Package: SequenceSpikeSlab 1.0.1

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

Contains fast functions to calculate the exact Bayes posterior for the Sparse Normal Sequence Model, implementing the algorithms described in Van Erven and Szabo (2021, <doi:10.1214/20-BA1227>). For general hierarchical priors, sample sizes up to 10,000 are feasible within half an hour on a standard laptop. For beta-binomial spike-and-slab priors, a faster algorithm is provided, which can handle sample sizes of 100,000 in half an hour. In the implementation, special care has been taken to assure numerical stability of the methods even for such large sample sizes.

Authors:Steven de Rooij [aut], Tim van Erven [cre, aut], Botond Szabo [aut]

# Install 'SequenceSpikeSlab' in R:

install.packages('SequenceSpikeSlab', repos = c('https://tverven.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

c++– GNU Standard C++ Library v3

On CRAN:

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

cpp

2.00 score 7 scripts 312 downloads 12 exports 18 dependencies

Last updated from:d6a341f86b. Checks:15 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	112
linux-devel-x86_64	OK	139
source / vignettes	OK	182
linux-release-arm64	OK	94
linux-release-x86_64	OK	124
macos-release-arm64	OK	155
macos-release-x86_64	OK	395
macos-oldrel-arm64	OK	244
macos-oldrel-x86_64	OK	289
windows-devel-arm64	OK	348
windows-devel-x86_64	OK	153
windows-release-arm64	OK	317
windows-release-x86_64	OK	125
windows-oldrel-x86_64	OK	86
wasm-release	OK	92

Exports:fast_spike_slab_beta general_sequence_model SSS_discrete_spike_slab SSS_discretize_Lambda SSS_discretize_Lambda_beta SSS_hierarchical_prior SSS_hierarchical_prior_binomial SSS_log_phi_psi_Cauchy SSS_log_phi_psi_Laplace SSS_make_beta_grid SSS_postmean_Cauchy SSS_postmean_Laplace

Dependencies:adaptMCMC coda codetools foreach glmnet intervals iterators lattice MASS Matrix ramcmc Rcpp RcppArmadillo RcppEigen RcppProgress selectiveInference shape survival

SequenceSpikeSlab-vignette

Rendered fromSequenceSpikeSlab-vignette.Rmdusingknitr::rmarkdown

Last update: 2022-01-23
Started: 2019-12-13

Help page	Topics
Compute marginal posterior estimates for beta-spike-and-slab prior	fast_spike_slab_beta
Compute marginal posterior estimates	general_sequence_model
Compute marginal posterior probabilities (slab probabilities) that data points have non-zero mean for the discretized spike-and-slab prior.	SSS_discrete_spike_slab
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
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_discretize_Lambda_beta
Compute marginal posterior probabilities (slab probabilities) that data points have non-zero mean for the hierarchical prior.	SSS_hierarchical_prior
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_hierarchical_prior_binomial
Calculate log of phi and psi marginal densities for Cauchy(gamma) slab	SSS_log_phi_psi_Cauchy
Calculate log of phi and psi marginal densities for Laplace(lambda) slab	SSS_log_phi_psi_Laplace
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_make_beta_grid
Compute posterior means of data points for the Cauchy(gamma) slab	SSS_postmean_Cauchy
Compute posterior means of data points for the Laplace(lambda) slab	SSS_postmean_Laplace

Package: SequenceSpikeSlab 1.0.1

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

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)