Package: corrcoverage 1.2.0

Anna Hutchinson

corrcoverage: Correcting the Coverage of Credible Sets from Bayesian Genetic Fine Mapping

Using a computationally efficient method, the package can be used to find the corrected coverage estimate of a credible set of putative causal variants from Bayesian genetic fine-mapping. The package can also be used to obtain a corrected credible set if required; that is, the smallest set of variants required such that the corrected coverage estimate of the resultant credible set is within some user defined accuracy of the desired coverage. Maller et al. (2012) <doi:10.1038/ng.2435>, Wakefield (2009) <doi:10.1002/gepi.20359>, Fortune and Wallace (2018) <doi:10.1093/bioinformatics/bty898>.

Authors:Anna Hutchinson [aut, cre], Chris Wallace [aut], Kevin Kunzmann [ctb]

corrcoverage_1.2.0.tar.gz

corrcoverage_1.2.0.tgz(r-4.4-x86_64)corrcoverage_1.2.0.tgz(r-4.4-arm64)corrcoverage_1.2.0.tgz(r-4.3-x86_64)corrcoverage_1.2.0.tgz(r-4.3-arm64)
corrcoverage_1.2.0.tar.gz(r-4.5-noble)corrcoverage_1.2.0.tar.gz(r-4.4-noble)
corrcoverage_1.2.0.tgz(r-4.4-emscripten)corrcoverage_1.2.0.tgz(r-4.3-emscripten)
corrcoverage.pdf |corrcoverage.html✨
corrcoverage/json (API)
NEWS

# Install 'corrcoverage' in R:
install.packages('corrcoverage', repos = c('https://annahutch.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/annahutch/corrcoverage/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

27 exports 6 stars 1.18 score 5 dependencies 16 scripts 181 downloads

Last updated 3 years agofrom:3b610d1450. Checks:ERROR: 1 WARNING: 5. Indexed: yes.

TargetResultDate
Doc / VignettesFAILSep 08 2024
R-4.5-linux-x86_64WARNINGSep 08 2024
R-4.4-mac-x86_64WARNINGSep 08 2024
R-4.4-mac-aarch64WARNINGSep 08 2024
R-4.3-mac-x86_64WARNINGSep 08 2024
R-4.3-mac-aarch64WARNINGSep 08 2024

Exports:%>%approx.bf.pbf_funccor2corrcovcorrcov_bhatcorrcov_CIcorrcov_CI_bhatcorrcov_nvarcorrcov_nvar_bhatcorrected_covcorrected_cscorrected_cs_bhatcredsetcredsetCcredsetmatest_muest_mu_bhatlogsumppfuncppfunc.matpvals_ppVar.data.ccz_simz0_ppzj_ppzj_pp_arma

Dependencies:data.tablemagrittrmatrixStatsRcppRcppArmadillo

Readme and manuals

Help Manual

Help pageTopics
Internal function: Simulate nrep ABFs from joint Z-score vector.zj_abf
Simulate posterior probabilities of causality from joint Z-score vector.zj_pp
Find approx. Bayes factors (ABFs)approx.bf.p
Calculate ABFs from Z scoresbf_func
Correlation matrix of SNPScor2
Corrected coverage estimate using Z-scores and MAFscorrcov
Corrected coverage estimate using estimated effect sizes and their standard errorscorrcov_bhat
Confidence interval for corrected coverage estimate using Z-scores and MAFscorrcov_CI
Confidence interval for corrected coverage estimate using estimated effect sizes and their standard errorscorrcov_CI_bhat
Corrected coverage estimate using Z-scores and MAFs (fixing nvar)corrcov_nvar
Corrected coverage estimate using estimated effect sizes and their standard errors (fixing nvar)corrcov_nvar_bhat
Corrected coverage estimate of the causal variant in the credible setcorrected_cov
Corrected credible set using Z-scores and MAFscorrected_cs
Corrected credible set using estimated effect sizes and their standard errorscorrected_cs_bhat
Credible set of genetic variantscredset
Credible set of variants from matrix of PPscredsetC
Obtain credible sets from a matrix of posterior probabilitiescredsetmat
Estimate the true effect at the causal variant using Z-scores and MAFsest_mu
Estimate the true effect at the causal variant using estimated effect sizes and their standard errorsest_mu_bhat
logsumlogsum
logsum rows of a matrixlogsum_matrix
Find PPs of SNPs from Z-scoresppfunc
Find PPs of SNPs from matrix of Z-scoresppfunc.mat
Proportion of credible sets containing the causal variantprop_cov
Find PPs for SNPs and null model from P-values and MAFspvals_pp
Variance of the estimated effect size for case-control dataVar.data.cc
Simulate marginal Z-scores from joint Z-score vectorz_sim
Find PPs for SNPs and null model from Z-scores and MAFsz0_pp
Simulate posterior probabilities of causality from joint Z-score vectorzj_pp
Obtain pp from a matrix of Zj and ERRzj_pp_arma