R: Exact Bayesian Model Selection Methods for the Sparse Normal Sequence Model

# Exact Bayesian Model Selection Methods for the Sparse Normal Sequence Model

## Documentation for package ‘SequenceSpikeSlab’ version 1.0.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS

## Help Pages

fast_spike_slab_beta | Compute marginal posterior estimates for beta-spike-and-slab prior
general_sequence_model | Compute marginal posterior estimates
SSS_discrete_spike_slab | Compute marginal posterior probabilities (slab probabilities) that data points have non-zero mean for the discretized spike-and-slab prior.
SSS_discretize_Lambda | Given a prior Lambda on the alpha-parameter in the spike-and-slab model, make a discretized version of Lambda that is only supported on a grid of approximately m * sqrt(n) discrete values of alpha. This discretized version of Lambda is required as input for 'SSS_discrete_spike_slab'. NB Lambda needs to satisfy a technical condition from the paper that guarantees its density does not vary too rapidly. For Lambda=Beta(kappa,lambda) use 'SSS_discretize_Lambda_beta' instead.
SSS_discretize_Lambda_beta | Given prior Lambda=Beta(kappa,lambda) on the alpha-parameter in the spike-and-slab model, make a discretized version of Lambda that is only supported on a grid of approximately m * sqrt(n) discrete values of alpha. This discretized version of Lambda is required as input for SSS_discrete_spike_slab.
SSS_hierarchical_prior | Compute marginal posterior probabilities (slab probabilities) that data points have non-zero mean for the hierarchical prior.
SSS_hierarchical_prior_binomial | Compute marginal posterior probabilities (slab probabilities) that data points have non-zero mean using the general hierarchical prior algorithm, but specialized to the Beta[kappa,lambda]-binomial prior. This function is equivalent to calling 'SSS_hierarchical_prior' with logprior = lbeta(kappa+(0:n),lambda+n-(0:n)) - lbeta(kappa,lambda) + lchoose(n,0:n), but more convenient when using the Beta[kappa,lambda]-binomial prior and with a minor interior optimization that avoids calculating the choose explicitly.
SSS_log_phi_psi_Cauchy | Calculate log of phi and psi marginal densities for Cauchy(gamma) slab
SSS_log_phi_psi_Laplace | Calculate log of phi and psi marginal densities for Laplace(lambda) slab
SSS_make_beta_grid | Creates a vector of uniformly spaced grid points in the beta parametrization Ensures the number of generated grid points is >= mingridpoints (which does not have to be integer), and that their number is always odd so there is always a grid point at pi/4.
SSS_postmean_Cauchy | Compute posterior means of data points for the Cauchy(gamma) slab
SSS_postmean_Laplace | Compute posterior means of data points for the Laplace(lambda) slab