SEI is an R package to calculate standardised indices. Standardised indices are frequently used to convert measurements to a common scale with a meaningful probabilistic interpretation. Well-known examples include the Standardised Precipitation Index (SPI) and Standardised Precipitation Evapotranspiration Index (SPEI), which are used to monitor hydrological droughts, and the Standardised Residual Load Index (SRLI), and the Standardised Renewable Energy Production Index (SREPI), which have been proposed to monitor energy droughts.
This package facilitates the implementation of standardised indices in practice. It's main features include:
- A range of distributions from which to calculate standardised indices (including flexible non- and semi-parametric methods),
- Non-stationary parametric distribution estimation via the GAMLSS framework,
- Estimation of distribution parameters using several methods, including maximum likelihood, method of moments, and method of L-moments,
- Diagnostic checks to ensure the distributional assumptions are valid for the input data,
- The ability to compute indices on any timescale, and from a rolling window of values,
- Three relevant notions of standardised indices,
- Functions to visualise the calculated standardised indices.
The functionality and usage of the SEI package is discussed in detail in the corresponding vignette. The vignette also lists several possible extensions that could be implemented. Additional comments, suggestions, and input are more than welcome - please feel free to create an issue or get in touch with the authors.
The SEI package is available on CRAN:
install.packages("SEI")
and the developmental version on GitHub can be installed using devtools:
# install.packages("devtools")
library(devtools)
install_github("noeliaof/SEI", build_vignettes = TRUE)
Standardised indices provide a means to convert measurements to a common and interpretable scale. This is useful for many reasons. For example:
- The common scale renders the indices easy to interpret.
- This standardised scale has an underlying probabilistic interpretation, making the indices ideal for risk management and decision-making.
- Since the standardisation can be performed separately under different conditions (e.g. in different seasons or locations), the indices can be defined in a relative sense, facilitating a comparison between measurements in these different conditions.
The general approach to compute the indices is to estimate the cumulative distribution function
The main challenge when calculating standardised indices is estimating the cumulative distribution function
The SEI package offers a wide range of distributions, and additionally provides diagnostic tools to check that the chosen distribution adequately fits the input data. Further details regarding the theory underlying standardised indices, and the functionality of the SEI package, are provided in the package vignette.
To cite the SEI package, please use the following BibTeX entry:
@article{SEI,
title={Calculating Standardised Indices Using SEI},
author={Allen, Sam and Otero, Noelia},
year={2023},
publisher={EarthArXiv}
}