Variable Selection Using Shrinkage Priors


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Documentation for package ‘VsusP’ version 1.1.0

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VsusP-package Variable selection from Bayesian shrinkage priors
ChainDiagnostics Calculate diagnostics with explicit chain identities
coef.vsusp_fit Posterior coefficient means
DSS Sparse prediction with the DSS adaptive-lasso approximation
FitGaussian Fit native Gaussian shrinkage regression
FitHorseshoe Fit native Gaussian shrinkage regression
FitRidge Fit native Gaussian shrinkage regression
FitSpikeSlab Fit native Gaussian shrinkage regression
OptimalHbi Plot the sequential two-means tuning path
plot.vsusp_fit Plot a native Gaussian fit
plot.vsusp_tuning Plot a tuning path
PlotAutocorrelation Plot within-chain sample autocorrelations
PlotCoefficients Plot posterior coefficient summaries
PlotInclusion Plot exact spike inclusion frequencies
PlotTrace Plot chains without pooling their iterations
PosteriorDraws Pool retained coefficient draws for selection
predict.vsusp_fit Predict from native Gaussian posterior draws
predict.vsusp_sparse Predict using sparse coefficient summaries
S2MSensitivity Summarize sensitivity to sequential two-means thresholds
S2MVarSelection Select coefficients by posterior median absolute magnitude
S2MVarSelectionV1 Select coefficients from an S2M summary
SAVS Select variables with the signal adaptive variable selector
Sequential2Means Fit Gaussian shrinkage regression and apply sequential two-means
Sequential2MeansBeta Apply sequential two-means to posterior coefficient draws
SpikeSlabSelection Summarize exact spike-and-slab posterior draws
summary.vsusp_fit Summarize native Gaussian posterior draws
summary.vsusp_selection Summarize basic two-means selection
summary.vsusp_sparse Summarize sparse coefficient results
summary.vsusp_spike Summarize exact spike-and-slab inclusion draws
summary.vsusp_tuning Summarize an S2M tuning path
TwoMeansBeta Select variables by the paper's two-means rule
VsusP Variable selection from Bayesian shrinkage priors