amandaforde. See also theR-universe documentation.Package: winnerscurse 0.1.1
Amanda Forde
winnerscurse: Winner's Curse Adjustment Methods for Summary Statistics from Genome-Wide Association Studies
Designed to provide users with easy access to published methods which aim to correct for Winner's Curse using only summary statistics from genome-wide association studies. With merely estimates of effect size and associated standard error for each genetic variant, users are able to implement these methods to obtain more accurate estimates of the true effect sizes. These methods can be applied to data from both quantitative and binary traits.
Authors:
winnerscurse_0.1.1.tar.gz
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winnerscurse_0.1.1.tgz(r-4.4-any)winnerscurse_0.1.1.tgz(r-4.3-any)
winnerscurse_0.1.1.tar.gz(r-4.5-noble)winnerscurse_0.1.1.tar.gz(r-4.4-noble)
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winnerscurse.pdf |winnerscurse.html✨
winnerscurse/json (API)
| # Install 'winnerscurse' in R: |
| install.packages('winnerscurse', repos = c('https://mrcieu.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/amandaforde/winnerscurse/issues
Pkgdown site:https://amandaforde.github.io
Last updated 11 months agofrom:2ed00bb119. Checks:1 OK, 4 NOTE, 2 ERROR. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Jan 01 2025 |
| R-4.5-win | NOTE | Jan 01 2025 |
| R-4.5-linux | NOTE | Jan 01 2025 |
| R-4.4-win | NOTE | Jan 01 2025 |
| R-4.4-mac | ERROR | Jan 01 2025 |
| R-4.3-win | NOTE | Jan 01 2025 |
| R-4.3-mac | ERROR | Jan 01 2025 |
Exports:BR_sscl_intervalconditional_likelihoodcondlike_repempirical_bayesFDR_IQTMSE_minimizerse_adjustsim_statsUMVCUE
Dependencies:clidplyrfansigenericsgluelatticelifecyclemagrittrMatrixmgcvnlmepillarpkgconfigR6rlangscamtibbletidyselectutf8vctrswithr
Methods for use with discovery and replication GWASs
Rendered fromdiscovery_replication.Rmdusingknitr::rmarkdownon Jan 01 2025.Last update: 2023-12-06
Started: 2021-03-18
Methods for use with discovery GWAS
Rendered fromwinners_curse_methods.Rmdusingknitr::rmarkdownon Jan 01 2025.Last update: 2023-12-06
Started: 2021-02-03
Standard errors and confidence intervals of adjusted estimates
Rendered fromstandard_errors_confidence_intervals.Rmdusingknitr::rmarkdownon Jan 01 2025.Last update: 2023-12-06
Started: 2021-03-09
