Fast, flexible framework for implementing Bayesian optimization of model 
	hyperparameters according to the methods described in Snoek et al. <arXiv:1206.2944>.
	The package allows the user to run scoring function in parallel, save intermediary 
	results, and tweak other aspects of the process to fully utilize the computing resources
	available to the user.

Complex graphical representations of data are best explored using interactive elements.
  'parcats' adds interactive graphing capabilities to the 'easyalluvial' package.
  The 'plotly.js' parallel categories diagrams offer a good framework for
  creating interactive flow graphs that allow manual drag and drop sorting of dimensions
  and categories, highlighting single flows and displaying mouse over information. The
  'plotly.js' dependency is quite heavy and therefore is outsourced into a separate package.

Construct parser combinator functions, higher order functions that
  parse input. Construction of such parsers is transparent and easy. Their main
  application is the parsing of structured text files like those generated by
  laboratory instruments. Based on a paper by Hutton (1992)
  <doi:10.1017/S0956796800000411>.

Parallelized version of the "segment" function from Bioconductor package "DNAcopy", utilizing multi-core computation on host CPU

Reviews other packages during code review by looking at their
    dependencies, code style, code complexity, and how internally defined
    functions interact with one another.

Utilities for the Pareto, piecewise Pareto and generalized Pareto distribution
    that are useful for reinsurance pricing. In particular, the package provides
    a non-trivial algorithm that can be used to match the expected losses of a 
    tower of reinsurance layers with a layer-independent collective risk model.
    The theoretical background of the matching algorithm and most other methods
    are described in Ulrich Riegel (2018) <doi:10.1007/s13385-018-0177-3>.

Statistical functions to describe a Pareto Positive Stable (PPS) 
    distribution and fit it to real data. Graphical and statistical tools to 
    validate the fits are included.