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 zerovariance control variates (ZVCV, Mira et al. (2013) <doi:10.1007/s1122201293446>), regularised ZVCV (South et al., 2018 <arXiv:1811.05073>), control functionals (CF, Oates et al. (2017) <doi:10.1111/rssb.12185>) and semiexact control functionals (SECF, South et al., 2020 <arXiv:2002.00033>). ZVCV is a parametric approach that is exact for (low order) polynomial integrands with Gaussian targets. CF is a nonparametric alternative that offers better than the standard Monte Carlo convergence rates. SECF has both a parametric and a nonparametric component and it offers the advantages of both for an additional computational cost. Functions for applying ZVCV 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.
Package details 


Author  Leah F. South [aut, cre] (<https://orcid.org/0000000256462963>) 
Maintainer  Leah F. South <leah.south@hdr.qut.edu.au> 
License  GPL (>= 2) 
Version  2.1.1 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

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