Zero-Variance Control Variates R package ZVCV version 2.1.3 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., 2023 <doi:10.1214/22-BA1328>), control functionals (CF, Oates et al. (2017) <doi:10.1111/rssb.12185>) and semi-exact control functionals (SECF, South et al., 2022 <doi:10.1093/biomet/asab036>). 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. Software Imports: Rcpp (>= 0.11.0), glmnet, abind, mvtnorm, stats, Rlinsolve, magrittr, dplyr LinkingTo: Rcpp, RcppArmadillo, BH Suggests: partitions, ggplot2, ggthemes Leah F. South <leah.south@hdr.qut.edu.au> Comprehensive R Archive Network (CRAN) Leah F. South [aut, cre] (ORCID: <https://orcid.org/0000-0002-5646-2963>) GPL (>= 2) 2025-10-21 application/tgz https://CRAN.R-project.org/package=ZVCV doi:10.32614/CRAN.package.ZVCV