Univariate and Multivariate Damped Random Walk Processes R package Rdrw version 1.0.3 Provides tools for fitting and simulating univariate and multivariate damped random walk processes, also known as Ornstein-Uhlenbeck processes or first-order continuous-time autoregressive models, CAR(1) or CARMA(1, 0). The package supports irregularly spaced observation times, heteroscedastic measurement errors, missing measurements across multivariate time series, and polynomial mean trends in normalized time. The current implementation models up to ten time series jointly. Kalman filtering is used to evaluate the likelihood efficiently for maximum likelihood estimation and Bayesian posterior sampling. Users should preserve sufficient numerical precision when loading astronomical observation times; see the manual for details. Also see Hu and Tak (2020) <doi:10.48550/arXiv.2005.08049>. Software Depends: R (>= 3.5.0) Imports: MASS Hyungsuk Tak <hyungsuk.tak@gmail.com> Comprehensive R Archive Network (CRAN) Zhirui Hu [aut], Hyungsuk Tak [aut, cre] GPL-2 2026-07-02 application/tgz https://CRAN.R-project.org/package=Rdrw doi:10.32614/CRAN.package.Rdrw