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lawstat: An R Package for Law, Public Policy and Biostatistics

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  • Hui, Wallace
  • Gel, Yulia R.
  • Gastwirth, Joseph L.

Abstract

We present a new R software package lawstat that contains statistical tests and procedures that are utilized in various litigations on securities law, antitrust law, equal employment and discrimination as well as in public policy and biostatistics. Along with the well known tests such as the Bartels test, runs test, tests of homogeneity of several sample proportions, the Brunner-Munzel tests, the Lorenz curve, the Cochran-Mantel-Haenszel test and others, the package contains new distribution-free robust tests for symmetry, robust tests for normality that are more sensitive to heavy-tailed departures, measures of relative variability, Levene-type tests against trends in variances etc. All implemented tests and methods are illustrated by simulations and real-life examples from legal cases, economics and biostatistics. Although the package is called lawstat, it presents implementation and discussion of statistical procedures and tests that are also employed in a variety of other applications, e.g., biostatistics, environmental studies, social sciences and others, in other words, all applications utilizing statistical data analysis. Hence, name of the package should not be considered as a restriction to legal statistics. The package will be useful to applied statisticians and "quantitatively alert practitioners" of other subjects as well as an asset in teaching statistical courses.

Suggested Citation

  • Hui, Wallace & Gel, Yulia R. & Gastwirth, Joseph L., 2008. "lawstat: An R Package for Law, Public Policy and Biostatistics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i03).
    • Handle: RePEc:jss:jstsof:v:028:i03
    DOI: http://hdl.handle.net/10.18637/jss.v028.i03
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    References listed on IDEAS

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    1. Einmahl, J.H.J. & McKeague, I.W., 2002. "Empirical Likelihood based on Hypothesis Testing," Other publications TiSEM 402576fa-8c0e-45e2-a394-8, Tilburg University, School of Economics and Management.
    2. Gel, Yulia R. & Miao, Weiwen & Gastwirth, Joseph L., 2007. "Robust directed tests of normality against heavy-tailed alternatives," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2734-2746, February.
    3. Antonietta Mira, 1999. "Distribution-free test for symmetry based on Bonferroni's measure," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(8), pages 959-972.
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    5. Bonett, Douglas G. & Seier, Edith, 2002. "A test of normality with high uniform power," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 435-445, September.
    6. Gel, Yulia R. & Gastwirth, Joseph L., 2008. "A robust modification of the Jarque-Bera test of normality," Economics Letters, Elsevier, vol. 99(1), pages 30-32, April.
    7. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    8. Poitras, Geoffrey, 2006. "More on the correct use of omnibus tests for normality," Economics Letters, Elsevier, vol. 90(3), pages 304-309, March.
    9. Ole E. Barndorff-Nielsen, 1997. "Processes of normal inverse Gaussian type," Finance and Stochastics, Springer, vol. 2(1), pages 41-68.
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    2. Guglielmo Lione & Francesca Brescia & Luana Giordano & Paolo Gonthier, 2022. "Effects of Seasonality and Climate on the Propagule Deposition Patterns of the Chestnut Blight Pathogen Cryphonectria parasitica in Orchards of the Alpine District of North Western Italy," Agriculture, MDPI, vol. 12(5), pages 1-24, April.
    3. I. Parra-Frutos, 2013. "Testing homogeneity of variances with unequal sample sizes," Computational Statistics, Springer, vol. 28(3), pages 1269-1297, June.
    4. Lyubchich, Vyacheslav & Wang, Xingyu & Heyes, Andrew & Gel, Yulia R., 2016. "A distribution-free m-out-of-n bootstrap approach to testing symmetry about an unknown median," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 1-9.
    5. Do, Linh Phuong Catherine & Lyócsa, Štefan & Molnár, Peter, 2021. "Residual electricity demand: An empirical investigation," Applied Energy, Elsevier, vol. 283(C).
    6. Punzo, Antonio & Bagnato, Luca, 2022. "Dimension-wise scaled normal mixtures with application to finance and biometry," Journal of Multivariate Analysis, Elsevier, vol. 191(C).
    7. Lai Yinglei & Gastwirth Joseph L., 2015. "Outlier reset CUSUM for the exploration of copy number alteration data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(4), pages 333-345, August.
    8. Víctor Leiva & Jimmy Corzo & Myrian E. Vergara & Raydonal Ospina & Cecilia Castro, 2024. "A Statistical Methodology for Evaluating Asymmetry after Normalization with Application to Genomic Data," Stats, MDPI, vol. 7(3), pages 1-17, September.
    9. Artur Tiago Silva & Maria Manuela Portela, 2018. "Using Climate-Flood Links and CMIP5 Projections to Assess Flood Design Levels Under Climate Change Scenarios: A Case Study in Southern Brazil," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(15), pages 4879-4893, December.

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