Ranked set sampling (RSS) is an advanced data collection method when the exact mea surement of an observation is difficult and/or expensive used in a number of research areas, e.g., environment, bioinformatics, ecology, etc. In this method, random sets are drawn from a population and the units in sets are ranked with a ranking mechanism which is based on a visual inspection or a concomitant variable. Because of the importance of working with a good design and easy analysis, there is a need for a software tool which provides sampling designs and statistical inferences based on RSS and its modifications. This paper introduces an R package as a free and easy-to-use analysis tool for both sampling processes and statistical inferences based on RSS and its modified versions. For researchers, the RSSampling package provides a sample with RSS, extreme RSS, median RSS, percentile RSS, balanced groups RSS, double versions of RSS, L-RSS, truncation-based RSS, and robust extreme RSS when the judgment rankings are both perfect and imperfect. Researchers can also use this new package to make parametric inferences for the population mean and the variance where the sample is obtained via classical RSS. Moreover, this package includes applications of the nonparametric methods which are one sample sign test, Mann-Whitney-Wilcoxon test, and Wilcoxon signed-rank test procedures. The package is available as RSSampling on CRAN.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2019-039.zip
NSM3, RSSampling, stats, LearnBayes
Bayesian, Distributions, Survival, TeachingStatistics
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For attribution, please cite this work as
Sevinc, et al., "The R Journal: RSSampling: A Pioneering Package for Ranked Set Sampling ", The R Journal, 2019
BibTeX citation
@article{RJ-2019-039,
author = {Sevinc, Busra and Cetintav, Bekir and Esemen, Melek and Gurler, Selma},
title = {The R Journal: RSSampling: A Pioneering Package for Ranked Set Sampling },
journal = {The R Journal},
year = {2019},
note = {https://doi.org/10.32614/RJ-2019-039},
doi = {10.32614/RJ-2019-039},
volume = {11},
issue = {1},
issn = {2073-4859},
pages = {401-415}
}