varStandardizedEffectSize, 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.