{ "_id": "6a10450dacfb0bcc41c9e645", "Package": "bootComb", "Type": "Package", "Title": "Combine Parameter Estimates via Parametric Bootstrap", "Version": "1.1.2", "Authors@R": "person(\"Marc\", \"Henrion\", email = \"mhenrion@mlw.mw\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-1242-839X\"))", "Description": "Propagate uncertainty from several estimates when\ncombining these estimates via a function. This is done by using\nthe parametric bootstrap to simulate values from the\ndistribution of each estimate to build up an empirical\ndistribution of the combined parameter. Finally either the\npercentile method is used or the highest density interval is\nchosen to derive a confidence interval for the combined\nparameter with the desired coverage. Gaussian copulas are used\nfor when parameters are assumed to be dependent / correlated.\nReferences: Davison and Hinkley (1997,ISBN:0-521-57471-4) for\nthe parametric bootstrap and percentile method, Gelman et al.\n(2014,ISBN:978-1-4398-4095-5) for the highest density interval,\nStockdale et al. (2020) for an\nexample of combining conditional prevalences.", "License": "GPL-3", "Encoding": "UTF-8", "RoxygenNote": "7.1.1", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-11 06:11:42 UTC", "User": "root" }, "Author": "Marc Henrion [aut, cre]\n()", "Maintainer": "Marc Henrion ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2022-01-30 18:40:02 UTC", "RemoteUrl": "https://github.com/cran/bootComb", "RemoteRef": "HEAD", "RemoteSha": "3cc937aa34a61e1b190e5909baed673927b9bd6d", "MD5sum": "12b992a20aff1f77be738f397af87bf4", "_user": "cran", "_type": "src", "_file": "bootComb_1.1.2.tar.gz", "_fileid": "74e0554de0c431d4e4577f2eb4a46c03c47b401fe2627984be6b9712ba58d401", "_filesize": 174050, "_sha256": "74e0554de0c431d4e4577f2eb4a46c03c47b401fe2627984be6b9712ba58d401", "_created": "2026-05-11T06:11:42.000Z", "_published": "2026-05-22T11:59:09.533Z", "_distro": "noble", "_jobs": [ { "job": 77375003365, "time": 99, "config": "linux-devel-x86_64", "r": "4.7.0", 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interval.", "topics": [ "getExpFromCI" ] }, { "page": "getGammaFromCI", "title": "Find the best-fit gamma distribution for a given confidence interval.", "topics": [ "getGammaFromCI" ] }, { "page": "getNegBinFromCI", "title": "Find the best-fit negative binomial distribution for a given confidence interval.", "topics": [ "getNegBinFromCI" ] }, { "page": "getNormFromCI", "title": "Find the best-fit normal / Gaussian distribution for a given confidence interval.", "topics": [ "getNormFromCI" ] }, { "page": "getPoisFromCI", "title": "Find the best-fit Poisson distribution for a given confidence interval.", "topics": [ "getPoisFromCI" ] }, { "page": "identifyBetaPars", "title": "Determine the parameters of the best-fit beta distribution for a given confidence interval for a probability parameter.", "topics": [ "identifyBetaPars" ] }, { "page": "identifyExpPars", "title": "Determine the parameters of the best-fit exponential distribution for a given confidence interval.", "topics": [ "identifyExpPars" ] }, { "page": "identifyGammaPars", "title": "Determine the parameters of the best-fit gamma distribution for a given confidence interval.", "topics": [ "identifyGammaPars" ] }, { "page": "identifyNegBinPars", "title": "Determine the parameters of the best-fit negative binomial distribution for a given confidence interval.", "topics": [ "identifyNegBinPars" ] }, { "page": "identifyNormPars", "title": "Determine the parameters of the best-fit normal / Gaussian distribution for a given confidence interval.", "topics": [ "identifyNormPars" ] }, { "page": "identifyPoisPars", "title": "Determine the parameters of the best-fit Poisson distribution for a given confidence interval.", "topics": [ "identifyPoisPars" ] }, { "page": "simScenPrevSensSpec", "title": "Simulation scenario for adjusting a prevalence for sensitivity and specificity.", "topics": [ "simScenPrevSensSpec" ] }, { "page": "simScenProductTwoPrevs", "title": "Simulation scenario for the product of two prevlaence estimates.", "topics": [ "simScenProductTwoPrevs" ] }, { "page": "ssBetaPars", "title": "Compute the sum of squares between the theoretical and observed quantiles of a beta distribution.", "topics": [ "ssBetaPars" ] }, { "page": "ssExpPars", "title": "Compute the sum of squares between the theoretical and observed quantiles of an exponential distribution.", "topics": [ "ssExpPars" ] }, { "page": "ssGammaPars", "title": "Compute the sum of squares between the theoretical and observed quantiles of a gamma distribution.", "topics": [ "ssGammaPars" ] }, { "page": "ssNegBinPars", "title": "Compute the sum of squares between the theoretical and observed quantiles of a negative binomial distribution.", "topics": [ "ssNegBinPars" ] }, { "page": "ssNormPars", "title": "Compute the sum of squares between the theoretical and observed quantiles of a normal / Gaussian distribution.", "topics": [ "ssNormPars" ] }, { "page": "ssPoisPars", "title": "Compute the sum of squares between the theoretical and observed quantiles of a Poisson distribution.", "topics": [ "ssPoisPars" ] } ], "_readme": "https://github.com/cran/bootComb/raw/HEAD/README.md", "_rundeps": [ "MASS" ], "_score": 2.0969100130080562, "_indexed": true, "_nocasepkg": "bootcomb", "_universes": [ "cran" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.1.2", "date": "2026-05-11T06:13:37.000Z", "distro": "noble", "commit": "3cc937aa34a61e1b190e5909baed673927b9bd6d", "fileid": "77c0e433be8a97a8c64a8efef4cf39875d7554ec4dec16230b11c317e771328b", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/25653408747" }, { "r": "4.6.0", "os": "linux", "version": "1.1.2", "date": "2026-05-11T06:13:35.000Z", "distro": "noble", "commit": "3cc937aa34a61e1b190e5909baed673927b9bd6d", "fileid": "e01a6c8c2c66263c2e672f2ec1a79bbd8284e543ba6b23fe112ab7506f005978", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/25653408747" 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