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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "sahpm" in publications use:'
type: software
license: GPL-2.0-only
title: 'sahpm: Variable Selection using Simulated Annealing'
version: 1.0.1
doi: 10.32614/CRAN.package.sahpm
abstract: 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:
- family-names: Maity
  given-names: Arnab
  email: arnab.maity@pfizer.com
repository: https://cranhaven.r-universe.dev
commit: 4e15be3dc38676fc515268c41aa73fd561038551
date-released: '2026-05-08'
contact:
- family-names: Maity
  given-names: Arnab
  email: arnab.maity@pfizer.com