Aims to provide simple intuitive functions to create quick prototypes of artificial neural network or deep learning models. In addition novel ensemble models like 'deeptree' and 'deepforest' has been included which combines decision trees and neural network.

Deep Gaussian mixture models as proposed by Viroli and McLachlan (2019) 
    <doi:10.1007/s11222-017-9793-z> provide a generalization of classical Gaussian mixtures 
    to multiple layers. Each layer contains a set of latent variables that follow a mixture of 
    Gaussian distributions. To avoid overparameterized solutions, dimension reduction is 
    applied at each layer by way of factor models.

Performs Bayesian posterior inference for deep Gaussian processes 
    following Sauer, Gramacy, and Higdon (2023, <doi:10.48550/arXiv.2012.08015>).  See Sauer 
    (2023, <http://hdl.handle.net/10919/114845>) for comprehensive methodological 
    details and <https://bitbucket.org/gramacylab/deepgp-ex/> for a variety of 
    coding examples. Models are trained through MCMC including elliptical 
    slice sampling of latent Gaussian layers and Metropolis-Hastings 
    sampling of kernel hyperparameters.  Vecchia-approximation for faster 
    computation is implemented following Sauer, Cooper, and Gramacy 
    (2023, <doi:10.48550/arXiv.2204.02904>).  Downstream tasks include sequential design 
    through active learning Cohn/integrated mean squared error 
    (ALC/IMSE; Sauer, Gramacy, and Higdon, 2023), optimization through 
    expected improvement (EI; Gramacy, Sauer, and Wycoff, 2022 <doi:10.48550/arXiv.2112.07457>), 
    and contour location through entropy 
    (Booth, Renganathan, and Gramacy, 2024 <doi:10.48550/arXiv.2308.04420>).  Models 
    extend up to three layers deep; a one layer model is equivalent to typical 
    Gaussian process regression.  Incorporates OpenMP and SNOW parallelization 
    and utilizes C/C++ under the hood.

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.