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
fast_spike_slab_beta    Compute marginal posterior estimates for
                        beta-spike-and-slab prior
general_sequence_model
                        Compute marginal posterior estimates
