pairedRankTest implementing the exact
paired rank test of Munzel and Brunner (2002) for two dependent samples
(overall mid-ranks, exact conditional null distribution via the shift
algorithm), with an asymptotic t-approximation option for larger
samples.pairedSignTest implementing the exact
sign test for paired samples.MunzelBrunner02.PGI (the patient global
impression data from Munzel and Brunner 2002, Table 2) used to
illustrate and validate the paired rank test.ggplot2::aes_string() calls in
densityCurveOnHistogram and boxplotHV with
tidy-evaluation (aes(.data[[...]]) and
after_stat(density)); the minimum required
ggplot2 version is now 3.4.0.F with FALSE in
ExtractMAStatistics calls and in the
rSimulations default arguments.calculateLargeSampleRandomizedDesignEffectSizes,
NP2GMetaAnalysisSimulation,
NP4GMetaAnalysisSimulation (fixed errors)calculatePhat,
calculateCliffdcalculate2GMdMRE,
calculate4GMdMREcalculateCliffd (added export)calculate2GMdMRE,
calculate4GMdMRE (fixed CentralPHatMdMRE
calculation)calculate2GMdMRE,
calculate4GMdMREsimulateRandomizedBlockDesignEffectSizes,
NP4GroupMetaAnalysisSimulation (now
NP4GMetaAnalysisSimulation),
RandomizedBlockDesignEffectSizes,
percentageInaccuracyOfLargeSampleVarianceApproximationNP4GMetaAnalysisSimulation, NP2GroupMetaAnalysisSimulation
now NP2GMetaAnalysisSimulation, Kendalltaupb now
calculateKendalltaupb, CalculateTheoreticalEffectSizes now
calculatePopulationStatisticsAnalyseResiduals calc.a
calc.b calcCliffdConfidenceIntervals
calcEffectSizeConfidenceIntervals
calcPHatConfidenceIntervals calculate2GMdMRE
calculate4GMdMRE calculateCliffd
calculateLargeSampleRandomizedDesignEffectSizes
calculateLargeSampleRandomizedBlockDesignEffectSizes
calculateNullESAccuracy CatchError
checkIfValidDummyVariable Cliffd.test
crossoverResidualAnalysis doLM
metaanalyseSmallSampleSizeExperiments
NP2GMetaAnalysisSimulation
NP4GMetaAnalysisSimulation PHat.test
simulate2GExperimentData
simulate4GExperimentData
testfunctionParameterChecksvarStandardizedEffectSize,
RandomizedBlocksAnalysis, Kendalltaupb,
Cliffd, calculatePhat,
Calc4GroupNPStats, LaplaceDist,
simulateRandomizedDesignEffectSizes,
RandomExperimentSimulations,
simulateRandomizedBlockDesignEffectSizes,
RandomizedBlocksExperimentSimulations,
NP4GroupMetaAnalysisSimulation,
NP2GroupMetaAnalysisSimulation,
MetaAnalysisSimulations,
CalculateTheoreticalEffectSizes,
RandomizedDesignEffectSizes,
RandomizedBlockDesignEffectSizesData set:
KitchenhamEtAl.CorrelationsAmongParticipants.Madeyski10,
KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello17TOSEM,
KitchenhamEtAl.CorrelationsAmongParticipants.Ricca10TSE,
KitchenhamEtAl.CorrelationsAmongParticipants.Romano18ESEM,
KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello14JVLC,
KitchenhamEtAl.CorrelationsAmongParticipants.Reggio15SSM,
KitchenhamEtAl.CorrelationsAmongParticipants.Gravino15JVLC,
KitchenhamEtAl.CorrelationsAmongParticipants.Ricca14TOSEM,
KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello14EASE,
KitchenhamEtAl.CorrelationsAmongParticipants.Abrahao13TSE,
KitchenhamEtAl.CorrelationsAmongParticipants.Torchiano17JVLC,
KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello15EMSE,
KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello14TOSEM,
New functions including computational procedures used to
reproduce the main findings in a joint paper (planned to be submitted):
Barbara Kitchenham, Lech Madeyski, Giuseppe Scanniello and Carmine
Gravino, “The Importance of the Correlation in Crossover Experiments”:
CalculateRLevel1, ExtractGroupSizeData,
ConstructLevel1ExperimentRData,
ExtractExperimentData,
CalculateLevel2ExperimentRData,
ExtractSummaryStatisticsRandomizedExp,
calculateBasicStatistics,
calculateGroupSummaryStatistics,
