{ "_id": "6a1fcf3ab401979e73433a9a", "Package": "ashr", "Encoding": "UTF-8", "Type": "Package", "Maintainer": "Peter Carbonetto ", "Authors@R": "c(person(\"Matthew\",\"Stephens\",role=\"aut\",\nemail=\"mstephens@uchicago.edu\"),\nperson(\"Peter\",\"Carbonetto\",role=c(\"aut\",\"cre\"),\nemail=\"pcarbo@uchicago.edu\"),\nperson(\"Chaoxing\",\"Dai\",role=\"ctb\"),\nperson(\"David\",\"Gerard\",role=\"aut\"),\nperson(\"Mengyin\",\"Lu\",role=\"aut\"),\nperson(\"Lei\",\"Sun\",role=\"aut\"),\nperson(\"Jason\",\"Willwerscheid\",role=\"aut\"),\nperson(\"Nan\",\"Xiao\",role=\"aut\"),\nperson(\"Mazon\",\"Zeng\",role=\"ctb\"))", "Version": "2.2-69", "Date": "2026-05-04", "Title": "Methods for Adaptive Shrinkage, using Empirical Bayes", "Description": "The R package 'ashr' implements an Empirical Bayes\napproach for large-scale hypothesis testing and false discovery\nrate (FDR) estimation based on the methods proposed in M.\nStephens, 2016, \"False discovery rates: a new deal\",\n. These methods can be\napplied whenever two sets of summary statistics---estimated\neffects and standard errors---are available, just as 'qvalue'\ncan be applied to previously computed p-values. Two main\ninterfaces are provided: ash(), which is more user-friendly;\nand ash.workhorse(), which has more options and is geared\ntoward advanced users. The ash() and ash.workhorse() also\nprovides a flexible modeling interface that can accommodate a\nvariety of likelihoods (e.g., normal, Poisson) and mixture\npriors (e.g., uniform, normal).", "License": "GPL (>=3)", "NeedsCompilation": "yes", "URL": "https://github.com/stephens999/ashr", "BugReports": "https://github.com/stephens999/ashr/issues", "VignetteBuilder": "knitr", "RoxygenNote": "7.3.1", "Repository": "https://stephens999.r-universe.dev", "Date/Publication": "2026-05-04 19:15:06 UTC", "RemoteUrl": "https://github.com/stephens999/ashr", "RemoteRef": "HEAD", "RemoteSha": "d3c2b4e43ed3989e0b887000a203ddcb90f3f4be", "Packaged": { "Date": "2026-06-03 06:46:43 UTC", "User": "root" }, "Author": "Matthew Stephens [aut],\nPeter Carbonetto [aut, cre],\nChaoxing Dai [ctb],\nDavid Gerard [aut],\nMengyin Lu [aut],\nLei Sun [aut],\nJason Willwerscheid [aut],\nNan Xiao [aut],\nMazon Zeng [ctb]", "MD5sum": "c26eb025fd45bc982413f3272f8be028", "_user": "stephens999", "_type": "src", "_file": "ashr_2.2-69.tar.gz", "_fileid": "6bb6c80ae1088e9d3982e41eca422426d4116893679b3c84b053e4822abbd322", "_filesize": 889619, "_sha256": "6bb6c80ae1088e9d3982e41eca422426d4116893679b3c84b053e4822abbd322", "_created": "2026-06-03T06:46:43.000Z", "_published": "2026-06-03T06:52:42.192Z", "_distro": "noble", "_jobs": [ { "job": 79237557796, "time": 164, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "7377613958" }, { "job": 79237557793, "time": 222, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7377629311" }, { "job": 79237557823, "time": 170, "config": "linux-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7377615719" }, { "job": 79237557799, "time": 188, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7377619736" }, { "job": 79237557809, "time": 147, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7377608689" }, { "job": 79237557812, "time": 253, "config": "macos-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "7377638001" }, { "job": 79237557805, "time": 162, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7377612279" }, { "job": 79237557808, "time": 316, "config": "macos-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7377655683" }, { "job": 79237098567, "time": 197, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7377568212" }, { "job": 79237557822, "time": 118, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7377600554" }, { "job": 79237557800, "time": 227, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7377630640" }, { "job": 79237557833, "time": 175, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7377615906" }, { "job": 79237557811, "time": 189, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7377620168" } ], "_buildurl": "https://github.com/r-universe/stephens999/actions/runs/26868320647", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/stephens999/ashr", "_commit": { "id": "d3c2b4e43ed3989e0b887000a203ddcb90f3f4be", "author": "Peter Carbonetto ", "committer": "Peter Carbonetto ", "message": "Bumped up version.\n", "time": 1777922106 }, "_maintainer": { "name": "Peter Carbonetto", "email": "pcarbo@uchicago.edu", "login": "pcarbo", "description": "Research Assistant Prof, Human Genetics, U. Chicago; previously, Staff Scientist at Ancestry, postdoc & HFSP fellow at U. 