Giulio Barcaroli, Daniela Pagliuca, Egon Willighagen,
Diego Zardetto

Giulio Barcaroli, Marco Ballin, Daniela Pagliuca, Egon Willighagen,
Diego Zardetto

Giulio Barcaroli, Marco Ballin, Hanjo Odendaal, Daniela Pagliuca, Egon Willighagen,
Diego Zardetto
R (>= 2.15.0), memoise, doParallel, pbapply

Giulio Barcaroli, Marco Ballin, Hanjo Odendaal, Daniela Pagliuca, Egon Willighagen, Diego Zardetto
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.
R (>= 2.15.0), memoise, doParallel, pbapply, formattable

R (>= 3.5.0), memoise, doParallel, pbapply, formattable

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. M.Ballin, G.Barcaroli (2020) <arXiv:2004.09366> "R package SamplingStrata: new developments and extension to Spatial Sampling".
R (>= 3.5.0), memoise, doParallel, pbapply, SamplingBigData,
glue