This is a Neural Network regression model implementation using 'Keras', consisting of 10 Long Short-Term Memory layers that are fully connected along with the rest of the inputs.

Linear dimension reduction subspaces can be uniquely defined using orthogonal projection matrices. This package provides tools to compute distances between such subspaces and to compute the average subspace. For details see Liski, E.Nordhausen K., Oja H., Ruiz-Gazen A. (2016) Combining Linear Dimension Reduction Subspaces <doi:10.1007/978-81-322-3643-6_7>.

Estimate haplotypic or composite pairwise linkage disequilibrium
    (LD) in polyploids, using either genotypes or genotype likelihoods. 
    Support is provided to estimate the popular measures of LD: the LD 
    coefficient D, the standardized LD coefficient D', and the Pearson 
    correlation coefficient r. All estimates are returned with corresponding 
    standard errors. These estimates and standard errors can then be used
    for shrinkage estimation. The main functions are ldfast(), ldest(), mldest(),
    sldest(), plot.lddf(), format_lddf(), and ldshrink(). Details of the methods
    are available in Gerard (2021a) <doi:10.1111/1755-0998.13349>
    and Gerard (2021b) <doi:10.1038/s41437-021-00462-5>.

Streamflow (and climate) reconstruction using Linear Dynamical Systems. 
    The advantage of this method is the additional state trajectory which can reveal more information 
    about the catchment or climate system. For details of the method please refer to Nguyen and Galelli 
    (2018) <doi:10.1002/2017WR022114>.

Methods and tools for model selection and multi-model inference (Burnham and Anderson (2002) <doi:10.1007/b97636>, among others). 
             'SUR' (for parameter estimation), 'logit'/'probit' (for binary classification), and 'VARMA' (for time-series forecasting) are implemented.
             Evaluations are both in-sample and out-of-sample. 
             It is designed to be efficient in terms of CPU usage and memory consumption.