{
  "_id": "6a16ba75acfb0bcc41d86bf7",
  "Package": "FuzzySTs",
  "Title": "Fuzzy Statistical Tools",
  "Description": "The main goal of this package is to present various fuzzy\nstatistical tools. It intends to provide an implementation of\nthe theoretical and empirical approaches presented in the book\nentitled \"The signed distance measure in fuzzy statistical\nanalysis. Some theoretical, empirical and programming advances\"\n<doi: 10.1007/978-3-030-76916-1>. For the theoretical\napproaches, see Berkachy R. and Donze L. (2019)\n<doi:10.1007/978-3-030-03368-2_1>. For the empirical\napproaches, see Berkachy R. and Donze L. (2016) <ISBN:\n978-989-758-201-1>). Important (non-exhaustive) implementation\nhighlights of this package are as follows: (1) a numerical\nprocedure to estimate the fuzzy difference and the fuzzy\nsquare. (2) two numerical methods of fuzzification. (3) a\nfunction performing different possibilities of distances,\nincluding the signed distance and the generalized signed\ndistance for instance with all its properties. (4) numerical\nestimations of fuzzy statistical measures such as the variance,\nthe moment, etc. (5) two methods of estimation of the bootstrap\ndistribution of the likelihood ratio in the fuzzy context. (6)\nan estimation of a fuzzy confidence interval by the likelihood\nratio method. (7) testing fuzzy hypotheses and/or fuzzy data by\nfuzzy confidence intervals in the Kwakernaak - Kruse and Meyer\nsense. (8) a general method to estimate the fuzzy p-value with\nfuzzy hypotheses and/or fuzzy data. (9) a method of estimation\nof global and individual evaluations of linguistic\nquestionnaires. (10) numerical estimations of multi-ways\nanalysis of variance models in the fuzzy context. The unbalance\nin the considered designs are also foreseen.",
  "Version": "0.4",
  "Authors@R": "c(\nperson(given = \"Redina\",\nfamily = \"Berkachy\",\nrole = c(\"aut\", \"cre\"),\nemail = \"redina.berkachy@hefr.ch\",\ncomment = c(ORCID = \"0000-0002-7491-0416\")),\nperson(given = \"Laurent\",\nfamily = \"Donze\",\nrole = \"aut\",\nemail = \"laurent.donze@unifr.ch\",\ncomment = c(ORCID = \"0000-0003-3522-4672\")))",
  "License": "MIT + file LICENSE",
  "RoxygenNote": "7.3.2",
  "Encoding": "UTF-8",
  "VignetteBuilder": "knitr",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-27 09:30:28 UTC",
    "User": "root"
  },
  "Author": "Redina Berkachy [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-7491-0416>), Laurent Donze [aut]\n(ORCID: <https://orcid.org/0000-0003-3522-4672>)",
  "Maintainer": "Redina Berkachy <redina.berkachy@hefr.ch>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2025-07-31 16:47:34 UTC",
  "RemoteUrl": "https://github.com/cran/FuzzySTs",
  "RemoteRef": "HEAD",
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  "MD5sum": "812720a6340af8ed24c6d019fef7cfef",
  "_user": "cran",
  "_type": "src",
  "_file": "FuzzySTs_0.4.tar.gz",
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  "_created": "2026-05-27T09:30:28.000Z",
  "_published": "2026-05-27T09:33:41.191Z",
  "_distro": "noble",
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    "author": "Redina Berkachy <redina.berkachy@hefr.ch>",
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    "message": "version 0.4\n",
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    "email": "redina.berkachy@hefr.ch",
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  "_rbuild": "4.6.0",
  "_assets": [
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    "extra/citation.html",
    "extra/citation.json",
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    "extra/contents.json",
    "extra/FuzzySTs.html",
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  "_releases": [
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      "version": "0.1",
      "date": "2020-07-03"
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    },
    {
      "version": "0.3",
      "date": "2024-07-07"
    },
    {
      "version": "0.4",
      "date": "2025-08-01"
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  ],
  "_exports": [
    "adjusted.weight.MI",
    "adjusted.weight.SI",
    "boot.mean.ml",
    "Defuzz.FANOVA",
    "distance",
    "FANOVA",
    "FANOVA.summary",
    "fci.ml",
    "fci.ml.boot",
    "FMANOVA",
    "FMANOVA.interaction.summary",
    "FMANOVA.summary",
    "Ftests",
    "FTukeyHSD",
    "FUZZ",
    "Fuzzy.CI.ML.test",
    "Fuzzy.CI.test",
    "Fuzzy.decisions",
    "Fuzzy.decisions.ML",
    "Fuzzy.Difference",
    "Fuzzy.exact.variance.poly.left",
    "Fuzzy.exact.variance.poly.right",
    "Fuzzy.p.value",
    "Fuzzy.p.value.mean",
    "Fuzzy.sample.mean",
    "Fuzzy.Square",
    "Fuzzy.Square.poly.left",
    "Fuzzy.Square.poly.right",
