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

Documentation for package ‘BayesS5’ version 1.41

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