{ "_id": "6a3265823efcd9bda43854c8", "Package": "ZVCV", "Type": "Package", "Title": "Zero-Variance Control Variates", "Version": "2.1.3", "Date": "2025-10-18", "Authors@R": "c(person(\"Leah F.\",\"South\",role=c(\"aut\",\"cre\"),email=\"leah.south@hdr.qut.edu.au\",comment = c(ORCID = \"0000-0002-5646-2963\")))", "Description": "Stein control variates can be used to improve Monte Carlo\nestimates of expectations when the derivatives of the log\ntarget are available. This package implements a variety of such\nmethods, including zero-variance control variates (ZV-CV, Mira\net al. (2013) ), regularised\nZV-CV (South et al., 2018 ), control\nfunctionals (CF, Oates et al. (2017) )\nand semi-exact control functionals (SECF, South et al., 2020\n). ZV-CV is a parametric approach that is\nexact for (low order) polynomial integrands with Gaussian\ntargets. CF is a non-parametric alternative that offers better\nthan the standard Monte Carlo convergence rates. SECF has both\na parametric and a non-parametric component and it offers the\nadvantages of both for an additional computational cost.\nFunctions for applying ZV-CV and CF to two estimators for the\nnormalising constant of the posterior distribution in Bayesian\nstatistics are also supplied in this package. The basic\nrequirements for using the package are a set of samples,\nderivatives and function evaluations.", "BugReports": "https://github.com/LeahPrice/ZVCV/issues", "License": "GPL (>= 2)", "LazyLoad": "yes", "LazyData": "true", "Encoding": "UTF-8", "RoxygenNote": "7.3.1", "Repository": "https://leahprice.r-universe.dev", "Date/Publication": "2025-10-20 09:59:52 UTC", "RemoteUrl": "https://github.com/leahprice/zvcv", "RemoteRef": "HEAD", "RemoteSha": "d30d6eb9e1c8aafdb9b5f515c444e798d057bc72", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-17 09:04:38 UTC", "User": "root" }, "Author": "Leah F. South [aut, cre] (ORCID:\n)", "Maintainer": "Leah F. 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