Perform state and parameter inference, and forecasting, in stochastic state-space systems using the 'ctsmTMB' class. This class, built with the 'R6' package, provides a user-friendly interface for defining and handling 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, both implemented in 'C++' using 'Rcpp' (Eddelbuettel et al., 2018) <doi:10.1080/00031305.2017.1375990> for computational efficiency.
Version: 1.0.0 Depends: R (≥ 4.0.0) Imports: TMB, RTMB (≥ 1.7), R6, Deriv, stringr, Rcpp, RcppEigen, RcppXPtrUtils, RcppZiggurat, Matrix, deSolve, ggplot2, ggfortify, patchwork LinkingTo: Rcpp, RcppEigen, RcppZiggurat Suggests: testthat (≥ 3.0.0), knitr, rmarkdown, RTMBode Published: 2025-04-08 DOI: 10.32614/CRAN.package.ctsmTMB Author: Phillip Vetter [aut, cre, cph], Jan Møller [ctb], Uffe Thygesen [ctb], Peder Bacher [ctb], Henrik Madsen [ctb] Maintainer: Phillip Vetter <pbrve at dtu.dk> BugReports: https://github.com/phillipbvetter/ctsmTMB/issues License: GPL-3 Copyright: See the file COPYRIGHTSPlease use the canonical form https://CRAN.R-project.org/package=ctsmTMB to link to this page.
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