R: Bayesian Variable Selection Using Simplified Shotgun Stochastic Search with Screening (S5)

# Bayesian Variable Selection Using Simplified Shotgun Stochastic Search with Screening (S5)

## Documentation for package ‘BayesS5’ version 1.41

- DESCRIPTION file

## Help Pages

Bernoulli_Uniform | Bernoulli-Uniform model prior
hyper_par | Tuning parameter selection for nonlocal priors
ind_fun_g | Zellner's g-prior
ind_fun_NLfP | the log-marginal likelhood function based on the invers moment functional priors and inverse gamma prior (0.01,0.01)
ind_fun_pemom | the log-marginal likelhood function based on peMoM priors and inverse gamma prior (0.01,0.01)
ind_fun_pimom | the log-marginal likelhood function based on piMoM priors
obj_fun_g | the log posterior distribution based on g-priors and inverse gamma prior (0.01,0.01)
obj_fun_pemom | the log posterior distribution based on peMoM priors and inverse gamma prior (0.01,0.01)
obj_fun_pimom | the log posterior distribution based on piMoM priors and inverse gamma prior (0.01,0.01)
result | Posterior inference results from the object of S5
result_est_LS | Posterior inference results from the object of S5
result_est_MAP | Posterior inference results from the object of S5
S5 | Simplified shotgun stochastic search algorithm with screening (S5)
S5_additive | Simplified shotgun stochastic search algorithm with screening (S5) for additive models
S5_parallel | Parallel version of S5
SSS | Shotgun stochastic search algorithm (SSS)
Uniform | Uniform model prior