Provides survival analysis functions with support for time-dependent 
    and subject-specific (e.g., propensity score) weighting. Implements weighted 
    estimation for Cox models, Kaplan-Meier survival curves, and treatment 
    differences with point-wise and simultaneous confidence bands. Includes 
    restricted mean survival time (RMST) comparisons evaluated across all 
    potential truncation times with both point-wise and simultaneous confidence 
    bands. See Cole, S. R. & Hernán, M. A. (2004) 
    <doi:10.1016/j.cmpb.2003.10.004> for methodological background.

Treemaps are a visually appealing graphical representation of
    numerical data using a space-filling approach. A plane or 'map' is
    subdivided into smaller areas called cells.  The cells in the map are
    scaled according to an underlying metric which allows to grasp the
    hierarchical organization and relative importance of many objects at
    once. This package contains two different implementations of treemaps,
    Voronoi treemaps and Sunburst treemaps.  The Voronoi treemap function
    subdivides the plot area in polygonal cells according to the highest
    hierarchical level, then continues to subdivide those parental cells
    on the next lower hierarchical level, and so on. The Sunburst treemap
    is a computationally less demanding treemap that does not require
    iterative refinement, but simply generates circle sectors that are
    sized according to predefined weights.  The Voronoi tesselation is
    based on functions from Paul Murrell (2012)
    <https://www.stat.auckland.ac.nz/~paul/Reports/VoronoiTreemap/voronoiTreeMap.html>.

Provides tools for weighted spatial tessellation using
    Euclidean and geodesic distances within constrained polygonal domains.
    The package can generate complete and connected spatial partitions
    that respect complex boundaries, heterogeneous point weights, and optional
    resistance or terrain effects. The methods extend weighted Voronoi
    tessellations to constrained domains and graph-based cost-distance
    surfaces. For background see Aurenhammer (1991)
    <doi:10.1145/116873.116880> and van Etten (2017)
    <doi:10.18637/jss.v076.i13>.