hydroGOF v0.7-0 on CRAN
After two years, the new version of hydroGOF (v0.7-0) was released on May 1st 2026, and it is available on CRAN now https://cran.r-project.org/package=hydroGOF
Among its new features the following stand out:
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New graphical logo.
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New webpage, created with pkgdown.
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The package DOI was changed from the one given by Zotero to the new DOI given by CRAN (10.32614/CRAN.package.hydroGOF) in June 2024.
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Improved documentation of some functions (e.g., sKGE, KGEkm, pfactor, rfactor)
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New functions:
o PMR : to compute the Proxy for Model Robustness to quantify the temporal stability of model bias, proposed by Royer-Gaspard et al. (2021).
o JDKGE: to compute the Joint Divergence Kling-Gupta Efficiency, which extends the traditional Kling-Gupta efficiency by incorporating a fourth diagnostic component that evaluates the similarity between the probability distributions of simulated and observed values, proposed by Ficchi et al. (2026).
o LME : to compute the Liu-Mean Efficiency, which evaluates how large the error is compared to the average level of the observations, making it particularly useful in hydrological applications where the mean value is a meaningful scale for evaluating prediction accuracy.
o LCE : to compute the Lee and Choi Efficiency, which jointly assess the correlation, variability, and bias component of the error, while explicitly penalizing imbalances between correlation and variability through two complementary terms (r x Alpha and r/Alpha).
o HFB : to compute the median annual high-flows bias, focused on high flows. This function was completely re-formulated with respect to version 0.6-0.1, in order to make it more compatible with APFB and PBIAS, with an optimum value in 0 and not in 1.
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Several enhancements and bugfixes, mostly related to temporal aggregation functions
I hope you enjoy it !.
New logo of the hydroGOF R package
I am an Associate Professor in the Department of Civil Engineering at the University of La Frontera, where I lead the Water Resources Observatory Kimün-Ko. I hold a PhD in Environmental Engineering from the University of Trento (Italy) and completed postdoctoral training at the European Commission’s Joint Research Centre.
I have more than 20 years of experience in water resources research and have previously served as an Associate Researcher at the Center for Climate and Resilience Research (CR)2 and as a member of the Earth Sciences Assessment Group of the Chilean National Research and Development Agency (ANID).
My research lies at the interface of hydrology, data science, and environmental sciences, with a particular focus on the use of gridded datasets and open-source tools to investigate droughts, extreme events, and water-related impacts of global change.
I work across spatial and temporal scales to improve the understanding of catchment-scale hydrological processes and to translate this knowledge into operational modelling, forecasting, and early-warning systems that support robust environmental decision-making.
Please reach out to collaborate 😃