{ "_id": "6a1d39f61d7bb097a0a3e90a", "Package": "BayesS5", "Type": "Package", "Title": "Bayesian Variable Selection Using Simplified Shotgun Stochastic\nSearch with Screening (S5)", "Version": "1.41", "Date": "2020-03-20", "Author": "Minsuk Shin and Ruoxuan Tian", "Maintainer": "Minsuk Shin ", "Description": "In p >> n settings, full posterior sampling using existing\nMarkov chain Monte Carlo (MCMC) algorithms is highly\ninefficient and often not feasible from a practical\nperspective. To overcome this problem, we propose a scalable\nstochastic search algorithm that is called the Simplified\nShotgun Stochastic Search (S5) and aimed at rapidly explore\ninteresting regions of model space and finding the maximum a\nposteriori(MAP) model. Also, the S5 provides an approximation\nof posterior probability of each model (including the marginal\ninclusion probabilities). This algorithm is a part of an\narticle titled \"Scalable Bayesian Variable Selection Using\nNonlocal Prior Densities in Ultrahigh-dimensional Settings\"\n(2018) by Minsuk Shin, Anirban Bhattacharya, and Valen E.\nJohnson and \"Nonlocal Functional Priors for Nonparametric\nHypothesis Testing and High-dimensional Model Selection\"\n(2020+) by Minsuk Shin and Anirban Bhattacharya.", "URL": "https://arxiv.org/abs/1507.07106v4", "License": "GPL (>= 2)", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-01 07:46:10 UTC", "User": "root" }, "Repository": "https://minsuk000.r-universe.dev", "Date/Publication": "2020-03-24 06:40:14 UTC", "RemoteUrl": "https://github.com/cran/BayesS5", "RemoteRef": "HEAD", "RemoteSha": "1d78d767f0a8407eaf7fa96f9113ca3336931657", "MD5sum": "71c12d54201f227ce03a1893ef0264f2", "_user": "minsuk000", "_type": "src", "_file": "BayesS5_1.41.tar.gz", "_fileid": "753ef8bc96638487b06ef1f47e391a977fa4ae921c22391f6e10cc722996e830", "_filesize": 166210, "_sha256": "753ef8bc96638487b06ef1f47e391a977fa4ae921c22391f6e10cc722996e830", "_created": "2026-06-01T07:46:10.000Z", "_published": "2026-06-01T07:51:18.765Z", "_distro": "noble", "_jobs": [ { "job": 78808084081, "time": 124, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7326011061" }, { "job": 78808084088, "time": 120, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7326009868" }, { "job": 78808084124, "time": 156, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7326036709" }, { "job": 78808084076, "time": 201, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7326036473" }, { "job": 78807770224, "time": 132, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7325971356" }, { "job": 78808084058, "time": 98, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7326002008" }, { "job": 78808084094, "time": 97, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7326002308" }, { "job": 78808084098, "time": 80, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7325996756" }, { "job": 78808084122, "time": 122, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7326010539" } ], "_buildurl": "https://github.com/r-universe/minsuk000/actions/runs/26741908488", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/cran/BayesS5", "_commit": { "id": "1d78d767f0a8407eaf7fa96f9113ca3336931657", "author": "Minsuk Shin ", "committer": "cran-robot ", "message": "version 1.41\n", "time": 1585032014 }, "_maintainer": { "name": "Minsuk Shin", "email": "minsuk000@gmail.com", "login": "minsuk000", "description": "I am a statistics nerd, Bayesian, and amateur soccer player.\n\nFor further information, please visit my webpage or email me.