ZVCV
Zero-Variance Control Variates
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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2.1.3 |
rolling linux/jammy R-4.5 | ZVCV_2.1.3.tar.gz |
532.9 KiB |
2.1.3 |
rolling linux/noble R-4.5 | ZVCV_2.1.3.tar.gz |
554.9 KiB |
2.1.3 |
rolling source/ R- | ZVCV_2.1.3.tar.gz |
75.8 KiB |
2.1.3 |
latest linux/jammy R-4.5 | ZVCV_2.1.3.tar.gz |
532.9 KiB |
2.1.3 |
latest linux/noble R-4.5 | ZVCV_2.1.3.tar.gz |
554.9 KiB |
2.1.3 |
latest source/ R- | ZVCV_2.1.3.tar.gz |
75.8 KiB |
2.1.3 |
2026-04-26 source/ R- | ZVCV_2.1.3.tar.gz |
75.8 KiB |
2.1.3 |
2026-04-23 source/ R- | ZVCV_2.1.3.tar.gz |
75.8 KiB |
2.1.3 |
2026-04-09 windows/windows R-4.5 | ZVCV_2.1.3.zip |
865.4 KiB |
2.1.2 |
2025-04-20 source/ R- | ZVCV_2.1.2.tar.gz |
74.6 KiB |