A wrapper for the 'DeepL' Pro API <https://www.deepl.com/docs-api>, a web service
    for translating texts between different languages. A DeepL API developer account is required
    to use the service (see <https://www.deepl.com/pro#developer>).

Functions providing an easy and intuitive way for fitting and clusters data using the Mixture of Unigrams models by means the Expectation-Maximization algorithm (Nigam, K. et al. (2000). <doi:10.1023/A:1007692713085>), Mixture of Dirichlet-Multinomials estimated by Gradient Descent (Anderlucci, Viroli (2020) <doi:10.1007/s11634-020-00399-3>) and Deep Mixture of Multinomials whose estimates are obtained with Gibbs sampling scheme (Viroli, Anderlucci (2020) <doi:10.1007/s11222-020-09989-9>). There are also functions for graphical representation of clusters obtained.

Implement some deep learning architectures and neural network
    algorithms, including BP,RBM,DBN,Deep autoencoder and so on.

Implementation of some Deep Learning methods. Includes multilayer perceptron, different activation functions, regularisation strategies, stochastic gradient descent and dropout. Thanks go to the following references for helping to inspire and develop the package: Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach (2016, ISBN:978-0262035613) Deep Learning. Terrence J. Sejnowski (2018, ISBN:978-0262038034) The Deep Learning Revolution. Grant Sanderson (3brown1blue) <https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi> Neural Networks YouTube playlist. Michael A. Nielsen <http://neuralnetworksanddeeplearning.com/> Neural Networks and Deep Learning.

A tool that contains trained deep learning models
    for predicting effector proteins. 'deepredeff' has been trained to
    identify effector proteins using a set of known experimentally
    validated effectors from either bacteria, fungi, or oomycetes.
    Documentation is available via several vignettes, and the paper by
    Kristianingsih and MacLean (2020) <doi:10.1101/2020.07.08.193250>.