Langevin Diffusion Samplers with a C++ Backend // 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.