Create and learn Chain Event Graph (CEG) models using a Bayesian 
    framework. It provides us with a Hierarchical Agglomerative algorithm to 
    search the CEG model space.
    The package also includes several facilities for visualisations of the
    objects associated with a CEG. The CEG class can represent a range of
    relational data types, and supports arbitrary vertex, edge and graph
    attributes. A Chain Event Graph is a tree-based graphical model that
    provides a powerful graphical interface through which domain experts can
    easily translate a process into sequences of observed events using plain
    language. CEGs have been a useful class of graphical model especially to
    capture context-specific conditional independences. References: Collazo R, 
    Gorgen C, Smith J. Chain Event Graph. CRC Press, ISBN 9781498729604, 2018
    (forthcoming); and Barday LM, Collazo RA, Smith JQ, Thwaites PA, Nicholson AE. 
    The Dynamic Chain Event Graph. Electronic Journal of Statistics, 9 (2) 2130-2169
    <doi:10.1214/15-EJS1068>.

Model building, surrogate model
    based optimization and Efficient Global Optimization in combinatorial
    or mixed search spaces.

Contains a number of common astronomy conversion routines, particularly the HMS and degrees schemes, which can be fiddly to convert between on mass due to the textural nature of the former. It allows users to coordinate match datasets quickly. It also contains functions for various cosmological calculations.

Data from statistical agencies and other institutions often
    need to be protected before they can be published. This package 
    can be used to perturb statistical tables in a consistent way. The
    main idea is to add - at the micro data level - a record key for each
    unit. Based on these keys, for any cell in a statistical table a
    cell key is computed as a function on the record keys contributing to
    a specific cell. Values that are added to the cell in order to perturb
    it are derived from a lookup-table that maps values of cell keys to 
    specific perturbation values. The theoretical basis for the methods 
    implemented can be found in Thompson, Broadfoot and Elazar 
    (2013) <https://unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.46/2013/Topic_1_ABS.pdf>
    which was extended and enhanced by Giessing and Tent 
    (2019) <https://unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.46/2019/mtg1/SDC2019_S2_Germany_Giessing_Tent_AD.pdf>.