Functions to assist in performing probabilistic record linkage and
    deduplication: generating pairs, comparing records, em-algorithm for
    estimating m- and u-probabilities
    (I. Fellegi & A. Sunter (1969) <doi:10.1080/01621459.1969.10501049>, 
    T.N. Herzog, F.J. Scheuren, & W.E. Winkler (2007), 
    "Data Quality and Record Linkage Techniques", ISBN:978-0-387-69502-0),
    forcing one-to-one matching. Can also be
    used for pre- and post-processing for machine learning methods for record
    linkage. Focus is on memory, CPU performance and flexibility.

The analysis of different aspects of biodiversity requires specific algorithms. 
	For example, in regionalisation analyses, the high frequency of ties and zero values in 
	dissimilarity matrices produced by Beta-diversity turnover produces hierarchical 
	cluster dendrograms whose topology and bootstrap supports are affected by the order of 
	rows in the original matrix. Moreover, visualisation of biogeographical regionalisation 
	can be facilitated by a combination of hierarchical clustering and multi-dimensional 
	scaling. The recluster package provides robust techniques to visualise and analyse 
	pattern of biodiversity and to improve occurrence data for cryptic taxa. 
	Other functions 	related to recluster (e.g. the biodecrypt family) are currently 
	available in GitHub at <https://github.com/leondap/recluster>.

Implements the RecMap MP2 construction heuristic
  <doi:10.1109/INFVIS.2004.57>.
  This algorithm draws maps according to a given statistical
  value, e.g., election results, population, or epidemiological data.
  The basic idea of the RecMap algorithm is that each map region,
  e.g., different countries, is represented by a rectangle.
  The area of each rectangle represents the statistical value given
  as input (maintain zero cartographic error).
  C++ is used to implement the computationally intensive tasks.
  The vignette included in this package provides documentation
  about the usage of the recmap algorithm.