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 25 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

sitmo  
Parallel Pseudo Random Number Generator (PPRNG) 'sitmo' Header Files
Provided within are two high quality and fast PPRNGs that may be used in an 'OpenMP' parallel enviro ...
Download / Learn more Package Citations See dependency  
foster  
Forest Structure Extrapolation with R
Set of tools to streamline the modeling of the relationship betweensatellite imagery time series or ...
Download / Learn more Package Citations See dependency  
quickcode  
Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to im ...
Download / Learn more Package Citations See dependency  
airGRiwrm  
'airGR' Integrated Water Resource Management
Semi-distributed Precipitation-Runoff Modelling based on 'airGR' package models integrating human i ...
Download / Learn more Package Citations See dependency  
edeaR  
Exploratory and Descriptive Event-Based Data Analysis
Exploratory and descriptive analysis of event based data. Provides methods for describing and select ...
Download / Learn more Package Citations See dependency  

27,535

R Packages

236,180

Dependencies

73,223

Author Associations

27,536

Publication Badges

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