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  "Description": "Provides lightweight, dependency-minimal implementations\nof Langevin diffusion based Markov chain Monte Carlo samplers,\nincluding the Unadjusted Langevin Algorithm (ULA) and the\nMetropolis-Adjusted Langevin Algorithm (MALA). The core\nsampling loops are written in C++ via 'Rcpp' and\n'RcppArmadillo' for performance, while exposing a simple\nR-level interface where the user supplies the gradient of the\nnegative log-density (and, for MALA, the negative log-density\nitself). Intended as a building block for Bayesian inference\nand stochastic optimization rather than a full probabilistic\nprogramming framework. Methods follow Roberts and Tweedie\n(1996) <doi:10.2307/3318418> and Roberts and Rosenthal (1998)\n<doi:10.1111/1467-9868.00123>.",
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