Package: bravo 4.0.0

bravo: Bayesian Screening and Variable Selection

Performs Bayesian variable screening and selection for ultra-high dimensional linear regression models.Also contains an user friendly web application to perform multi trait GWAS.

Authors:Dongjin Li [aut, cre], Debarshi Chakraborty [aut], Somak Dutta [aut], Vivekananda Roy [ctb]

bravo_4.0.0.tar.gz
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bravo_4.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
bravo/json (API)

# Install 'bravo' in R:

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

Bug tracker:https://github.com/dongjli/bravo/issues

Uses libs:

c++– GNU Standard C++ Library v3

On CRAN:

cpp

3.78 score 2 stars 4 scripts 473 downloads 10 exports 46 dependencies

Last updated from:a3e57ff7a2. Checks:13 WARNING, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	WARNING	140
linux-devel-x86_64	WARNING	147
source / vignettes	OK	153
linux-release-arm64	WARNING	138
linux-release-x86_64	WARNING	168
macos-release-arm64	WARNING	227
macos-release-x86_64	WARNING	307
macos-oldrel-arm64	WARNING	217
macos-oldrel-x86_64	WARNING	361
windows-devel-arm64	WARNING	363
windows-devel-x86_64	WARNING	149
windows-release-arm64	WARNING	368
windows-release-x86_64	WARNING	155
windows-oldrel-x86_64	WARNING	126
wasm-release	OK	100

Exports:bits calc.runtime dense_to_sparse_converter dense2sparse mip.sven parameter_selection predict.sven sven svenetics svenetics_pipeline

Dependencies:base64enc bslib cachem cli codetools commonmark digest doParallel dplyr fastmap fontawesome foreach fs generics glue htmltools httpuv iterators jquerylib jsonlite later lattice lifecycle magrittr Matrix memoise memuse mime otel pillar pkgconfig promises R6 rappdirs Rcpp rlang sass shiny shinyjs sourcetools tibble tidyselect utf8 vctrs withr xtable

Citation

Development and contributors

Readme and manuals

Help Manual

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