Bayesian Screening and Variable Selection

Documentation for package ‘bravo’ version 4.0.0

basic.sven.model	Run SVEN with Optimal Parameters
bits	Bayesian Iterated Screening (ultra-high, high or low dimensional).
bwas	BWAS: Bayesian GWAS for a Single Trait
calc.runtime	Estimate SVEN Runtime
clean	Clean SNP Matrix
create_param_mat	Create Parameter Grid
dense2sparse	Convert numeric matrix to sparse matrix
dense_to_sparse_converter	Launch the Sparse Converter Shiny App
FDR_corrected	FDR using correlation threshold
FDR_WS	FDR using window size
FPR_corrected	FPR using correlation threshold
FPR_WS	FPR using window size
jcidx	Jaccard Index
mip.sven	Compute marginal inclusion probabilities from a fitted "sven" object.
parameter_selection	Select Optimal Tuning Parameters
pipeline_single_trait	Run GWAS for a Single Trait
predict.sven	Make predictions from a fitted "sven" object.
run_all_params	Run SVEN Across Parameter Grid
sven	Selection of variables with embedded screening using Bayesian methods (SVEN) in Gaussian linear models (ultra-high, high or low dimensional).
svenetics	Launch the SVENETICS Shiny App
svenetics_pipeline	Full Multi-Trait GWAS Pipeline
TPR_corrected	TPR using correlation threshold
TPR_WS	TPR using window size
tune.sven	Tune SVEN Parameters
tune.sven.all	Tune SVEN for All Hit Sizes
unite.sven	UNITE: Post-process SVEN Model