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.