Package: sns Type: Package Title: Stochastic Newton Sampler (SNS) Version: 1.2.2 Date: 2022-11-01 Author: Alireza S. Mahani, Asad Hasan, Marshall Jiang, Mansour T.A. Sharabiani Maintainer: Alireza Mahani Description: 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 . License: GPL (>= 2) Imports: mvtnorm, coda, numDeriv Suggests: RegressionFactory, MfUSampler NeedsCompilation: no Repository: CRAN Packaged: 2022-11-01 19:30:51 UTC; ec2-user Date/Publication: 2022-11-02 11:02:22 UTC