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"page": "ash", "title": "Adaptive Shrinkage", "topics": [ "ash", "ash.workhorse" ] }, { "page": "ash_pois", "title": "Performs adaptive shrinkage on Poisson data", "topics": [ "ash_pois" ] }, { "page": "ashci", "title": "Credible Interval Computation for the ash object", "topics": [ "ashci" ] }, { "page": "ashr", "title": "ashr", "topics": [ "ashr-package", "ashr" ] }, { "page": "calc_loglik", "title": "Compute loglikelihood for data from ash fit", "topics": [ "calc_loglik" ] }, { "page": "calc_logLR", "title": "Compute loglikelihood ratio for data from ash fit", "topics": [ "calc_logLR" ] }, { "page": "calc_mixmean", "title": "Generic function of calculating the overall mean of the mixture", "topics": [ "calc_mixmean" ] }, { "page": "calc_mixsd", "title": "Generic function of calculating the overall standard deviation of the mixture", "topics": [ "calc_mixsd" ] }, { "page": "calc_null_loglik", "title": "Compute loglikelihood for data under null that all beta are 0", "topics": [ "calc_null_loglik" ] }, { "page": "calc_null_vloglik", "title": "Compute vector of loglikelihood for data under null that all beta are 0", "topics": [ "calc_null_vloglik" ] }, { "page": "calc_vloglik", "title": "Compute vector of loglikelihood for data from ash fit", "topics": [ "calc_vloglik" ] }, { "page": "calc_vlogLR", "title": "Compute vector of loglikelihood ratio for data from ash fit", "topics": [ "calc_vlogLR" ] }, { "page": "cdf_conv", "title": "cdf_conv", "topics": [ "cdf_conv" ] }, { "page": "cdf_post", "title": "cdf_post", "topics": [ "cdf_post" ] }, { "page": "cdf.ash", "title": "cdf method for ash object", "topics": [ "cdf.ash" ] }, { "page": "comp_cdf", "title": "Generic function of computing the cdf for each component", "topics": [ "comp_cdf" ] }, { "page": "comp_cdf_conv", "title": "comp_cdf_conv", "topics": [ "comp_cdf_conv" ] }, { "page": "comp_cdf_conv.normalmix", "title": "comp_cdf_conv.normalmix", "topics": [ "comp_cdf_conv.normalmix" ] }, { "page": "comp_cdf_conv.unimix", "title": "cdf of convolution of each component of a unif mixture", "topics": [ "comp_cdf_conv.unimix" ] }, { "page": "comp_cdf_post", "title": "comp_cdf_post", "topics": [ "comp_cdf_post" ] }, { "page": "comp_dens", "title": "Generic function of calculating the component densities of the mixture", "topics": [ "comp_dens" ] }, { "page": "comp_dens_conv", "title": "comp_dens_conv", "topics": [ "comp_dens_conv" ] }, { "page": "comp_dens_conv.normalmix", "title": "comp_dens_conv.normalmix", "topics": [ "comp_dens_conv.normalmix" ] }, { "page": "comp_dens_conv.unimix", "title": "density of convolution of each component of a unif mixture", "topics": [ "comp_dens_conv.unimix" ] }, { "page": "comp_mean", "title": "Generic function of calculating the first moment of components of the mixture", "topics": [ "comp_mean" ] }, { "page": "comp_mean.normalmix", "title": "comp_mean.normalmix", "topics": [ "comp_mean.normalmix" ] }, { "page": "comp_mean.tnormalmix", "title": "comp_mean.tnormalmix", "topics": [ "comp_mean.tnormalmix" ] }, { "page": "comp_mean2", "title": "Generic function of calculating the second moment of components of the mixture", "topics": [ "comp_mean2" ] }, { "page": "comp_postmean", "title": "comp_postmean", "topics": [ "comp_postmean" ] }, { "page": "comp_postmean2", "title": "comp_postmean2", "topics": [ "comp_postmean2" ] }, { "page": "comp_postprob", "title": "comp_postprob", "topics": [ "comp_postprob" ] }, { "page": "comp_postsd", "title": "comp_postsd", "topics": [ "comp_postsd" ] }, { "page": "comp_sd", "title": "Generic function to extract the standard deviations of components of the mixture", "topics": [ "comp_sd" ] }, { "page": "comp_sd.normalmix", "title": "comp_sd.normalmix", "topics": [ "comp_sd.normalmix" ] }, { "page": "comp_sd.tnormalmix", "title": "comp_sd.normalmix", "topics": [ "comp_sd.tnormalmix" ] }, { "page": "compute_lfsr", "title": "Function to compute the local false sign rate", "topics": [ "compute_lfsr" ] }, { "page": "cxxMixSquarem", "title": "Brief description of function.", "topics": [ "cxxMixSquarem" ] }, { "page": "dens", "title": "Find density at y, a generic function", "topics": [ "dens" ] }, { "page": "dens_conv", "title": "dens_conv", "topics": [ "dens_conv" ] }, { "page": "dlogf", "title": "The log-F distribution", "topics": [ "dlogf" ] }, { "page": "estimate_mixprop", "title": "Estimate mixture proportions of a mixture g given noisy (error-prone) data from that