmlm |
