| BayesNnet | Bayesian Feed Forward Neural Networks |
| BsplineBasis | Spline Basis Expansions |
| CoefficientGroup | Shrinking Regression Coefficients |
| ConditionalZellnerPrior | Create a spike and slab prior for use with lm.spike. |
| GetPredictorMatrix | GetPredictorMatrix |
| HiddenLayer | Bayesian Feed Forward Neural Networks |
| IndependentSpikeSlabPrior | A spike and slab prior assuming a priori independence. |
| IsplineBasis | Spline Basis Expansions |
| knots | Spline Basis Expansions |
| knots.SplineBasis | Spline Basis Expansions |
| lm.spike | Spike and slab regression |
| logit.spike | Spike and slab logistic regression |
| LogitPrior | Zellner Prior for Logistic Regression |
| LogitZellnerPrior | Zellner Prior for Logistic Regression |
| mlm.spike | Spike and slab multinomial logistic regression |
| model.matrix.glm.spike | Construct Design Matrices |
| MsplineBasis | Spline Basis Expansions |
| MultinomialLogitSpikeSlabPrior | Create a spike and slab prior for use with mlm.spike. |
| NestedRegression | Nested Regression |
| OdaOptions | Spike and slab regression |
| PartialDependencePlot | Plot a Bayesian Neural Network |
| plot.BayesNnet | Plot a Bayesian Neural Network |
| plot.lm.spike | Plot the results of a spike and slab regression. |
| plot.logit.spike | Plot a 'logit.spike' object |
| plot.poisson.spike | Plot a 'poisson.spike' object |
| plot.probit.spike | Plot a 'logit.spike' object |
| plot.qreg.spike | Plot the results of a spike and slab regression. |
| PlotBayesNnetPredictions | Plot a Bayesian Neural Network |
| PlotBayesNnetResiduals | Plot a Bayesian Neural Network |
| PlotLmSpikeCoefficients | Plot Coefficients. |
| PlotLmSpikeFit | Predicted vs actual plot for lm.spike. |
| PlotLmSpikeResiduals | Residual plot for lm.spike |
| PlotLogitSpikeFitSummary | Plot Logit or Probit Fit Summary |
| PlotLogitSpikeResiduals | Residual plot for 'logit.spike' objects. |
| PlotMarginalInclusionProbabilities | Plot marginal inclusion probabilities. |
| PlotModelSize | Plot a distribution of model size |
| PlotNetworkStructure | Plot a Bayesian Neural Network |
| PlotProbitSpikeFitSummary | Plot Logit or Probit Fit Summary |
| PlotProbitSpikeResiduals | Residual plot for 'logit.spike' objects. |
| poisson.spike | Spike and slab Poisson regression |
| PoissonZellnerPrior | Zellner Prior for Poisson Regression |
| predict.BayesNnet | Predictions using spike-and-slab regression. |
| predict.lm.spike | Predictions using spike-and-slab regression. |
| predict.logit.spike | Predictions using spike-and-slab regression. |
| predict.poisson.spike | Predictions using spike-and-slab regression. |
| predict.probit.spike | Predictions using spike-and-slab regression. |
| predict.qreg.spike | Predictions using spike-and-slab regression. |
| print.summary.lm.spike | Print method for spikeslab objects. |
| print.summary.logit.spike | Print method for spikeslab objects. |
| probit.spike | Spike and slab probit regression |
| qreg.spike | Quantile Regression |
| residuals.lm.spike | Extract lm.spike Residuals |
| ShrinkageRegression | Shrinking Regression Coefficients |
| spikeslab | Spike and slab regression |
| SpikeSlabGlmPrior | Zellner Prior for Glm's. |
| SpikeSlabGlmPriorDirect | Zellner Prior for Glm's. |
| SpikeSlabPrior | Create a spike and slab prior for use with lm.spike. |
| SpikeSlabPriorBase | Base class for spike and slab priors |
| SpikeSlabPriorDirect | Create a spike and slab prior for use with lm.spike. |
| SsvsOptions | Spike and slab regression |
| StudentIndependentSpikeSlabPrior | Spike and Slab Prior for Regressions with Student T Errors |
| StudentSpikeSlabPrior | Spike and Slab Prior for Student-T Regression |
| SuggestBurn | Suggest Burn-in |
| SummarizeSpikeSlabCoefficients | Numerical summaries of coefficients from a spike and slab regression. |
| summary.lm.spike | Numerical summaries of the results from a spike and slab regression. |
| summary.logit.spike | Numerical summaries of the results from a spike and slab logistic regression. |
| summary.probit.spike | Numerical summaries of the results from a spike and slab logistic regression. |