Provides a minimal R and C++ API for parsing
  well-known binary and well-known text representation of
  geometries to and from R-native formats.
  Well-known binary is compact
  and fast to parse; well-known text is human-readable
  and is useful for writing tests. These formats are
  useful in R only if the information they contain can be
  accessed in R, for which high-performance functions
  are provided here.

Utility functions to convert between the 'Spatial' classes
  specified by the package 'sp', and the well-known binary '(WKB)'
  representation for geometry specified by the 'Open Geospatial Consortium'.
  Supports 'Spatial' objects of class 'SpatialPoints',
  'SpatialPointsDataFrame', 'SpatialLines', 'SpatialLinesDataFrame',
  'SpatialPolygons', and 'SpatialPolygonsDataFrame'. Supports 'WKB' geometry
  types 'Point', 'LineString', 'Polygon', 'MultiPoint', 'MultiLineString', and
  'MultiPolygon'. Includes extensions to enable creation of maps with
  'TIBCO Spotfire'.

Establishes and maintains vertex pool topology for geometry 
    handled by 'wk'. Segments are the atomic unit, vertices are shared via
    integer references into a pool. Topology is made discoverable via coincident
    vertex detection while not requiring modification of the input data. 
    Topological data models follow principles described in Worboys and
    Duckham (2004, ISBN:978-0415283755). The edge-based topology geometry decomposed 
    into vertices and directed edge pairs is a simplification of the quad-edge case in
    Guibas & Stolfi (1985) <doi:10.1145/282918.282923>.

Converts weekly data to monthly data.
     Users can use three types of week formats: ISO week, epidemiology week (epi week) and calendar date.

It proposes a novel variable selection approach in classification problem that takes into account the correlations that may exist between the predictors of the design matrix in a high-dimensional logistic model. Our approach consists in rewriting the initial high-dimensional logistic model to remove the correlation between the predictors and in applying the generalized Lasso criterion.