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probe  

Sparse High-Dimensional Linear Regression with PROBE
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("probe", "1.1")



Attach the package and use:
library("probe")
Maintained by
Alexander McLain
[Scholar Profile | Author Map]
First Published: 2023-10-31
Latest Update: 2023-10-31
Description:
Implements an efficient and powerful Bayesian approach for sparse high-dimensional linear regression. It uses minimal prior assumptions on the parameters through plug-in empirical Bayes estimates of hyperparameters. An efficient Parameter-Expanded Expectation-Conditional-Maximization (PX-ECM) algorithm estimates maximum a posteriori (MAP) values of regression parameters and variable selection probabilities. The PX-ECM results in a robust computationally efficient coordinate-wise optimization, which adjusts for the impact of other predictor variables. The E-step is motivated by the popular two-group approach to multiple testing. The result is a PaRtitiOned empirical Bayes Ecm (PROBE) algorithm applied to sparse high-dimensional linear regression, implemented using one-at-a-time or all-at-once type optimization. More information can be found in McLain, Zgodic, and Bondell (2022) .
How to cite:
Alexander McLain (2023). probe: Sparse High-Dimensional Linear Regression with PROBE. R package version 1.1, https://cran.r-project.org/web/packages/probe. Accessed 29 Mar. 2025.
Previous versions and publish date:
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Other packages that cited probe R package
View probe citation profile
Other R packages that probe depends, imports, suggests or enhances
Complete documentation for probe
Functions, R codes and Examples using the probe R package
Some associated functions: Sim_data . Sim_data_cov . Sim_data_test . e_step_func . m_step_regression . predict_probe_func . probe-package . probe . probe_one . 
Some associated R codes: RcppExports.R . e_step_func.R . m_step_func.R . predict_probe_func.R . probe-package.R . probe_wrapper.R . probe_wrapper_one.R .  Full probe package functions and examples
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