\n", "uuid": 7375458 }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.4.0", "role": "Depends" }, { "package": "Matrix", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "snowfall", "role": "Imports" }, { "package": "abind", "role": "Imports" }, { "package": "splines2", "role": "Imports" } ], "_owner": "cran", "_selfowned": true, "_usedby": 0, "_updates": [], "_tags": [], "_stars": 2, "_contributors": [ { "user": "minsuk000", "count": 5, "uuid": 7375458 } ], "_userbio": { "uuid": 7375458, "type": "user", "name": "Minsuk Shin", "description": "I am a statistics nerd, Bayesian, and amateur soccer player.\r\n\r\nFor further information, please visit my webpage or email me.\r\n" }, "_downloads": { "count": 230, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/BayesS5" }, "_searchresults": 10, "_rbuild": "4.6.0", "_assets": [ "extra/BayesS5.html", "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "manual.pdf" ], "_realowner": "minsuk000", "_cranurl": false, "_releases": [ { "version": "1.22", "date": "2017-01-17" }, { "version": "1.30", "date": "2017-02-24" }, { "version": "1.31", "date": "2018-10-26" }, { "version": "1.40", "date": "2020-03-23" }, { "version": "1.41", "date": "2020-03-24" } ], "_exports": [ "Bernoulli_Uniform", "hyper_par", "ind_fun_g", "ind_fun_NLfP", "ind_fun_pemom", "ind_fun_pimom", "obj_fun_g", "obj_fun_pemom", "obj_fun_pimom", "result", "result_est_LS", "result_est_MAP", "S5", "S5_additive", "S5_parallel", "SSS", "Uniform" ], "_help": [ { "page": "Bernoulli_Uniform", "title": "Bernoulli-Uniform model prior", "topics": [ "Bernoulli_Uniform" ] }, { "page": "hyper_par", "title": "Tuning parameter selection for nonlocal priors", "topics": [ "hyper_par" ] }, { "page": "ind_fun_g", "title": "Zellner's g-prior", "topics": [ "ind_fun_g" ] }, { "page": "ind_fun_NLfP", "title": "the log-marginal likelhood function based on the invers moment functional priors and inverse gamma prior (0.01,0.01)", "topics": [ "ind_fun_NLfP" ] }, { "page": "ind_fun_pemom", "title": "the log-marginal likelhood function based on peMoM priors and inverse gamma prior (0.01,0.01)", "topics": [ "ind_fun_pemom" ] }, { "page": "ind_fun_pimom", "title": "the log-marginal likelhood function based on piMoM priors", "topics": [ "ind_fun_pimom" ] }, { "page": "obj_fun_g", "title": "the log posterior distribution based on g-priors and inverse gamma prior (0.01,0.01)", "topics": [ "obj_fun_g" ] }, { "page": "obj_fun_pemom", "title": "the log posterior distribution based on peMoM priors and inverse gamma prior (0.01,0.01)", "topics": [ "obj_fun_pemom" ] }, { "page": "obj_fun_pimom", "title": "the log posterior distribution based on piMoM priors and inverse gamma prior (0.01,0.01)", "topics": [ "obj_fun_pimom" ] }, { "page": "result", "title": "Posterior inference results from the object of S5", "topics": [ "result" ] }, { "page": "result_est_LS", "title": "Posterior inference results from the object of S5", "topics": [ "result_est_LS" ] }, { "page": "result_est_MAP", "title": "Posterior inference results from the object of S5", "topics": [ "result_est_MAP" ] }, { "page": "S5", "title": "Simplified shotgun stochastic search algorithm with screening (S5)", "topics": [ "S5" ] }, { "page": "S5_additive", "title": "Simplified shotgun stochastic search algorithm with screening (S5) for additive models", "topics": [ "S5_additive" ] }, { "page": "S5_parallel", "title": "Parallel version of S5", "topics": [ "S5_parallel" ] }, { "page": "SSS", "title": "Shotgun stochastic search algorithm (SSS)", "topics": [ "SSS" ] }, { "page": "Uniform", "title": "Uniform model prior", "topics": [ "Uniform" ] } ], "_rundeps": [ "abind", "lattice", "Matrix", "Rcpp", "RcppArmadillo", "snow", "snowfall", "splines2" ], "_score": 1.3010299956639813, "_indexed": true, "_nocasepkg": "bayess5", "_universes": [ "minsuk000" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.41", "date": "2026-06-01T07:48:03.000Z", "distro": "noble", "commit": 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