| Name : R-tsgc
| |
| Version : 0.0
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp155.1.1
| Date : 2024-09-10 13:55:27
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| Group : Development/Libraries/Other
| Source RPM : R-tsgc-0.0-lp155.1.1.src.rpm
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| Size : 1.14 MB
| |
| Packager : https://www_suse_com/
| |
| Summary : Time Series Methods Based on Growth Curves
|
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>.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/15.5/x86_64 |
