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ZVCV  

Zero-Variance Control Variates
View on CRAN: Click here


Download and install ZVCV package within the R console
Install from CRAN:
install.packages("ZVCV")

Install from Github:
library("remotes")
install_github("cran/ZVCV")

Install by package version:
library("remotes")
install_version("ZVCV", "2.1.3")



Attach the package and use:
library("ZVCV")
Maintained by
Leah F. South
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-01-24
Latest Update: 2022-11-02
Description:
Stein control variates can be used to improve Monte Carlo estimates of expectations when the derivatives of the log target are available. This package implements a variety of such methods, including zero-variance control variates (ZV-CV, Mira et al. (2013) <doi:10.1007/s11222-012-9344-6>), regularised ZV-CV (South et al., 2018 <doi:10.48550/arXiv.1811.05073>), control functionals (CF, Oates et al. (2017) <doi:10.1111/rssb.12185>) and semi-exact control functionals (SECF, South et al., 2020 <doi:10.48550/arXiv.2002.00033>). ZV-CV is a parametric approach that is exact for (low order) polynomial integrands with Gaussian targets. CF is a non-parametric alternative that offers better than the standard Monte Carlo convergence rates. SECF has both a parametric and a non-parametric component and it offers the advantages of both for an additional computational cost. Functions for applying ZV-CV and CF to two estimators for the normalising constant of the posterior distribution in Bayesian statistics are also supplied in this package. The basic requirements for using the package are a set of samples, derivatives and function evaluations.
How to cite:
Leah F. South (2019). ZVCV: Zero-Variance Control Variates. R package version 2.1.3, https://cran.r-project.org/web/packages/ZVCV. Accessed 09 Mar. 2026.
Previous versions and publish date:
1.0.0 (2019-01-24 01:00), 2.1.0 (2020-06-17 11:40), 2.1.1 (2021-06-30 16:50), 2.1.2 (2022-11-02 10:30)
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Complete documentation for ZVCV
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