hydroGOF v0.4-0 on CRAN
Following a request made by CRAN regarding compatibility issues with the upcoming version of R, the new version of hydroGOF (v0.4-0) was released today March 12th 2020, and it is available on CRAN now https://cran.r-project.org/package=hydroGOF
Among its new features stand out:
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Package tested against R Under development (unstable) (2020-03-10 r77920) – “Unsuffered Consequences”, following an imperative request made by CRAN.
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Citation file changed, following CRAN comments.
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Vignette on Goodness-of-fit Measures to Compare Obserligved and Simulated Values was moved from Sweave to Knitr.
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New references were added for
KGE(Santos et. al, 2018; Knoben et al., 2019; Mizukami et al., 2019) -
New reference was added for
me(Hill et al., 2006) Thanks to Erli Pinto dos Santos !. -
br2: new argument ‘use.abs=FALSE’, to allow the user to use ‘abs(b)’ as condition to decide whether using abs(b)*r2 or [1/abs(b)]*r2 in equation (5) in Krausse et al. (2005). Thanks to Ellie White !
I hope you enjoy it !.
My Caption
I am an Associate Professor in the Department of Civil Engineering at the University of La Frontera. 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 😃