Package: BoomSpikeSlab 1.2.7

BoomSpikeSlab: MCMC for Spike and Slab Regression

Spike and slab regression with a variety of residual error distributions corresponding to Gaussian, Student T, probit, logit, SVM, and a few others. Spike and slab regression is Bayesian regression with prior distributions containing a point mass at zero. The posterior updates the amount of mass on this point, leading to a posterior distribution that is actually sparse, in the sense that if you sample from it many coefficients are actually zeros. Sampling from this posterior distribution is an elegant way to handle Bayesian variable selection and model averaging. See <doi:10.1504/IJMMNO.2014.059942> for an explanation of the Gaussian case.

Authors:Steven L. Scott [aut, cre]

BoomSpikeSlab_1.2.7.tar.gz
BoomSpikeSlab_1.2.7.zip(r-4.7)BoomSpikeSlab_1.2.7.zip(r-4.6)BoomSpikeSlab_1.2.7.zip(r-4.5)
BoomSpikeSlab_1.2.7.tgz(r-4.6-x86_64)BoomSpikeSlab_1.2.7.tgz(r-4.6-arm64)BoomSpikeSlab_1.2.7.tgz(r-4.5-x86_64)BoomSpikeSlab_1.2.7.tgz(r-4.5-arm64)
BoomSpikeSlab_1.2.7.tar.gz(r-4.7-arm64)BoomSpikeSlab_1.2.7.tar.gz(r-4.7-x86_64)BoomSpikeSlab_1.2.7.tar.gz(r-4.6-arm64)BoomSpikeSlab_1.2.7.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
card.svg |card.png
BoomSpikeSlab/json (API)

# Install 'BoomSpikeSlab' in R:
install.packages('BoomSpikeSlab', repos = c('https://steve-the-bayesian.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

5.43 score 6 stars 9 packages 122 scripts 2.8k downloads 5 mentions 59 exports 2 dependencies

Last updated from:d1e93a1a39. Checks:12 OK, 1 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK160
linux-devel-x86_64OK156
source / vignettesOK176
linux-release-arm64OK156
linux-release-x86_64OK153
macos-release-arm64OK262
macos-release-x86_64OK256
macos-oldrel-arm64OK184
macos-oldrel-x86_64OK406
windows-develOK190
windows-releaseOK203
windows-oldrelOK177
wasm-releaseFAIL114

Exports:BayesNnetBsplineBasisCoefficientGroupConditionalZellnerPriorGetPredictorMatrixHiddenLayerIndependentSpikeSlabPriorIsplineBasisknotslm.spikelogit.spikeLogitZellnerPriormlm.spikemodel.matrix.glm.spikeMsplineBasisMultinomialLogitSpikeSlabPriorNestedRegressionOdaOptionsPartialDependencePlotplot.lm.spikeplot.logit.spikeplot.poisson.spikeplot.qreg.spikePlotBayesNnetPredictionsPlotBayesNnetResidualsPlotLmSpikeCoefficientsPlotLmSpikeFitPlotLmSpikeResidualsPlotLogitSpikeFitSummaryPlotLogitSpikeResidualsPlotMarginalInclusionProbabilitiesPlotModelSizePlotNetworkStructurePlotProbitSpikeResidualspoisson.spikePoissonZellnerPriorpredict.BayesNnetpredict.lm.spikepredict.logit.spikepredict.poisson.spikepredict.probit.spikepredict.qreg.spikeprobit.spikeqreg.spikeresiduals.lm.spikeShrinkageRegressionSpikeSlabGlmPriorSpikeSlabGlmPriorDirectSpikeSlabPriorSpikeSlabPriorBaseSpikeSlabPriorDirectSsvsOptionsStudentIndependentSpikeSlabPriorStudentSpikeSlabPriorSuggestBurnSummarizeSpikeSlabCoefficientssummary.lm.spikesummary.logit.spikesummary.probit.spike

Dependencies:BoomMASS

Readme and manuals

Help Manual

Help pageTopics
A spike and slab prior assuming a priori independence.IndependentSpikeSlabPrior
Spike and Slab Prior for Regressions with Student T ErrorsStudentIndependentSpikeSlabPrior
Spike and slab regressionlm.spike OdaOptions spikeslab SsvsOptions
Spike and slab logistic regressionlogit.spike
Zellner Prior for Logistic RegressionLogitPrior LogitZellnerPrior
Spike and slab multinomial logistic regressionmlm.spike
Create a spike and slab prior for use with mlm.spike.MultinomialLogitSpikeSlabPrior
GetPredictorMatrixGetPredictorMatrix
Construct Design Matricesmodel.matrix.glm.spike
Nested RegressionNestedRegression
Bayesian Feed Forward Neural NetworksBayesNnet HiddenLayer
Plot a Bayesian Neural NetworkPartialDependencePlot
Plot a Bayesian Neural Networkplot.BayesNnet PlotBayesNnetPredictions PlotBayesNnetResiduals PlotNetworkStructure
Plot Coefficients.PlotLmSpikeCoefficients
Plot the results of a spike and slab regression.plot.lm.spike
Predicted vs actual plot for lm.spike.PlotLmSpikeFit
Residual plot for lm.spikePlotLmSpikeResiduals
Plot a 'logit.spike' objectplot.logit.spike plot.probit.spike
Plot Logit or Probit Fit SummaryPlotLogitSpikeFitSummary PlotProbitSpikeFitSummary
Residual plot for 'logit.spike' objects.PlotLogitSpikeResiduals PlotProbitSpikeResiduals
Plot marginal inclusion probabilities.PlotMarginalInclusionProbabilities
Plot a 'poisson.spike' objectplot.poisson.spike
Plot the results of a spike and slab regression.plot.qreg.spike
Plot a distribution of model sizePlotModelSize
Spike and slab Poisson regressionpoisson.spike
Zellner Prior for Poisson RegressionPoissonZellnerPrior
Predictions using spike-and-slab regression.predict.BayesNnet predict.lm.spike predict.logit.spike predict.poisson.spike predict.probit.spike predict.qreg.spike
Print method for spikeslab objects.print.summary.lm.spike print.summary.logit.spike
Spike and slab probit regressionprobit.spike
Quantile Regressionqreg.spike
Extract lm.spike Residualsresiduals.lm.spike
Shrinking Regression CoefficientsCoefficientGroup ShrinkageRegression
Zellner Prior for Glm's.SpikeSlabGlmPrior SpikeSlabGlmPriorDirect
Create a spike and slab prior for use with lm.spike.ConditionalZellnerPrior SpikeSlabPrior SpikeSlabPriorDirect
Base class for spike and slab priorsSpikeSlabPriorBase
Spline Basis ExpansionsBsplineBasis IsplineBasis knots knots.SplineBasis MsplineBasis
Spike and Slab Prior for Student-T RegressionStudentSpikeSlabPrior
Suggest Burn-inSuggestBurn
Numerical summaries of coefficients from a spike and slab regression.SummarizeSpikeSlabCoefficients
Numerical summaries of the results from a spike and slab regression.summary.lm.spike
Numerical summaries of the results from a spike and slab logistic regression.summary.logit.spike summary.probit.spike