Ranked Set Sampling
Selecting a ranked set sample (classical or modified) with a concomita...
Selecting a robust ranked set sample with a concomitant variable
Selecting ranked set sample with a concomitant variable
Selecting double (classical or modified) ranked set sample
Mann-Whitney-Wilcoxon test with RSS
Mean estimation based on ranked set sampling
Selecting a ranked set sample (classical or modified)
observation numbers based on classical and modified ranked set samplin...
Selecting ranked sets
Regression estimator based on ranked set sampling
Selecting a robust ranked set sample
Selecting classical ranked set sample
Sign Test with RSS
Variance estimation based on ranked set sampling
Wilcoxon signed rank test with RSS
Ranked set sampling (RSS) is introduced as an advanced method for data collection which is substantial for the statistical and methodological analysis in scientific studies by McIntyre (1952) (reprinted in 2005) <doi:10.1198/000313005X54180>. This package introduces the first package that implements the RSS and its modified versions for sampling. With 'RSSampling', the researchers can sample with basic RSS and the modified versions, namely, Median RSS, Extreme RSS, Percentile RSS, Balanced groups RSS, Double RSS, L-RSS, Truncation-based RSS, Robust extreme RSS. The 'RSSampling' also allows imperfect ranking using an auxiliary variable (concomitant) which is widely used in the real life applications. Applicants can also use this package for parametric and nonparametric inference such as mean, median and variance estimation, regression analysis and some distribution-free tests where the the samples are obtained via basic RSS.