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sparsereg (version 1.2)

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. 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.

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Version

Install

install.packages('sparsereg')

Monthly Downloads

132

Version

1.2

License

GPL (>= 2)

Maintainer

Last Published

March 10th, 2016

Functions in sparsereg (1.2)

violinplot

Function for plotting posterior distribution of effects of interest.
grid-internal

Internal Sparsereg Functions
difference

Plotting difference in posterior estimates from a sparse regression.
sparsereg

Sparse regression for experimental and observational data.
print.sparsereg

A summary of the estimated posterior mode of each parameter.
summary.sparsereg

Summaries for a sparse regression.
sparsereg-package

Sparse regression for experimental and observational data.
plot.sparsereg

Plotting output from a sparse regression.