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. Beyond regression analyses, applications include subgroup analysis, particularly for conjoint experiments, and panel data. Functionality for dichotomous and censored outcome (Types I and II tobit) is also included. Future plans involve extending the method to propensity score and instrumental variable methods.