MCMC for Spike and Slab Regression
A spike and slab prior assuming a priori independence.
Spike and Slab Prior for Regressions with Student T Errors
Spike and slab regression
Spike and slab logistic regression
Zellner Prior for Logistic Regression
Spike and slab multinomial logistic regression
Create a spike and slab prior for use with mlm.spike.
Construct Design Matrices
GetPredictorMatrix
Nested Regression
Bayesian Feed Forward Neural Networks
Plot a Bayesian Neural Network
Plot a Bayesian Neural Network
Plot Coefficients.
Predicted vs actual plot for lm.spike.
Plot the results of a spike and slab regression.
Residual plot for lm.spike
Plot Logit or Probit Fit Summary
Plot a logit.spike object
Residual plot for logit.spike objects.
Plot marginal inclusion probabilities.
Plot a distribution of model size
Plot a poisson.spike object
Plot the results of a spike and slab regression.
Spike and slab Poisson regression
Zellner Prior for Poisson Regression
Predictions using spike-and-slab regression.
Print method for spikeslab objects.
Spike and slab probit regression
Quantile Regression
Extract lm.spike Residuals
Shrinking Regression Coefficients
Zellner Prior for Glm's.
Base class for spike and slab priors
Create a spike and slab prior for use with lm.spike.
Spline Basis Expansions
Spike and Slab Prior for Student-T Regression
Suggest Burn-in
Numerical summaries of coefficients from a spike and slab regression.
Numerical summaries of the results from a spike and slab regression.
Numerical summaries of the results from a spike and slab logistic regr...
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.