README.md

In hzambran/hydroGOF: Goodness-of-Fit Functions for Comparison of Simulated and Observed Hydrological Time Series

hydroGOF

hydroGOF is an R package that provides S3 functions implementing both statistical and graphical goodness-of-fit measures between observed and simulated values, mainly oriented to be used during the calibration, validation, and application of hydrological models.

Missing values in observed and/or simulated values can be automatically removed before the computations.

Bugs / comments / questions / collaboration of any kind are very welcomed.

Installation

Installing the latest stable version from CRAN:

install.packages("hydroGOF")

Alternatively, you can also try the under-development version from Github:

if (!require(devtools)) install.packages("devtools")
library(devtools)
install_github("hzambran/hydroGOF")

Reporting bugs, requesting new features

If you find an error in some function, or want to report a typo in the documentation, or to request a new feature (and wish it be implemented :) you can do it here

Citation

citation("hydroGOF")

To cite hydroGOF in publications use:

Zambrano-Bigiarini, Mauricio (2024). hydroGOF: Goodness-of-fit functions for comparison of simulated and observed hydrological time series. R package version 0.5-4. URL:https://cran.r-project.org/package=hydroGOF. doi:10.5281/zenodo.839854.

A BibTeX entry for LaTeX users is

@Manual{hydroGOF, title = {hydroGOF: Goodness-of-fit functions for comparison of simulated and observed hydrological time series}, author = {Zambrano-Bigiarini, Mauricio}, note = {R package version 0.5-4}, year = {2024}, url = {https://cran.r-project.org/package=hydroGOF}, doi = {10.5281/zenodo.839854}, }

Vignette

Here you can find an introductory vignette illustrating the use of several hydroGOF functions.

Related Material

• R: a statistical environment for hydrological analysis (EGU-2010) abstract, poster.

• Comparing Goodness-of-fit Measures for Calibration of Models Focused on Extreme Events (EGU-2012) abstract, poster.

• Using R for analysing spatio-temporal datasets: a satellite-based precipitation case study (EGU-2017) abstract, poster.

See Also

• hydroTSM: Time series management, analysis and interpolation for hydrological modelling.

• hydroPSO: Model-independent Particle Swarm Optimisation (PSO) for environmental/hydrological models.

• RFmerge: Merging of Satellite Datasets with Ground Observations using Random Forests.

hzambran/hydroGOF documentation built on March 27, 2024, 11:21 p.m.