Spbsampling

Description

Selection of spatially balanced samples. In particular, the implemented sampling designs allow to select probability samples well spread over the population of interest, in any dimension and using any distance function (e.g. Euclidean distance, Manhattan distance). For more details, Pantalone F, Benedetti R, and Piersimoni F (2022) <doi:10.18637/jss.v103.c02>, Benedetti R and Piersimoni F (2017) <doi:10.1002/bimj.201600194>, and Benedetti R and Piersimoni F (2017) <arXiv:1710.09116>. The implementation has been done in C++ through the use of 'Rcpp' and 'RcppArmadillo'.

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Maintainer: ‘Francesco Pantalone <pantalone.fra@gmail.com>’

The Description field contains
  <arXiv:1710.09116>. The implementation has been done in C++ through the
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Maintainer: ‘Francesco Pantalone <pantalone.fra@gmail.com>’

The Description field contains
  <arXiv:1710.09116>. The implementation has been done in C++ through the
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

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Maintainer: ‘Francesco Pantalone <pantalone.fra@gmail.com>’

The Description field contains
  <arXiv:1710.09116>. The implementation has been done in C++ through the
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Maintainer: ‘Francesco Pantalone <pantalone.fra@gmail.com>’

The Description field contains
  <arXiv:1710.09116>. The implementation has been done in C++ through the
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

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