Events from individual hydrologic time series are extracted, and events
 are matched across multiple time series. The package has been applied in studies
 such as Wasko and Guo (2022) <doi:10.1002/hyp.14563> and Mohammadpour Khoie,
 Guo and Wasko (2025) <doi:10.1016/j.envsoft.2025.106521>.

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 removed before computations. Comments / questions / collaboration of any kind are very welcomed.

A collection of utilities that support creation of network attributes for hydrologic networks. Methods and algorithms implemented are documented in Moore et al. (2019) <doi:10.3133/ofr20191096>, Cormen and Leiserson (2022) <ISBN:9780262046305> and Verdin and Verdin (1999) <doi:10.1016/S0022-1694(99)00011-6>.

Estimates the parameters of infiltration and water retention models using the curve-fitting methods as shown in Omuto and Gumbe (2009) <doi:10.1016/j.cageo.2008.08.011>. The models considered are those that are commonly used in soil science. Version 2 of the package has new models for water retention characteristic curves.

Classifies hydraulic conditions into nominal fluvial mesohabitat
    categories using water depth and velocity. It implements the eight-class
    scheme in the preprint by Cordero and Harris <doi:10.2139/ssrn.7100727>, supports
    validated rectangular custom schemes, and works with tabular and spatial
    data through 'terra'. Outputs describe hydraulic classes and do not by
    themselves establish biological habitat quality or species occurrence.

State-of-the-art Multi-Objective Particle Swarm Optimiser (MOPSO), based on the algorithm developed by Lin et al. (2018) <doi:10.1109/TEVC.2016.2631279> with improvements described by Marinao-Rivas & Zambrano-Bigiarini (2020) <doi:10.1109/LA-CCI48322.2021.9769844>. This package is inspired by and closely follows the philosophy of the single objective 'hydroPSO' R package ((Zambrano-Bigiarini & Rojas, 2013) <doi:10.1016/j.envsoft.2013.01.004>), and can be used for global optimisation of non-smooth and non-linear R functions and R-base models (e.g., 'TUWmodel', 'GR4J', 'GR6J'). However, the main focus of 'hydroMOPSO' is optimising environmental and other real-world models that need to be run from the system console (e.g., 'SWAT+'). 'hydroMOPSO' communicates with the model to be optimised through its input and output files, without requiring modifying its source code. Thanks to its flexible design and the availability of several fine-tuning options, 'hydroMOPSO' can tackle a wide range of multi-objective optimisation problems (e.g., multi-objective functions, multiple model variables, multiple periods). Finally, 'hydroMOPSO' is designed to run on multi-core machines or network clusters, to alleviate the computational burden of complex models with long execution time.