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sparsereg  

Sparse Bayesian Models for Regression, Subgroup Analysis, and Panel Data
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("sparsereg", "1.2")



Attach the package and use:
library("sparsereg")
Maintained by
Marc Ratkovic
[Scholar Profile | Author Map]
First Published: 2015-07-21
Latest Update: 2016-03-10
Description:
Sparse modeling provides a mean selecting a small number of non-zero effects from a large possible number of candidate effects.This package includes a suite of methods for sparse modeling: estimation via EM or MCMC, approximate confidence intervals with nominal coverage, and diagnostic and summary plots.The method can implement sparse linear regression and sparse probit regression.Beyond regression analyses, applications include subgroup analysis, particularly for conjoint experiments, and panel data. Future versions will include extensions tomodels with truncated outcomes, propensity score, and instrumental variable analysis.
How to cite:
Marc Ratkovic (2015). sparsereg: Sparse Bayesian Models for Regression, Subgroup Analysis, and Panel Data. R package version 1.2, https://cran.r-project.org/web/packages/sparsereg. Accessed 29 Mar. 2025.
Previous versions and publish date:
1.0 (2015-07-21 07:53), 1.1 (2015-10-01 13:19)
Other packages that cited sparsereg R package
View sparsereg citation profile
Other R packages that sparsereg depends, imports, suggests or enhances
Complete documentation for sparsereg
Functions, R codes and Examples using the sparsereg R package
Some associated functions: difference . plot.sparsereg . print.sparsereg . sparsereg-internal . sparsereg-package . sparsereg . summary.sparsereg . violinplot . 
Some associated R codes: RcppExports.R . cleandata.R . subgroup.R . updateREs.R . utility.R .  Full sparsereg package functions and examples
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