Maximum likelihood estimation for univariate reducible
 stochastic differential equation models. Discrete, possibly noisy
 observations, not necessarily evenly spaced in time. Can fit
 multiple individuals/units with global and local parameters, by
 fixed-effects or mixed-effects methods. Ref.: Garcia, O. (2019)
 "Estimating reducible stochastic differential equations by
 conversion to a least-squares problem", Computational Statistics
 34(1): 23-46, <doi:10.1007/s00180-018-0837-4>.

Functions for dissimilarity analysis and memory-based learning 
    (MBL, a.k.a local modeling) in complex spectral data sets. 
    Most of these functions are based on the methods presented in 
    Ramirez-Lopez et al. (2013) <doi:10.1016/j.geoderma.2012.12.014>.

Measure single-storage water supply system performance using resilience,
    reliability, and vulnerability metrics; assess storage-yield-reliability
    relationships; determine no-fail storage with sequent peak analysis; optimize
    release decisions for water supply, hydropower, and multi-objective reservoirs
    using deterministic and stochastic dynamic programming; generate inflow
    replicates using parametric and non-parametric models; evaluate inflow
    persistence using the Hurst coefficient.

A simple user-friendly library based on the 'python' module 'reservoirpy'.
             It provides a flexible interface to implement efficient Reservoir
             Computing (RC) architectures with a particular focus on Echo State Networks
             (ESN). Some of its features are: offline and online training, parallel implementation, 
             sparse matrix computation, fast spectral initialization, advanced learning 
             rules (e.g. Intrinsic Plasticity) etc. It also makes possible to easily create 
             complex architectures with multiple reservoirs (e.g. deep reservoirs), readouts, 
             and complex feedback loops. Moreover, graphical tools are included to easily 
             explore hyperparameters. Finally, it includes several tutorials exploring
             time series forecasting, classification and hyperparameter tuning. For more information
             about 'reservoirpy', please see Trouvain et al. (2020) <doi:10.1007/978-3-030-61616-8_40>.
             This package was developed in the framework of the University of Bordeaux’s IdEx
             "Investments for the Future" program / RRI PHDS.