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sahpm

Variable Selection using Simulated Annealing

v1.0.1 · Feb 24, 2022 · GPL-2

Description

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.

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dependencies in R code

Namespace in Imports field not imported from: ‘mvtnorm’
  All declared Imports should be used.
NOTE r-devel-linux-x86_64-fedora-gcc

dependencies in R code

Namespace in Imports field not imported from: ‘mvtnorm’
  All declared Imports should be used.
OK r-devel-macos-arm64

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OK r-devel-windows-x86_64

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Check History

NOTE 12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026
NOTE r-devel-linux-x86_64-fedora-clang

dependencies in R code

Namespace in Imports field not imported from: ‘mvtnorm’
  All declared Imports should be used.
NOTE r-devel-linux-x86_64-fedora-gcc

dependencies in R code

Namespace in Imports field not imported from: ‘mvtnorm’
  All declared Imports should be used.

Dependency Network

Dependencies Reverse dependencies mvtnorm sahpm

Version History

new 1.0.1 Mar 10, 2026