Functions for deep learning estimation of Conditional Average Treatment Effects (CATEs) 
    from meta-learner models and Population Average Treatment Effects on the Treated (PATT) in settings with 
    treatment noncompliance using reticulate, TensorFlow and Keras3. Functions in the package also implements
    the conformal prediction framework that enables computation and illustration of conformal prediction (CP) 
    intervals for estimated individual treatment effects (ITEs) from meta-learner models. Additional
    functions in the package permit users to estimate the meta-learner CATEs and the PATT in settings with
    treatment noncompliance using weighted ensemble learning via the super learner approach and R neural networks.

A wrapper for the 'DeepL' API <https://developers.deepl.com/docs>, 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.