rSimulations
MadeyskiLewowski.IndustryRelevantGitHubJavaProjects20191022
- over 15% of entries present in this data set is not present in the
previous data set
MadeyskiLewowski.IndustryRelevantGitHubJavaProjects20190324
due to moved time windows for the project creation and last push
dates.searchForIndustryRelevantGitHubProjects - now supports
flexible creation date and last push thresholds (enabling the script to
better support researchers interested in gathering evolving data
sets).transformHgtoZr,searchForIndustryRelevantGitHubProjectsMadeyskiLewowski.IndustryRelevantGitHubJavaProjects20190324reproduceTablesOfPaperMetaAnalysisForFamiliesOfExperimentsExtractMAStatistics function: it
works with metafor version 2.0-0, but changes to metafor’s
method of providing access to its individual results may introduce
errors into the function.calculateSmallSampleSizeAdjustment,
constructEffectSizes, transformRtoZr,
transformZrtoR, transformHgtoR,
calculateHg, transformRtoHg,
transformZrtoHgapprox, transformZrtoHg,
PrepareForMetaAnalysisGtoR,
ExtractMAStatistics,
aggregateIndividualDocumentStatistics,
reproduceTablesOfPaperMetaAnalysisForFamiliesOfExperiments.KitchenhamMadeyskiBrereton.MetaAnalysisReportedResults,
KitchenhamMadeyskiBrereton.ABBAMetaAnalysisReportedResults,
KitchenhamMadeyskiBrereton.ReportedEffectSizes,
KitchenhamMadeyskiBrereton.ABBAReportedEffectSizes
KitchenhamMadeyskiBrereton.ExpData, and
KitchenhamMadeyskiBrereton.DocDataMadeyskiKitchenham.EUBASdata and
functions getEffectSizesABBA,
effectSizeCIgetTheoreticalEffectSizeVariancesABBAgetSimulationData,
plotOutcomesForIndividualsInEachSequenceGroup,
getEffectSizesABBA, effectSizeCIeffectSizeCI to calculate 95% Confidence
Intervals (CI) on Standardised Effect Sizes (d) for cross-over
repeated-measures designsreproduceSimulationResultsBasedOn500Reps1000Obs function
(we agreed to write joint paper with Dr Curtin describing corrections to
his equations to calculate effect size variances for continuous outcomes
of cross-over clinical trials)getSimulationDataplotOutcomesForIndividualsInEachSequenceGroupgetEffectSizesABBAgetEffectSizesABBAIgnoringPeriodEffectreproduceSimulationResultsBasedOn500Reps1000ObspercentageInaccuracyOfLargeSampleVarianceApproximationproportionOfSignificantTValuesUsingCorrectAnalysisproportionOfSignificantTValuesUsingIncorrectAnalysisKitchenhamMadeyski.SimulatedCrossoverDataSets backed by
functions (varianceSimulation,
getSimulatedCrossoverDataSets) to reproduce the data
set.cloudOfWordsKitchenhamMadeyskiBudgen16.FINNISHKitchenhamMadeyskiBudgen16.PolishSubjectsKitchenhamMadeyskiBudgen16.SubjectDataKitchenhamMadeyskiBudgen16.PolishDataKitchenhamMadeyskiBudgen16.DiffInDiffDataKitchenhamMadeyskiBudgen16.COCOMOdensityCurveOnHistogramboxplotHVboxplotAndDensityCurveOnHistogramprintXTablecloudOfWordsreproduceForestPlotRandomEffectsreproduceMixedEffectsAnalysisWithEstimatedVarianceAndExperimentalDesignModeratorreproduceMixedEffectsAnalysisWithExperimentalDesignModeratorreproduceMixedEffectsForestPlotWithExperimentalDesignModeratorreproduceTableWithEffectSizesBasedOnMeanDifferencesreproduceTableWithPossibleModeratingFactorsreproduceTableWithSourceDataByCiolkowskiCiolkowski09ESEM.MetaAnalysis.PBRvsCBRorARMadeyskiKitchenham.MetaAnalysis.PBRvsCBRorARMadeyski15EISEJ.StudProjects$STUD data
setMadeyski15SQJ.NDCMadeyski15EISEJ.OpenProjectsMadeyski15EISEJ.PropProjectsMadeyski15EISEJ.StudProjects and functions (for
importing data, visualization and descriptive analyses):readExcelSheetdensityCurveOnHistogramboxplotHVboxplotAndDensityCurveOnHistogramSee the package homepage (https://madeyski.e-informatyka.pl/reproducible-research/) for documentation and examples.
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