Sparse Bayesian Models for Regression, Subgroup Analysis, and Panel Data
Plotting difference in posterior estimates from a sparse regression.
Plotting output from a sparse regression.
A summary of the estimated posterior mode of each parameter.
Internal Sparsereg Functions
Sparse regression for experimental and observational data.
Sparse regression for experimental and observational data.
Summaries for a sparse regression.
Function for plotting posterior distribution of effects of interest.
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.