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sahpm  

Variable Selection using Simulated Annealing
View on CRAN: Click here


Download and install sahpm package within the R console
Install from CRAN:
install.packages("sahpm")

Install from Github:
library("remotes")
install_github("cran/sahpm")

Install by package version:
library("remotes")
install_version("sahpm", "1.0.1")



Attach the package and use:
library("sahpm")
Maintained by
Arnab Maity
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-02-24
Latest Update: 2022-02-24
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.
How to cite:
Arnab Maity (2022). sahpm: Variable Selection using Simulated Annealing. R package version 1.0.1, https://cran.r-project.org/web/packages/sahpm. Accessed 07 Nov. 2024.
Previous versions and publish date:
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View sahpm citation profile
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Complete documentation for sahpm
Functions, R codes and Examples using the sahpm R package
Some associated functions: sahpmlm . 
Some associated R codes: sahpmlm.R .  Full sahpm package functions and examples
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