Continuous Time Stochastic Modelling using Template Model Builder R package ctsmTMB version 1.1.1 Perform state and parameter inference, and forecasting, in stochastic state-space systems using the 'ctsmTMB' R6 class. This class provides a user-friendly interface for working with stochastic state space models. Inference is based on maximum likelihood estimation, with derivatives efficiently computed through automatic differentiation enabled by the 'TMB'/'RTMB' packages (Kristensen et al., 2016) <doi:10.18637/jss.v070.i05>. The available inference methods include Kalman filters, in addition to a Laplace approximation-based smoothing method. For further details of these methods refer to the documentation of the 'CTSMR' package <https://ctsm.info/ctsmr-reference.pdf> and Thygesen (2025) <doi:10.48550/arXiv.2503.21358>. Forecasting capabilities include moment predictions and stochastic path simulations implemented in 'C++' using 'Rcpp' (Eddelbuettel et al., 2018) <doi:10.1080/00031305.2017.1375990> for computational efficiency. Software Depends: R (>= 4.0.0) Imports: Deriv, ggplot2, graphics, grDevices, Matrix, patchwork, R6, Rcpp, rlang, RTMB (>= 1.7), stats, stringr, TMB, utils LinkingTo: Rcpp, RcppArmadillo, RcppEigen, zigg Suggests: knitr, rmarkdown, testthat (>= 3.0.0) Phillip Vetter <pbrve@dtu.dk> Comprehensive R Archive Network (CRAN) Phillip Vetter [aut, cre, cph], Jan Møller [ctb], Uffe Thygesen [ctb], Peder Bacher [ctb], Henrik Madsen [ctb] GPL-3 2026-07-14 application/tgz https://CRAN.R-project.org/package=ctsmTMB doi:10.32614/CRAN.package.ctsmTMB