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tsgc
View on CRAN: Click
here
Download and install tsgc package within the R console
Install from CRAN:
install.packages("tsgc")
Install from Github:
library("remotes")
install_github("cran/tsgc")
Install by package version:
library("remotes")
install_version("tsgc", "0.0")
Attach the package and use:
library("tsgc")
Maintained by
Craig Thamotheram
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
https://www.jbs.cam.ac.uk/wp-content/uploads/2024/03/cchle-tsgc-paper-2024.pdf . 10.32614/CRAN.package.tsgc . https://github.com/Craig-PT/tsgc/issues . https://github.com/Craig-PT/tsgc . tsgc results . tsgc.pdf . Forecasting epidemic trajectories: Time Series Growth Curves package 'tsgc' . tsgc_0.0.tar.gz . tsgc_0.0.zip . tsgc_0.0.zip . tsgc_0.0.zip . tsgc_0.0.tgz . tsgc_0.0.tgz . tsgc_0.0.tgz . tsgc_0.0.tgz . tsgc_0.0.tgz . tsgc_0.0.tgz . https://CRAN.R-project.org/package=tsgc .
First Published: 2024-08-26
Latest Update: 2024-08-26
Description:
The 'tsgc' package provides comprehensive tools for the analysis and forecasting of epidemic trajectories. It is designed to model the progression of an epidemic over time while accounting for the various uncertainties inherent in real-time data. Underpinned by a dynamic Gompertz model, the package adopts a state space approach, using the Kalman filter for flexible and robust estimation of the non-linear growth pattern commonly observed in epidemic data. The reinitialization feature enhances the model’s ability to adapt to the emergence of new waves. The forecasts generated by the package are of value to public health officials and researchers who need to understand and predict the course of an epidemic to inform decision-making. Beyond its application in public health, the package is also a useful resource for researchers and practitioners in fields where the trajectories of interest resemble those of epidemics, such as innovation diffusion. The package includes functionalities for data preprocessing, model fitting, and forecast visualization, as well as tools for evaluating forecast accuracy. The core methodologies implemented in 'tsgc' are based on well-established statistical techniques as described in Harvey and Kattuman (2020) <doi:10.1162/99608f92.828f40de>, Harvey and Kattuman (2021) <doi:10.1098/rsif.2021.0179>, and Ashby, Harvey, Kattuman, and Thamotheram (2024) <https://www.jbs.cam.ac.uk/wp-content/uploads/2024/03/cchle-tsgc-paper-2024.pdf>.
How to cite:
Craig Thamotheram (2024). tsgc: Time Series Methods Based on Growth Curves. R package version 0.0, https://cran.r-project.org/web/packages/tsgc. Accessed 17 Feb. 2025.
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