sparsereg: Sparse Bayesian Models for Regression, Subgroup Analysis, and Panel Data

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 to models with truncated outcomes, propensity score, and instrumental variable analysis.

AuthorMarc Ratkovic and Dustin Tingley
Date of publication2016-03-10 23:32:18
MaintainerMarc Ratkovic <ratkovic@princeton.edu>
LicenseGPL (>= 2)
Version1.2

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Files in this package

sparsereg
sparsereg/src
sparsereg/src/Makevars
sparsereg/src/subgroup.cpp
sparsereg/src/makethreeinter.cpp
sparsereg/src/makeinter.cpp
sparsereg/src/Makevars.win
sparsereg/src/RcppExports.cpp
sparsereg/NAMESPACE
sparsereg/R
sparsereg/R/cleandata.R sparsereg/R/updateREs.R sparsereg/R/subgroup.R sparsereg/R/utility.R sparsereg/R/RcppExports.R
sparsereg/MD5
sparsereg/DESCRIPTION
sparsereg/ChangeLog
sparsereg/man
sparsereg/man/sparsereg.Rd sparsereg/man/sparsereg-internal.Rd sparsereg/man/summary.sparsereg.Rd sparsereg/man/sparsereg-package.Rd sparsereg/man/print.sparsereg.Rd sparsereg/man/violinplot.Rd sparsereg/man/plot.sparsereg.Rd sparsereg/man/difference.Rd

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