Other packages > Find by keyword >

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]
All associated links for this package
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 04 Jun. 2026.
Previous versions and publish date:
No previous versions
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
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

golem  
A Framework for Robust Shiny Applications
An opinionated framework for building a production-ready 'Shiny' application. This package contains ...
Download / Learn more Package Citations See dependency  
murphydiagram  
Murphy Diagrams for Forecast Comparisons
Data and code for the paper by Ehm, Gneiting, Jordan and Krueger ('Of Quantiles and Expectiles: Con ...
Download / Learn more Package Citations See dependency  
crplyr  
A 'dplyr' Interface for Crunch
In order to facilitate analysis of datasets hosted on the Crunch data platform ...
Download / Learn more Package Citations See dependency  
shinybusy  
Busy Indicators and Notifications for 'Shiny' Applications
Add indicators (spinner, progress bar, gif) in your 'shiny' applications to show the user that the ...
Download / Learn more Package Citations See dependency  
phers  
Calculate Phenotype Risk Scores
Use phenotype risk scores based on linked clinical and genetic data to study Mendelian disease and ...
Download / Learn more Package Citations See dependency  
nextGenShinyApps  
Craft Exceptional 'R Shiny' Applications and Dashboards with Novel Responsive Tools
Nove responsive tools for designing and developing 'Shiny' dashboards and applications. The scripts ...
Download / Learn more Package Citations See dependency  

27,268

R Packages

233,548

Dependencies

72,590

Author Associations

27,205

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA