Calc4GroupNPStats       Calc4GroupNPStats
CalculateLevel2ExperimentRData
                        CalculateLevel2ExperimentRData
CalculateRLevel1        CalculateRLevel1
CalculateTheoreticalEffectSizes
                        CalculateTheoreticalEffectSizes
Ciolkowski09ESEM.MetaAnalysis.PBRvsCBRorAR
                        Ciolkowski09ESEM.MetaAnalysis.PBRvsCBRorAR data
                        form a set of primary studies on reading
                        methods for software inspections. They were
                        reported and analysed by M. Ciolkowski ("What
                        do we know about perspective-based reading? an
                        approach for quantitative aggregation in
                        software engineering", in Proceedings of the
                        3rd International Symposium on Empirical
                        Software Engineering and Measurement, ESEM'09,
                        pp. 133-144, IEEE Computer Society, 2009),
                        corrected and re-analysed by Madeyski and
                        Kitchenham ("How variations in experimental
                        designs impact the construction of comparable
                        effect sizes for meta-analysis" (to be
                        submitted)).
Cliffd                  Cliffd
ConstructLevel1ExperimentRData
                        ConstructLevel1ExperimentRData
ExtractExperimentData   ExtractExperimentData
ExtractGroupSizeData    ExtractGroupSizeData
ExtractMAStatistics     ExtractMAStatistics
ExtractSummaryStatisticsRandomizedExp
                        ExtractSummaryStatisticsRandomizedExp
Kendalltaupb            Kendalltaupb
KitchenhamEtAl.CorrelationsAmongParticipants.Abrahao13TSE
                        KitchenhamEtAl.CorrelationsAmongParticipants.Abrahao13TSE
                        data illustrate correlations between results
                        from individual participants in a family of
                        five cross-over experiments conducted by
                        Abrahao et al: [1] S. Abrahao, C. Gravino, E.
                        Insfran Pelozo, G. Scanniello, and G. Tortora,
                        "Assessing the effectiveness of sequence
                        diagrams in the comprehension of functional
                        requirements: Results from a family of five
                        experiments," IEEE Transactions on Software
                        Engineering, vol. 39, no. 3, pp. 327342, March
                        2013 The five experiments assess whether the
                        comprehensibility of function requirements
                        improve when software models include UML
                        sequence diagrams. If you use this data set
                        please cite: [1] S. Abrahao, C. Gravino, E.
                        Insfran Pelozo, G. Scanniello, and G. Tortora,
                        "Assessing the effectiveness of sequence
                        diagrams in the comprehension of functional
                        requirements: Results from a family of five
                        experiments," IEEE Transactions on Software
                        Engineering, vol. 39, no. 3, pp. 327342, March
                        2013 [2] Barbara Kitchenham, Lech Madeyski,
                        Giuseppe Scanniello and Carmine Gravino, "The
                        importance of the Correlation between Results
                        from Individual Participants in Crossover
                        Experiments" (to be submitted as of 2020).
KitchenhamEtAl.CorrelationsAmongParticipants.Gravino15JVLC
                        KitchenhamEtAl.CorrelationsAmongParticipants.Gravino15JVLC
                        data illustrate correlations between results
                        from individual participants in a family of 2
                        cross-over experiments conducted by Gravino et
                        al.: [1] C. Gravino, G. Scanniello, and G.
                        Tortora, "Source-code comprehension tasks
                        supported by UML design models: Results from a
                        controlled experiment and a differentiated
                        replication," Journal of Visual Languages and
                        Computing, vol. 28, pp. 2338, 2015. The
                        experiments assess whether the comprehension of
                        object oriented source-code increases used with
                        UML class and sequence diagrams produced in the
                        software design phase. If you use this data set
                        please cite: [1] C. Gravino, G. Scanniello, and
                        G. Tortora, "Source-code comprehension tasks
                        supported by UML design models: Results from a
                        controlled experiment and a differentiated
                        replication," Journal of Visual Languages and
                        Computing, vol. 28, pp. 2338, 2015. [2]
                        Barbara Kitchenham, Lech Madeyski, Giuseppe
                        Scanniello and Carmine Gravino, "The importance
                        of the Correlation between Results from
                        Individual Participants in Crossover
                        Experiments" (to be submitted as of 2020).
KitchenhamEtAl.CorrelationsAmongParticipants.Madeyski10
                        KitchenhamEtAl.CorrelationsAmongParticipants.Madeyski10
                        data illustrate correlations between results
                        from individual participants in cross-over
                        experiment P2007 (Smell&Library) conducted by
                        Madeyski, see: [1] Lech Madeyski, Test-Driven
                        Development: An Empirical Evaluation of Agile
                        Practice. (Heidelberg, London, New York):
                        Springer, 2010. Foreword by Prof. Claes Wohlin.
                        If you use this data set please cite: [1] Lech
                        Madeyski, Test-Driven Development: An Empirical
                        Evaluation of Agile Practice. (Heidelberg,
                        London, New York): Springer, 2010. Foreword by
                        Prof. Claes Wohlin. [2] Barbara Kitchenham,
                        Lech Madeyski, Giuseppe Scanniello and Carmine
                        Gravino, "The importance of the Correlation
                        between Results from Individual Participants in
                        Crossover Experiments" (to be submitted as of
                        2020).
KitchenhamEtAl.CorrelationsAmongParticipants.Reggio15SSM
                        KitchenhamEtAl.CorrelationsAmongParticipants.Reggio15SSM
                        data illustrate correlations between results
                        from individual participants in a family of two
                        cross-over experiments conducted by Reggio et
                        al: [1] G. Reggio, F. Ricca, G. Scanniello, F.
                        D. Cerbo, and G. Dodero,"On the comprehension
                        of workflows modeled with a precise style:
                        results from a family of controlled
                        experiments". Software and Systems Modeling,
                        vol. 14, pp. 14811504, 2015. The experiments
                        assess whether the level of formality/precision
                        in workflow model influences comprehension. If
                        you use this data set please cite: [1] G.
                        Reggio, F. Ricca, G. Scanniello, F. D. Cerbo,
                        and G. Dodero, "On the comprehension of
                        workflows modeled with a precise style: results
                        from a family of controlled experiments".
                        Software and Systems Modeling, vol. 14, pp.
                        14811504, 2015. [2] Barbara Kitchenham, Lech
                        Madeyski, Giuseppe Scanniello and Carmine
                        Gravino, "The Importance of the Correlation
                        between Results from Individual Participants in
                        Crossover Experiments" (to be submitted as of
                        2020).
KitchenhamEtAl.CorrelationsAmongParticipants.Ricca10TSE
                        KitchenhamEtAl.CorrelationsAmongParticipants.Ricca10TSE
                        data illustrate correlations between results
                        from individual participants in a family of
                        four cross-over experiments conducted by Ricca
                        et al.: [1] F. Ricca, M. D. Penta, M.
                        Torchiano, P. Tonella, and M. Ceccato "How
                        developers experience and ability influence
                        web application comprehension tasks supported
                        by uml stereotypes: A series of four
                        experiments", IEEE Transactions on Software
                        Engineering, vol. 36, no. 1, pp. 96-118, 2010.
                        Although we present the full data set, only the
                        first two experiments were used in the
                        correlation study, because many of the
                        observations in the final two studies were
                        unpaired. The experiments assess whether
                        participants performance comprehension tasks
                        better when using source code complemented by
                        standard UML diagrams (UML) or by diagrams
                        stereotyped using the Conallen notation
                        (Conallen). If you use this data set please
                        cite: [1] F. Ricca, M. D. Penta, M. Torchiano,
                        P. Tonella, and M. Ceccato "How developers
                        experience and ability influence web
                        application comprehension tasks supported by
                        uml stereotypes: A series of four experiments",
                        IEEE Transactions on Software Engineering, vol.
                        36, no. 1, pp. 96118, 2010. [2] Barbara
                        Kitchenham, Lech Madeyski, Giuseppe Scanniello
                        and Carmine Gravino, "The Importance of the
                        Correlation between Results from Individual
                        Participants in Crossover Experiments" (to be
                        submitted as of 2020).
KitchenhamEtAl.CorrelationsAmongParticipants.Ricca14TOSEM
                        KitchenhamEtAl.CorrelationsAmongParticipants.Ricca14TOSEM
                        data illustrate correlations between results
                        from individual participants in a family of
                        three of four cross-over experiments conducted
                        by Ricca et al: [1] F. Ricca, G. Scanniello, M.
                        Torchiano, G. Reggio, and E. Astesiano,
                        "Assessing the effect of screen mockups on the
                        comprehension of functional requirements," ACM
                        Transactions on Software Engineering and
                        Methodology, vol. 24, no. 1, pp. 1:11:38, Oct.
                        2014. The goal of the study was to assess
                        whether stakeholders benefit from the presence
                        of screen mock-ups in the comprehension of
                        functional requirements represented with use
                        cases. [2] Barbara Kitchenham, Lech Madeyski,
                        Giuseppe Scanniello and Carmine Gravino, "The
                        importance of the Correlation between Results
                        from Individual Participants in Crossover
                        Experiments" (to be submitted as of 2020).
KitchenhamEtAl.CorrelationsAmongParticipants.Romano18ESEM
                        KitchenhamEtAl.CorrelationsAmongParticipants.Romano18ESEM
                        data illustrate correlations between results
                        from individual participants in a cross-over
                        experiment conducted by Romano et al.: [1] S.
                        Romano, G. Scanniello, D. Fucci, N. Juristo,
                        and B. Turhan, "The effect of noise on software
                        engineers performance", in Proceedings of the
                        12th ACM/IEEE International Symposium on
                        Empirical Software Engineering and Measurement,
                        ser. ESEM'18, 2018. The experiments assess
                        whether noise has an impact on the performance
                        of software engineers. If you use this data set
                        please cite: [1] S. Romano, G. Scanniello, D.
                        Fucci, N. Juristo, and B. Turhan, "The effect
                        of noise on software engineers performance",
                        in Proceedings of the 12th ACM/IEEE
                        International Symposium on Empirical Software
                        Engineering and Measurement, ser. ESEM'18,
                        2018. [2] Barbara Kitchenham, Lech Madeyski,
                        Giuseppe Scanniello and Carmine Gravino, "The
                        Importance of the Correlation between Results
                        from Individual Participants in Crossover
                        Experiments" (to be submitted as of 2020). The
                        experiment had two parts but Kitchenham et al.
                        only use the data from the first part of the
                        experiment.
KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello14EASE
                        KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello14EASE
                        data illustrate correlations between results
                        from individual participants in a family of two
                        cross-over experiments conducted by Scanniello
                        et al: [1] G. Scanniello, M. Staron, H. Burden,
                        and R. Heldal, "On the effect of using SysML
                        requirement diagrams to comprehend
                        requirements: results from two controlled
                        experiments," in Proceedings of the 18th
                        International Conference on Evaluation and
                        Assessment in Software Engineering, EASE. ACM,
                        2014. The two experiments investigate whether
                        requirements specified as SysML requirement
                        diagrams improve the comprehensibility of
                        requirements. If you use this data set please
                        cite: [1] G. Scanniello, M. Staron, H. Burden,
                        and R. Heldal, "On the effect of using SysML
                        requirement diagrams to comprehend
                        requirements: results from two controlled
                        experiments", in Proceedings of the 18th
                        International Conference on Evaluation and
                        Assessment in Software Engineering, EASE. ACM,
                        2014. [2] Barbara Kitchenham, Lech Madeyski,
                        Giuseppe Scanniello and Carmine Gravino, "The
                        importance of the Correlation between Results
                        from Individual Participants in Crossover
                        Experiments" (to be submitted as of 2020).
KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello14JVLC
                        KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello14JVLC
                        data illustrate correlations between results
                        from individual participants in a cross-over
                        experiment conducted by Scanniello and Erra:
                        [1] G. Scanniello and U. Erra, "Distributed
                        modeling of use case diagrams with a method
                        based on think-pair-square: Results from two
                        controlled experiments", Journal of Visual
                        Languages and Computing, vol. 25, no. 4, pp.
                        494517, 2014. The experiment investigated
                        whether a new method based on think-pair-square
                        and its implementation in a integrated
                        communication/modeling environment (TPS
                        approach) is as effective as traditional
                        face-to-face (F2F approach) for requirements
                        elicitation. The experiment was performed in
                        two stages using different software systems. If
                        you use this data set please cite: [1] G.
                        Scanniello and U. Erra, "Distributed modeling
                        of use case diagrams with a method based on
                        think-pair-square: Results from two controlled
                        experiments, Journal of Visual Languages and
                        Computing, vol. 25, no. 4, pp. 494517, 2014.
                        [2] Barbara Kitchenham, Lech Madeyski, Giuseppe
                        Scanniello and Carmine Gravino, "The Importance
                        of the Correlation between Results from
                        Individual Participants in Crossover
                        Experiments" (to be submitted as of 2020).
KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello14TOSEM
                        KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello14TOSEM
                        data illustrate correlations between results
                        from individual participants in a family of
                        four cross-over experiments conducted by
                        Scanniello et al: [1] G.  Scanniello, C.
                        Gravino, M. Genero, J.A. Cruz-Lemus, and G.
                        Tortora, "On the Impact of UML Analysis Models
                        on Source-Code Comprehensibility and
                        Modifiability", ACM Transactions on Software
                        Engineering and Methodlogy, vol. 23, no. 2, pp.
                        13:1-13:26, 2014 The family of experiments
                        investigated whether the availability of
                        analysis models in addition to the source code
                        made the code easier to understand and modify.
                        If you use this data set please cite: [1] G.
                        G. Scanniello, C. Gravino, M. Genero, J.A.
                        Cruz-Lemus, and G. Tortora, "On the Impact of
                        UML Analysis Models on Source-Code
                        Comprehensibility and Modifiability", ACM
                        Transactions on Software Engineering and
                        Methodology, vol. 23, no. 2, pp. 13:1-13:26,
                        2014 [2] Barbara Kitchenham, Lech Madeyski,
                        Giuseppe Scanniello and Carmine Gravino, "The
                        importance of the Correlation between Results
                        from Individual Participants in Crossover
                        Experiments" (to be submitted as of 2020).
KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello15EMSE
                        KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello15EMSE
                        data illustrate correlations between results
                        from individual participants in cross-over
                        experiment usb2 conducted by Scanniello et al:
                        [1] G. Scanniello, A. Marcus, and D. Pascale,
                        "Link analysis algorithms for static concept
                        location: an empirical assessment", Empirical
                        Software Engineering, vol. 20, no. 6, pp.
                        16661720, 2015. The goal of the experiment is
                        to assess whether a new technique (implemented
                        as an Eclipse plug-in) for static concept
                        location (proposed by the authors) supports
                        users in identifying the places in the code
                        where changes are to be made.
KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello17TOSEM
                        KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello17TOSEM
                        data illustrate correlations between results
                        from individual participants in a family of
                        four cross-over experiments conducted by
                        Scanniello et al.: [1] G. Scanniello, M. Risi,
                        P. Tramontana, and S. Romano, "Fixing faults in
                        C and Java source code: Abbreviated vs.
                        full-word identifier names", ACM Transactions
                        on Software Engineering Methodology, vol. 26,
                        no. 2, 2017. The experiments assess whether
                        whether the use of abbreviated identifier names
                        (ABBR), impacts the effectiveness of fault
                        fixing in C and Java source code in comparison
                        with full-word identifier names (FULL). If you
                        use this data set please cite: [1] G.
                        Scanniello, M. Risi, P. Tramontana, and S.
                        Romano, Fixing faults in C and Java source
                        code: Abbreviated vs. full-word identifier
                        names", ACM Transactions on Software
                        Engineering Methodology, vol. 26, no. 2, 2017.
                        [2] Barbara Kitchenham, Lech Madeyski, Giuseppe
                        Scanniello and Carmine Gravino, "On the
                        Importance of the Correlation between Results
                        from Individual Participants in Crossover
                        Experiments" (to be submitted as of 2020).
KitchenhamEtAl.CorrelationsAmongParticipants.Torchiano17JVLC
                        KitchenhamEtAl.CorrelationsAmongParticipants.Torchiano17JVLC
                        data illustrate correlations between results
                        from individual participants in a family of
                        three cross-over experiments conducted by
                        Torchiano et al: [1] M. Torchiano, G.
                        Scanniello, F. Ricca, G. Reggio, and M. Leotta,
                        "Do UML object diagrams affect design
                        comprehensibility? Results from a family of
                        four controlled experiments." Journal of Visual
                        Languages and Computing, vol. 41, pp. 1021,
                        2017. Although the paper reports four
                        experiment, we only have data from three of
                        those experiments. The experiments assess
                        whether the comprehensibility of UML
                        specifications improve when the software
                        documents include UML object diagrams as well
                        as the standard UML class diagrams. If you use
                        this data set please cite: [1] M. Torchiano, G.
                        Scanniello, F. Ricca, G. Reggio, and M. Leotta,
                        "Do UML object diagrams affect design
                        comprehensibility? Results from a family of
                        four controlled experiments." Journal of Visual
                        Languages and Computing, vol. 41, pp. 1021,
                        2017. [2] Barbara Kitchenham, Lech Madeyski,
                        Giuseppe Scanniello and Carmine Gravino, "The
                        importance of the Correlation between Results
                        from Individual Participants in Crossover
                        Experiments" (to be submitted as of 2020).
KitchenhamMadeyski.SimulatedCrossoverDataSets
                        KitchenhamMadeyski.SimulatedCrossoverDataSets
                        data
KitchenhamMadeyskiBrereton.ABBAMetaAnalysisReportedResults
                        KitchenhamMadeyskiBrereton.ABBAMetaAnalysisReportedResults
                        data
KitchenhamMadeyskiBrereton.ABBAReportedEffectSizes
                        KitchenhamMadeyskiBrereton.ABBAReportedEffectSizes
                        data
KitchenhamMadeyskiBrereton.DocData
                        KitchenhamMadeyskiBrereton.DocData data
KitchenhamMadeyskiBrereton.ExpData
                        KitchenhamMadeyskiBrereton.ExpData data
KitchenhamMadeyskiBrereton.MetaAnalysisReportedResults
                        KitchenhamMadeyskiBrereton.MetaAnalysisReportedResults
                        data
KitchenhamMadeyskiBrereton.ReportedEffectSizes
                        KitchenhamMadeyskiBrereton.ReportedEffectSizes
                        data
KitchenhamMadeyskiBudgen16.COCOMO
                        KitchenhamMadeyskiBudgen16.COCOMO data
KitchenhamMadeyskiBudgen16.DiffInDiffData
                        KitchenhamMadeyskiBudgen16.DiffInDiffData data
KitchenhamMadeyskiBudgen16.FINNISH
                        KitchenhamMadeyskiBudgen16.FINNISH data
KitchenhamMadeyskiBudgen16.PolishData
                        KitchenhamMadeyskiBudgen16.PolishData data
KitchenhamMadeyskiBudgen16.PolishSubjects
                        KitchenhamMadeyskiBudgen16.PolishSubjects data
KitchenhamMadeyskiBudgen16.SubjectData
                        KitchenhamMadeyskiBudgen16.SubjectData
LaplaceDist             LaplaceDist
Madeyski15EISEJ.OpenProjects
                        Madeyski15EISEJ.OpenProjects data
Madeyski15EISEJ.PropProjects
                        Madeyski15EISEJ.PropProjects data
Madeyski15EISEJ.StudProjects
                        Madeyski15EISEJ.StudProjects data
Madeyski15SQJ.NDC       Madeyski15SQJ.NDC data
MadeyskiKitchenham.EUBASdata
                        MadeyskiKitchenham.EUBASdata data
MadeyskiKitchenham.MetaAnalysis.PBRvsCBRorAR
                        MadeyskiKitchenham.MetaAnalysis.PBRvsCBRorAR
                        data form a set of primary studies on reading
                        methods for software inspections. They were
                        analysed by Lech Madeyski and Barbara
                        Kitchenham, "How variations in experimental
                        designs impact the construction of comparable
                        effect sizes for meta-analysis", 2015.
