LangevinFlow: 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>.

Version:	0.1.0
Depends:	R (≥ 3.5.0)
Imports:	Rcpp (≥ 1.0.0), stats, graphics
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown, covr
Published:	2026-05-29
DOI:	10.32614/CRAN.package.LangevinFlow
Author:	Behrooz Moosavi [aut, cre]
Maintainer:	Behrooz Moosavi <bem159 at pitt.edu>
BugReports:	https://github.com/BehroozMoosavi/LangevinFlow/issues
License:	MIT + file LICENSE
URL:	https://github.com/BehroozMoosavi/LangevinFlow
NeedsCompilation:	yes
Materials:	NEWS
CRAN checks:	LangevinFlow results

Documentation:

Reference manual:	LangevinFlow.html , LangevinFlow.pdf
Vignettes:	Introduction to LangevinFlow (source, R code)

Downloads:

Package source:	LangevinFlow_0.1.0.tar.gz
Windows binaries:	r-devel: LangevinFlow_0.1.0.zip, r-release: LangevinFlow_0.1.0.zip, r-oldrel: LangevinFlow_0.1.0.zip
macOS binaries:	r-release (arm64): LangevinFlow_0.1.0.tgz, r-oldrel (arm64): LangevinFlow_0.1.0.tgz, r-release (x86_64): LangevinFlow_0.1.0.tgz, r-oldrel (x86_64): LangevinFlow_0.1.0.tgz

Linking:

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