Computational topology, which enables topological data analysis
    (TDA), makes pervasive use of abstract mathematical objects called
    simplicial complexes; see Edelsbrunner and Harer (2010)
    <doi:10.1090/mbk/069>.
    Several R packages and other software libraries used through an R interface
    construct and use data structures that represent simplicial complexes,
    including mathematical graphs viewed as 1-dimensional complexes.
    This package provides coercers (converters) between these data structures.
    Currently supported structures are complete lists of simplices as used by
    'TDA'; the simplex trees of Boissonnat and Maria (2014)
    <doi:10.1007/s00453-014-9887-3> as implemented in 'simplextree' and in
    Python GUDHI (by way of 'reticulate'); and the graph classes of 'igraph' and
    'network', by way of the 'intergraph' package.

Plots the conditional coefficients ("marginal effects") of
    variables included in multiplicative interaction terms.

Provides two different methods, linear and nonlinear, to
    interpolate a bivariate function, scalar-valued or vector-valued. 
    The interpolated data are not necessarily gridded. The algorithms 
    are performed by the 'C++' library 'CGAL' (<https://www.cgal.org/>).

Some interpolation methods taken from 'Boost': barycentric
    rational interpolation, modified Akima interpolation, PCHIP (piecewise
    cubic Hermite interpolating polynomial) interpolation, and Catmull-Rom
    splines.

Package for training interpretable machine learning models. Historically, the most interpretable machine learning models were not very accurate, and the most accurate models were not very interpretable. Microsoft Research has developed an algorithm called the Explainable Boosting Machine (EBM) which has both high accuracy and interpretable characteristics. EBM uses machine learning techniques like bagging and boosting to breathe new life into traditional GAMs (Generalized Additive Models). This makes them as accurate as random forests and gradient boosted trees, and also enhances their intelligibility and editability. Details on the EBM algorithm can be found in the paper by Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad (2015, <doi:10.1145/2783258.2788613>).