ctsmTMB
1.1.1Continuous Time Stochastic Modelling using Template Model Builder
Overview
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.
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- OK2026-07-2213 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-2511 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- NOTE2026-04-2212 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-1811 OK · 2 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- NOTE2026-03-1012 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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- Return-value docs
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- References docs
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3 releases. Pick two to compare their code metrics. R releases are shown for context.
- 1.1.1Latest
- RR 4.6.0 released · 2026-04-24
- 1.0.12025-08-28 · diff ↗
- RR 4.5.0 released · 2025-04-11
- 1.0.02025-04-08
- RR 4.4.0 released · 2024-04-24
Package metadata
- First published
- 2025-04-08
- Total releases
- 3 / 1 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 4.0.0
- Bundled data
- 497 KB / 4 files
- Download size
- 1.1 MB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet