Functions to estimate Conditional Average Treatment Effects (CATE) 
    and Population Average Treatment Effects on the Treated (PATT) from 
    experimental or observational data using the Super Learner (SL) ensemble 
    method and Deep neural networks. The package first provides functions to 
    implement meta-learners such as the Single-learner (S-learner) and 
    Two-learner (T-learner) described in Künzel et al. (2019) 
    <doi:10.1073/pnas.1804597116> for estimating the CATE. The S- and T-learner 
    are each estimated using the SL ensemble method and deep neural networks. It 
    then provides functions to implement the Ottoboni and Poulos (2020) 
    <doi:10.1515/jci-2018-0035>  PATT-C estimator to obtain the PATT from 
    experimental data with noncompliance by using the SL ensemble method and 
    deep neural networks.

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.