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.

Define distribution families and fit them to
    interval-censored and interval-truncated data, where the truncation
    bounds may depend on the individual observation. The defined
    distributions feature density, probability, sampling and fitting
    methods as well as efficient implementations of the log-density log
    f(x) and log-probability log P(x0 <= X <= x1) for use in 'TensorFlow'
    neural networks via the 'tensorflow' package. Allows training
    parametric neural networks on interval-censored and interval-truncated
    data with flexible parameterization. Applications include Claims
    Development in Non-Life Insurance, e.g. modelling reporting delay
    distributions from incomplete data, see Bücher, Rosenstock (2022)
    <doi:10.1007/s13385-022-00314-4>.

Contains logic for sample-level variable set scoring using randomized reduced rank reconstruction error. 
  Frost, H. Robert (2023) "Reconstruction Set Test (RESET): a computationally efficient method for 
  single sample gene set testing based on randomized reduced rank reconstruction error" <doi:10.1101/2023.04.03.535366>.