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ctsmTMB  

Continuous Time Stochastic Modelling using Template Model Builder
View on CRAN: Click here


Download and install ctsmTMB package within the R console
Install from CRAN:
install.packages("ctsmTMB")

Install from Github:
library("remotes")
install_github("cran/ctsmTMB")

Install by package version:
library("remotes")
install_version("ctsmTMB", "1.0.0")



Attach the package and use:
library("ctsmTMB")
Maintained by
Phillip Vetter
[Scholar Profile | Author Map]
First Published: 2025-04-08
Latest Update: 2025-04-08
Description:
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.
How to cite:
Phillip Vetter (2025). ctsmTMB: Continuous Time Stochastic Modelling using Template Model Builder. R package version 1.0.0, https://cran.r-project.org/web/packages/ctsmTMB. Accessed 04 May. 2025.
Previous versions and publish date:
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Complete documentation for ctsmTMB
Functions, R codes and Examples using the ctsmTMB R package
Full ctsmTMB package functions and examples
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