Provides tools for exploration of R package dependencies. 
    The main deepdep() function allows to acquire deep dependencies of any package and plot them in an elegant way.
    It also adds some popularity measures for the packages e.g. in the form of download count through the 'cranlogs' package. 
    Uses the CRAN metadata database <http://crandb.r-pkg.org> and Bioconductor metadata <https://bioconductor.org>.
    Other data acquire functions are: get_dependencies(), get_downloads() and get_description(). 
    The deepdep_shiny() function runs shiny application that helps to produce a nice 'deepdep' plot.

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.  Gradient-enhancement and gradient
    predictions are offered following Booth (2025, <doi:10.48550/arXiv.2512.18066>).
    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. (2025, <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, 
    2025, <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.