https://behroozmoosavi.r-universe.devRecent package updates in behroozmoosaviR-universehttps://github.com/behroozmoosavi.pnghttps://behroozmoosavi.r-universe.devTue, 26 May 2026 20:54:49 GMTbem159@pitt.edu (Behrooz Moosavi)Provides lightweight, dependency-minimal implementations of Langevin diffusion based Markov chain Monte Carlo samplers, including the Unadjusted Langevin Algorithm (ULA) and the Metropolis-Adjusted Langevin Algorithm (MALA). The core sampling loops are written in C++ via 'Rcpp' and 'RcppArmadillo' for performance, while exposing a simple R-level interface where the user supplies the gradient of the negative log-density (and, for MALA, the negative log-density itself). Intended as a building block for Bayesian inference and stochastic optimization rather than a full probabilistic programming framework. Methods follow Roberts and Tweedie (1996) <doi:10.2307/3318418> and Roberts and Rosenthal (1998) <doi:10.1111/1467-9868.00123>.https://github.com/r-universe/behroozmoosavi/actions/runs/39019453534Tue, 26 May 2026 20:54:49 GMTLangevinFlow0.1.0successhttps://behroozmoosavi.r-universe.devhttps://github.com/behroozmoosavi/langevinflowintroduction.Rmdintroduction.htmlIntroduction to LangevinFlow2026-05-26 20:43:242026-05-26 20:43:24