df2ldl

Helper method to compute the log growth rates of cumulated
variables. It will compute the log cumulative growth rate for each column in
the data frame.

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>.

Craig Thamotheram

tsgc

Time Series Methods Based on Growth Curves

df2ldl function

<dl><dt>dt</dt>
<dd>Cumulated data series.</dd></dl>

Arguments

Compute log growth rate of cumulated dataset — df2ldl

<dl>

<dt>dt</dt>
<dd>Cumulated data series.</dd>

</dl>

df2ldl: Compute log growth rate of cumulated dataset

Description

Usage

Value

Arguments

Examples