Package: Spbsampling 1.3.5

Spbsampling: Spatially Balanced Sampling

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'.

Authors:Francesco Pantalone [aut, cre], Roberto Benedetti [aut], Federica Piersimoni [aut]

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Spbsampling/json (API)
NEWS

# Install 'Spbsampling' in R:
install.packages('Spbsampling', repos = c('https://francescopantalone.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/francescopantalone/spbsampling/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • income_emilia - The income of municipalities of "Emilia Romagna".
  • lucas_abruzzo - LUCAS data for the region "Abruzzo", Italy.
  • simul1 - Simulated Population 1.
  • simul2 - Simulated Population 2.
  • simul3 - Simulated Population 3.

On CRAN:

sampling-methodsspatial-data

6 exports 3 stars 0.94 score 2 dependencies 6 scripts 332 downloads

Last updated 2 years agofrom:8e236a298f. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 16 2024
R-4.5-win-x86_64NOTESep 16 2024
R-4.5-linux-x86_64NOTESep 16 2024
R-4.4-win-x86_64NOTESep 16 2024
R-4.4-mac-x86_64NOTESep 16 2024
R-4.4-mac-aarch64NOTESep 16 2024
R-4.3-win-x86_64NOTESep 16 2024
R-4.3-mac-x86_64NOTESep 16 2024
R-4.3-mac-aarch64NOTESep 16 2024

Exports:hpwdpwdsbistprodstsumswd

Dependencies:RcppRcppArmadillo