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  "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) <doi:10.1007/s11222-012-9344-6>), regularised\nZV-CV (South et al., 2018 <arXiv:1811.05073>), control\nfunctionals (CF, Oates et al. (2017) <doi:10.1111/rssb.12185>)\nand semi-exact control functionals (SECF, South et al., 2020\n<arXiv:2002.00033>). 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)",
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