Package: SamplingStrata
Type: Package
Title: Optimal Stratification of Sampling Frames for Multipurpose
Sampling Surveys
Version: 1.3
Date: 2018-09-24
Author: Giulio Barcaroli, Marco Ballin, Hanjo Odendaal, Daniela Pagliuca, Egon Willighagen,
Diego Zardetto
Maintainer: Giulio Barcaroli <gbarcaroli@gmail.com>
Description: In the field of stratified sampling design, this package
offers an approach for the determination of the best
stratification of a sampling frame, the one that ensures the
minimum sample cost under the condition to satisfy precision
constraints in a multivariate and multidomain case. This
approach is based on the use of the genetic algorithm: each
solution (i.e. a particular partition in strata of the sampling
frame) is considered as an individual in a population; the
fitness of all individuals is evaluated applying the
Bethel-Chromy algorithm to calculate the sampling size
satisfying precision constraints on the target estimates.
Functions in the package allows to: (a) analyse the obtained
results of the optimisation step; (b) assign the new strata
labels to the sampling frame; (c) select a sample from the new
frame accordingly to the best allocation.
Functions for the execution of the genetic algorithm are a modified
version of the functions in the 'genalg' package.
License: GPL (>= 2)
LazyLoad: yes
Depends: R (>= 2.15.0), memoise, doParallel, pbapply
NeedsCompilation: no
Packaged: 2018-10-07 22:04:59 UTC; UTENTE
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
URL: https://github.com/barcaroli/SamplingStrata
BugReports: https://github.com/barcaroli/SamplingStrata/issues
RoxygenNote: 6.0.1
Repository: CRAN
Date/Publication: 2018-10-07 22:30:03 UTC

Author: Giulio Barcaroli, Marco Ballin, Hanjo Odendaal, Daniela Pagliuca, Egon Willighagen, Diego Zardetto
Description: In the field of stratified sampling design, this package offers an approach for the determination of the best stratification of a sampling frame, the one that ensures the minimum sample cost under the condition to satisfy precision constraints in a multivariate and multidomain case. This approach is based on the use of the genetic algorithm: each solution (i.e. a particular partition in strata of the sampling frame) is considered as an individual in a population; the fitness of all individuals is evaluated applying the Bethel-Chromy algorithm to calculate the sampling size satisfying precision constraints on the target estimates. Functions in the package allows to: (a) analyse the obtained results of the optimisation step; (b) assign the new strata labels to the sampling frame; (c) select a sample from the new frame accordingly to the best allocation. Functions for the execution of the genetic algorithm are a modified version of the functions in the 'genalg' package.
Depends: R (>= 2.15.0), memoise, doParallel, pbapply, formattable