Package: RSD 0.3.0

RSD: Compares Random Distributions using Stochastic Dominance

The Stochastic Dominance (SD) is the classical way of comparing two random prospects, using their distribution functions. Almost Stochastic Dominance (ASD) has also been developed to cover the SD failures due to the extreme utility functions. This package focuses on classical and heuristic methods for testing the first and second SD and ASD methods given the probability mass function (PMF) of the random prospects. The goal is to apply these methods easily, efficiently, and effectively on real-world datasets. For more details see Hanoch and Levy (1969) <doi:10.2307/2296431>, Leshno and Levy (2002) <doi:10.1287/mnsc.48.8.1074.169>, and Tzeng et al. (2012) <doi:10.1287/mnsc.1120.1616>.

Authors:Shayan Tohidi [aut, cre], Sigurdur Olafsson [aut]

RSD_0.3.0.tar.gz
RSD_0.3.0.zip(r-4.7-any)RSD_0.3.0.zip(r-4.6-any)RSD_0.3.0.zip(r-4.5-any)
RSD_0.3.0.tgz(r-4.6-any)RSD_0.3.0.tgz(r-4.5-any)
RSD_0.3.0.tar.gz(r-4.7-any)RSD_0.3.0.tar.gz(r-4.6-any)
RSD_0.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
RSD/json (API)

# Install 'RSD' in R:
install.packages('RSD', repos = c('https://shayantohidi.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/shayantohidi/rsd/issues

Datasets:

On CRAN:

Conda:

2.70 score 1 stars 3 scripts 184 downloads 11 exports 29 dependencies

Last updated from:15980941b8. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK121
source / vignettesOK218
linux-releaseOK118
macos-releaseOK156
macos-oldrelOK216
windows-develOK88
windows-releaseOK79
windows-oldrelOK77
wasm-releaseOK99

Exports:afsd.testassd.testcompare.allcompare.paired.distributionscreateStochasticDominancefsd.plotfsd.testscreen.by.asdscreen.by.sdssd.plotssd.test

Dependencies:clicpp11dplyrfarvergenericsggplot2gluegtableisobandlabelinglifecyclemagrittrpillarpkgconfigpurrrR6RColorBrewerrlangS7scalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Compares prospects based on AFSDafsd.test
Calculates area below SSD functionarea.below.ssd.calc
Calculates area between CDFsarea.btwn.cdfs.calc
Calculates area between SSD functionsarea.btwn.ssd.calc
Create the inefficient set by an ASD ruleasd.screen
Compares random prospects by ASSD-LLassd.ll.test
Compares prospects based on ASSD methodsassd.test
Compares random prospects by ASSD-THSassd.ths.test
Calculates the area between x-axis and a straight linecalc.area.below.line
Calculates the intersection point of two lines.calc.intersection
Comparing all pairs by all rules, and finding the setscompare.all
Creating paired distributions and execute all SD and ASD rules on themcompare.paired.distributions
Comparing two numeric vectorscomparison
create a data frame from input parameterscreate.dataframe
Creating paired distributionscreate.paired.distributions
Constructor of StochasticDominance ClasscreateStochasticDominance
The example datasetdata_ex
Calculating Expected valueexpected.values
Drawing the CDFsfsd.plot
Compares random prospects by FSDfsd.test
If two lines have intersection or no.has.intersection
Modify outcome and SSD vectorsmodif.outcome.ssd.calc
Paring all the distributionspair.distributions
Paring all the variablespair.variables
Calculates positive and negative area between CDFs for ASSD-LLpos.neg.area.assd.ll
A wrapper for computing inefficient and efficient setsscreen
Screening by all ASD rulesscreen.by.asd
Screening by all SD rulesscreen.by.sd
Performing all SD and ASD methods on a pairsd.asd.test
Performing SD and ASD tests on distribution pairssd.asd.test.all
Create the inefficient set by an SD rulesd.screen
Sorting all variables based on their outcomessorting.variables
Calculates the SSD values for a prospect.ssd.calc
Drawing the SSDssd.plot
Compares random prospects by SSDssd.test
StochasticDominance ClassStochasticDominance-class