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:Arnab Maity [aut, cre], Sanjib Basu [ctb]

sahpm_1.0.1.tar.gz
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sahpm_1.0.1.tgz(r-4.4-any)sahpm_1.0.1.tgz(r-4.3-any)
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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'))

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.70 score 192 downloads 1 exports 1 dependencies

Last updated 3 years agofrom:b7b86c8412. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 04 2024
R-4.5-winNOTENov 04 2024
R-4.5-linuxNOTENov 04 2024
R-4.4-winNOTENov 04 2024
R-4.4-macNOTENov 04 2024
R-4.3-winNOTENov 04 2024
R-4.3-macNOTENov 04 2024

Exports:sahpmlm

Dependencies:mvtnorm