R package BalancedSampling. Select balanced and spatially balanced probability samples in multi-dimensional spaces with any prescribed inclusion probabilities. It contains fast (C++ via Rcpp) implementations of the included sampling methods. The local pivotal method and spatially correlated Poisson sampling (for spatially balanced sampling) are included. Also the cube method (for balanced sampling) and the local cube method (for doubly balanced sampling) are included.
Keywords for this software
References in zbMATH (referenced in 10 articles )
Showing results 1 to 10 of 10.
- Benedetti, Roberto; Dickson, Maria Michela; Espa, Giuseppe; Pantalone, Francesco; Piersimoni, Federica: A simulated annealing-based algorithm for selecting balanced samples (2022)
- Leuenberger, Michael; Eustache, Esther; Jauslin, Raphaël; Tillé, Yves: Balancing a sample almost perfectly (2022)
- Jauslin, Raphaël; Tillé, Yves: Spatial spread sampling using weakly associated vectors (2020)
- Rivest, Louis-Paul; Ebouele, Sergio Ewane: Sampling a two dimensional matrix (2020)
- Benedetti, R.; Andreano, M. S.; Piersimoni, F.: Sample selection when a multivariate set of size measures is available (2019)
- Dickson, Maria Michela; Grafström, Anton; Giuliani, Diego; Espa, Giuseppe: Efficiency and feasibility of sampling schemes in establishment surveys (2019)
- Tillé, Yves: A general result for selecting balanced unequal probability samples from a stream (2019)
- Wang, Zhonglei; Zhu, Zhengyuan: Spatiotemporal balanced sampling design for longitudinal area surveys (2019)
- Benedetti, R.; Piersimoni, F.; Postiglione, P.: Alternative and complementary approaches to spatially balanced samples (2017)
- Dickson, Maria Michela; Tillé, Yves: Ordered spatial sampling by means of the traveling salesman problem (2016)