MadeyskiLewowski.IndustryRelevantGitHubJavaProjects20190324
                        MadeyskiLewowski.IndustryRelevantGitHubJavaProjects20190324
                        data
MadeyskiLewowski.IndustryRelevantGitHubJavaProjects20191022
                        MadeyskiLewowski.IndustryRelevantGitHubJavaProjects20191022
                        data
MetaAnalysisSimulations
                        MetaAnalysisSimulations
NP2GroupMetaAnalysisSimulation
                        NP2GroupMetaAnalysisSimulation
NP4GroupMetaAnalysisSimulation
                        NP4GroupMetaAnalysisSimulation
PrepareForMetaAnalysisGtoR
                        PrepareForMetaAnalysisGtoR
RandomExperimentSimulations
                        RandomExperimentSimulations
RandomizedBlockDesignEffectSizes
                        RandomizedBlockDesignEffectSizes
RandomizedBlocksAnalysis
                        RandomizedBlocksAnalysis
RandomizedBlocksExperimentSimulations
                        RandomizedBlocksExperimentSimulations
RandomizedDesignEffectSizes
                        RandomizedDesignEffectSizes
aggregateIndividualDocumentStatistics
                        aggregateIndividualDocumentStatistics
boxplotAndDensityCurveOnHistogram
                        boxplotAndDensityCurveOnHistogram
boxplotHV               boxplotHV
calculateBasicStatistics
                        calculateBasicStatistics
calculateGroupSummaryStatistics
                        calculateGroupSummaryStatistics
calculateHg             calculateHg
calculatePhat           calculatePhat
calculateSmallSampleSizeAdjustment
                        calculateSmallSampleSizeAdjustment
constructEffectSizes    constructEffectSizes
densityCurveOnHistogram
                        densityCurveOnHistogram
effectSizeCI            effectSizeCI
fmt                     fmt
getEffectSizesABBA      getEffectSizesABBA
getEffectSizesABBAIgnoringPeriodEffect
                        getEffectSizesABBAIgnoringPeriodEffect
getSimulationData       getSimulationData
getTheoreticalEffectSizeVariancesABBA
                        getTheoreticalEffectSizeVariancesABBA
percentageInaccuracyOfLargeSampleVarianceApproximation
                        percentageInaccuracyOfLargeSampleVarianceApproximation
plotOutcomesForIndividualsInEachSequenceGroup
                        plotOutcomesForIndividualsInEachSequenceGroup
printXTable             printXTable
proportionOfSignificantTValuesUsingCorrectAnalysis
                        proportionOfSignificantTValuesUsingCorrectAnalysis
proportionOfSignificantTValuesUsingIncorrectAnalysis
                        proportionOfSignificantTValuesUsingIncorrectAnalysis
rSimulations            rSimulations
readExcelSheet          readExcelSheet
reproduceForestPlotRandomEffects
                        reproduceForestPlotRandomEffects()
reproduceMixedEffectsAnalysisWithEstimatedVarianceAndExperimentalDesignModerator
                        reproduceMixedEffectsAnalysisWithEstimatedVarianceAndExperimentalDesignModerator()
reproduceMixedEffectsAnalysisWithExperimentalDesignModerator
                        reproduceMixedEffectsAnalysisWithExperimentalDesignModerator()
reproduceMixedEffectsForestPlotWithExperimentalDesignModerator
                        reproduceMixedEffectsForestPlotWithExperimentalDesignModerator()
reproduceSimulationResultsBasedOn500Reps1000Obs
                        reproduceSimulationResultsBasedOn500Reps1000Obs
reproduceTableWithEffectSizesBasedOnMeanDifferences
                        reproduceTableWithEffectSizesBasedOnMeanDifferences()
reproduceTableWithPossibleModeratingFactors
                        reproduceTableWithPossibleModeratingFactors()
reproduceTableWithSourceDataByCiolkowski
                        reproduceTableWithSourceDataByCiolkowski
reproduceTablesOfPaperMetaAnalysisForFamiliesOfExperiments
                        reproduceTablesOfPaperMetaAnalysisForFamiliesOfExperiments
searchForIndustryRelevantGitHubProjects
                        searchForIndustryRelevantGitHubProjects
simulateRandomizedBlockDesignEffectSizes
                        simulateRandomizedBlockDesignEffectSizes
simulateRandomizedDesignEffectSizes
                        simulateRandomizedDesignEffectSizes
transformHgtoR          transformHgtoR
transformHgtoZr         transformHgtoZr
transformRtoHg          transformRtoHg
transformRtoZr          transformRtoZr
transformZrtoHg         transformZrtoHg
transformZrtoHgapprox   transformZrtoHgapprox
transformZrtoR          transformZrtoR
varStandardizedEffectSize
                        varStandardizedEffectSize
