sparsereg-package: Sparse regression for experimental and observational data.

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

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. Beyond regression analyses, applications include subgroup analysis, particularly for conjoint experiments, and panel data. Future versions will include extensions to limited dependent variables, models with truncated outcomes, and propensity score and instrumental variable analysis.

Details

Package:	sparsereg
Type:	Package
Version:	1.0
Date:	2015-03-20
License:	GPL (>= 2)

Author(s)

Marc Ratkovic and Dustin Tingley Maintainer: Marc Ratkovic ([e-mail])

References

Ratkovic, Marc and Tingley, Dustin. 2015. "Sparse Estimation with Uncertainty: Subgroup Analysis in Large Dimensional Design." Working paper.

See Also

FindIt, glmnet

sparsereg documentation built on May 1, 2019, 7:28 p.m.