Duplicated data can exist in different rows and columns and user may need to
    treat observations (rows) connected by duplicated data as one observation,
    e.g. companies can belong to one family (and thus: be one company) by sharing
    some telephone numbers. This package allows to find connected rows
    based on data on chosen columns and collapse it into one row.

Explore neural networks in a layer oriented way, the framework
    is intended to give the user total control of the
    internals of a net without much effort. Use classes like PerceptronLayer
    to create a layer of Percetron neurons, and specify how many you want. The
    package does all the tricky stuff internally leaving you focused in what
    you want. I wrote this package during a neural networks course to help me
    with the problem set.

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.  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.