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>).  Optional monotonic warpings are implemented
    following Barnett et al. (2024, <doi:10.48550/arXiv.2408.01540>).  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 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.