mixture.", "topics": [ "estimate_mixprop" ] }, { "page": "gen_etruncFUN", "title": "gen_etruncFUN", "topics": [ "gen_etruncFUN" ] }, { "page": "get_density", "title": "Density method for ash object", "topics": [ "get_density" ] }, { "page": "get_lfdr", "title": "Return lfsr from an ash object", "topics": [ "get_fitted_g", "get_lfdr", "get_lfsr", "get_loglik", "get_logLR", "get_np", "get_pi0", "get_pm", "get_pp", "get_psd", "get_qvalue", "get_svalue" ] }, { "page": "get_post_sample", "title": "Sample from posterior", "topics": [ "get_post_sample" ] }, { "page": "igmix", "title": "Constructor for igmix class", "topics": [ "igmix" ] }, { "page": "lik_binom", "title": "Likelihood object for Binomial error distribution", "topics": [ "lik_binom" ] }, { "page": "lik_logF", "title": "Likelihood object for logF error distribution", "topics": [ "lik_logF" ] }, { "page": "lik_normal", "title": "Likelihood object for normal error distribution", "topics": [ "lik_normal" ] }, { "page": "lik_normalmix", "title": "Likelihood object for normal mixture error distribution", "topics": [ "lik_normalmix" ] }, { "page": "lik_pois", "title": "Likelihood object for Poisson error distribution", "topics": [ "lik_pois" ] }, { "page": "lik_t", "title": "Likelihood object for t error distribution", "topics": [ "lik_t" ] }, { "page": "log_comp_dens_conv", "title": "log_comp_dens_conv", "topics": [ "log_comp_dens_conv" ] }, { "page": "log_comp_dens_conv.normalmix", "title": "log_comp_dens_conv.normalmix", "topics": [ "log_comp_dens_conv.normalmix" ] }, { "page": "log_comp_dens_conv.unimix", "title": "log density of convolution of each component of a unif mixture", "topics": [ "log_comp_dens_conv.unimix" ] }, { "page": "loglik_conv", "title": "loglik_conv", "topics": [ "loglik_conv" ] }, { "page": "loglik_conv.default", "title": "loglik_conv.default", "topics": [ "loglik_conv.default" ] }, { "page": "mixcdf", "title": "mixcdf", "topics": [ "mixcdf" ] }, { "page": "mixcdf.default", "title": "mixcdf.default", "topics": [ "mixcdf.default" ] }, { "page": "mixEM", "title": "Estimate mixture proportions of a mixture model by EM algorithm", "topics": [ "mixEM" ] }, { "page": "mixIP", "title": "Estimate mixture proportions of a mixture model by Interior Point method", "topics": [ "mixIP" ] }, { "page": "mixmean2", "title": "Generic function of calculating the overall second moment of the mixture", "topics": [ "mixmean2" ] }, { "page": "mixprop", "title": "Generic function of extracting the mixture proportions", "topics": [ "mixprop" ] }, { "page": "mixSQP", "title": "Estimate mixture proportions of a mixture model using mix-SQP algorithm.", "topics": [ "mixSQP" ] }, { "page": "mixVBEM", "title": "Estimate posterior distribution on mixture proportions of a mixture model by a Variational Bayes EM algorithm", "topics": [ "mixVBEM" ] }, { "page": "my_e2truncbeta", "title": "second moment of truncated Beta distribution", "topics": [ "my_e2truncbeta" ] }, { "page": "my_e2truncgamma", "title": "second moment of truncated gamma distribution", "topics": [ "my_e2truncgamma" ] }, { "page": "my_e2truncnorm", "title": "Expected Squared Value of Truncated Normal", "topics": [ "my_e2truncnorm" ] }, { "page": "my_e2trunct", "title": "my_e2trunct", "topics": [ "my_e2trunct" ] }, { "page": "my_etruncbeta", "title": "mean of truncated Beta distribution", "topics": [ "my_etruncbeta" ] }, { "page": "my_etruncgamma", "title": "mean of truncated gamma distribution", "topics": [ "my_etruncgamma" ] }, { "page": "my_etrunclogf", "title": "my_etrunclogf", "topics": [ "my_etrunclogf" ] }, { "page": "my_etruncnorm", "title": "Expected Value of Truncated Normal", "topics": [ "my_etruncnorm" ] }, { "page": "my_etrunct", "title": "my_etrunct", "topics": [ "my_etrunct" ] }, { "page": "my_vtruncnorm", "title": "Variance of Truncated Normal", "topics": [ "my_vtruncnorm" ] }, { "page": "ncomp", "title": "ncomp", "topics": [ "ncomp" ] }, { "page": "ncomp.default", "title": "ncomp.default", "topics": [ "ncomp.default" ] }, { "page": "normalmix", "title": "Constructor for normalmix class", "topics": [ "normalmix" ] }, { "page": "pcdf_post", "title": "pcdf_post", "topics": [ "pcdf_post" ] }, { "page": "plogf", "title": "The log-F distribution", "topics": [ "plogf" ] }, { "page": "plot_diagnostic", "title": "Diagnostic plots for ash object", "topics": [ "plot_diagnostic" ] }, { "page": "plot.ash", "title": "Plot method for ash object", "topics": [ "plot.ash" ] }, { "page": "pm_on_zero", "title": 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