Stochastic Newton Sampler (SNS) R package sns version 1.2.2 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 <https://cran.r-project.org/src/contrib/Archive/MfUSampler/>. Software Imports: mvtnorm, coda, numDeriv Suggests: RegressionFactory, MfUSampler Alireza Mahani <alireza.s.mahani@gmail.com> Comprehensive R Archive Network (CRAN) Alireza S. Mahani, Asad Hasan, Marshall Jiang, Mansour T.A. Sharabiani GPL (>= 2) 2022-11-02 application/tgz https://CRAN.R-project.org/package=sns doi:10.32614/CRAN.package.sns