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  "Title": "Zero-Variance Control Variates",
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  "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) <doi:10.1007/s11222-012-9344-6>), regularised\nZV-CV (South et al., 2023 <doi:10.1214/22-BA1328>), control\nfunctionals (CF, Oates et al. (2017) <doi:10.1111/rssb.12185>)\nand semi-exact control functionals (SECF, South et al., 2022\n<doi:10.1093/biomet/asab036>). ZV-CV is a parametric approach\nthat is exact for (low order) polynomial integrands with\nGaussian targets. CF is a non-parametric alternative that\noffers better than the standard Monte Carlo convergence rates.\nSECF has both a parametric and a non-parametric component and\nit offers the advantages of both for an additional\ncomputational cost. Functions for applying ZV-CV and CF to two\nestimators for the normalising constant of the posterior\ndistribution in Bayesian statistics are also supplied in this\npackage. The basic requirements for using the package are a set\nof samples, derivatives and function evaluations.",
  "BugReports": "https://github.com/LeahPrice/ZVCV/issues",
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