sns: Stochastic Newton Sampler (SNS)

Stochastic Newton Sampler (SNS) is a Metropolis-Hastings-based, Markov Chain Monte Carlo sampler for twice differentiable, log-concave probability density functions (PDFs) where the proposal density function is a multivariate Gaussian resulting from a second-order Taylor-series expansion of log-density around the current point. The mean of the Gaussian proposal is the full Newton-Raphson step from the current point. A Boolean flag allows for switching from SNS to Newton-Raphson optimization (by choosing the mean of proposal function as next point). This can be used during burn-in to get close to the mode of the PDF (which is unique due to concavity). For high-dimensional densities, mixing can be improved via 'state space partitioning' strategy, in which SNS is applied to disjoint subsets of state space, wrapped in a Gibbs cycle. Numerical differentiation is available when analytical expressions for gradient and Hessian are not available. Facilities for validation and numerical differentiation of log-density are provided. Note: Formerly available versions of the MfUSampler can be obtained from the archive <>.

Version: 1.2.2
Imports: mvtnorm, coda, numDeriv
Suggests: RegressionFactory, MfUSampler
Published: 2022-11-02
DOI: 10.32614/CRAN.package.sns
Author: Alireza S. Mahani, Asad Hasan, Marshall Jiang, Mansour T.A. Sharabiani
Maintainer: Alireza Mahani <alireza.s.mahani at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: sns citation info
Materials: ChangeLog
CRAN checks: sns results


Reference manual: sns.pdf
Vignettes: Stochastic Newton Sampler: The R Package sns


Package source: sns_1.2.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): sns_1.2.2.tgz, r-oldrel (arm64): sns_1.2.2.tgz, r-release (x86_64): sns_1.2.2.tgz, r-oldrel (x86_64): sns_1.2.2.tgz
Old sources: sns archive

Reverse dependencies:

Reverse suggests: MfUSampler, RegressionFactory


Please use the canonical form to link to this page.