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RSSampling (version 1.0)

Ranked Set Sampling

Description

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) . 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.

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Version

Install

install.packages('RSSampling')

Monthly Downloads

192

Version

1.0

License

GPL-2

Maintainer

Busra Sevinc

Last Published

March 19th, 2018

Functions in RSSampling (1.0)

Rrss

Selecting a robust ranked set sample
Mrss

Selecting a ranked set sample (classical or modified)
con.rss

Selecting ranked set sample with a concomitant variable
regRSS

Regression estimator based on ranked set sampling
wsrtestrss

Wilcoxon signed rank test with RSS
con.Mrss

Selecting a ranked set sample (classical or modified) with a concomitant variable
meanRSS

Mean estimation based on ranked set sampling
obsno.Mrss

observation numbers based on classical and modified ranked set sampling methods
con.Rrss

Selecting a robust ranked set sample with a concomitant variable
rankedsets

Selecting ranked sets
Drss

Selecting double (classical or modified) ranked set sample
rss

Selecting classical ranked set sample
mwwutestrss

Mann-Whitney-Wilcoxon test with RSS
sign1testrss

Sign Test with RSS
varRSS

Variance estimation based on ranked set sampling