    "Fuzzy.variance",
    "GaussianBellFuzzyNumber",
    "GaussianFuzzyNumber",
    "GFUZZ",
    "GLOB.EVAL",
    "GLOB.EVAL.mean",
    "IND.EVAL",
    "integrate.num",
    "is.alphacuts",
    "is.balanced",
    "is.fuzzification",
    "is.trfuzzification",
    "Kurtosis",
    "Moment",
    "nbreakpoints",
    "optimal.distance",
    "R",
    "Ri",
    "SEQ.ORDERING",
    "Skewness",
    "tr.gfuzz",
    "Weighted.fuzzy.mean"
  ],
  "_help": [
    {
      "page": "adjusted.weight.MI",
      "title": "Calculates the adjusted weight for a given main-item of a linguistic questionnaire",
      "topics": [
        "adjusted.weight.MI"
      ]
    },
    {
      "page": "adjusted.weight.SI",
      "title": "Calculates the adjusted weight for a given sub-item of a linguistic questionnaire",
      "topics": [
        "adjusted.weight.SI"
      ]
    },
    {
      "page": "Bertoluzza",
      "title": "Calculates a distance by the d_Bertoluzza between fuzzy numbers",
      "topics": [
        "Bertoluzza"
      ]
    },
    {
      "page": "boot.mean.algo1",
      "title": "Estimates the bootstrap distribution of the likelihood ratio LR by the Algorithm 1 using the mean",
      "topics": [
        "boot.mean.algo1"
      ]
    },
    {
      "page": "boot.mean.algo2",
      "title": "Estimates the bootstrap distribution of the likelihood ratio LR by the Algorithm 2 using the mean",
      "topics": [
        "boot.mean.algo2"
      ]
    },
    {
      "page": "boot.mean.ml",
      "title": "Estimates the bootstrap distribution of the likelihood ratio LR by the Algorithm 1 or 2 using the mean",
      "topics": [
        "boot.mean.ml"
      ]
    },
    {
      "page": "cube",
      "title": "Cube a number",
      "topics": [
        "cube"
      ]
    },
    {
      "page": "D2",
      "title": "Calculates a distance by the D2 between fuzzy numbers",
      "topics": [
        "D2"
      ]
    },
    {
      "page": "Defuzz.FANOVA",
      "title": "Defuzzify the fuzzy sums of squares calculated by a FANOVA model by an exact calculation or an approximation",
      "topics": [
        "Defuzz.FANOVA"
      ]
    },
    {
      "page": "delta_jki",
      "title": "Calculates the factor Delta_jki",
      "topics": [
        "Delta_jki"
      ]
    },
    {
      "page": "Delta.pq",
      "title": "Calculates a distance by the d_Delta.pq between fuzzy numbers",
      "topics": [
        "Delta.pq"
      ]
    },
    {
      "page": "distance",
      "title": "Calculates a distance between fuzzy numbers according to the chosen type",
      "topics": [
        "distance"
      ]
    },
    {
      "page": "DSGD",
      "title": "Calculates a distance by the SGD between fuzzy numbers",
      "topics": [
        "DSGD"
      ]
    },
    {
      "page": "DSGD.G",
      "title": "Calculates a distance by the d_DSGD.G between fuzzy numbers",
      "topics": [
        "DSGD.G"
      ]
    },
    {
      "page": "FANOVA",
      "title": "Computes a FANOVA model by a convenient metric, an exact calculation or an approximation",
      "topics": [
        "FANOVA"
      ]
    },
    {
      "page": "FANOVA.approximation",
      "title": "Computes a FANOVA model by an approximation",
      "topics": [
        "FANOVA.approximation"
      ]
    },
    {
      "page": "FANOVA.distance",
      "title": "Computes a FANOVA model by a convenient metric",
      "topics": [
        "FANOVA.distance"
      ]
    },
    {
      "page": "FANOVA.exact",
      "title": "Computes a FANOVA model by an exact calculation",
      "topics": [
        "FANOVA.exact"
      ]
    },
    {
      "page": "FANOVA.summary",
      "title": "Prints the summary of the estimation of a FANOVA metric-based model",
      "topics": [
        "FANOVA.summary"
      ]
    },
    {
      "page": "fci.ml",
      "title": "Estimates a fuzzy confidence interval by the Likelihood method",
      "topics": [
        "fci.ml"
      ]
    },
    {
      "page": "fci.ml.boot",
      "title": "Estimates a fuzzy confidence interval by the Likelihood method",
      "topics": [
        "fci.ml.boot"
      ]
    },
    {
      "page": "FMANOVA",
      "title": "Computes a Mult-FANOVA model by a convenient metric, an exact calculation or an approximation",
      "topics": [
        "FMANOVA"
      ]
    },
    {
      "page": "FMANOVA.approximation",
      "title": "Computes a Mult-FANOVA model by an approximation",
      "topics": [
        "FMANOVA.approximation"
      ]
    },
    {
      "page": "FMANOVA.distance",
      "title": "Computes a Mult-FANOVA model by a convenient metric",
      "topics": [
        "FMANOVA.distance"
      ]
    },
    {
      "page": "FMANOVA.exact",
      "title": "Computes a Mult-FANOVA model by an exact calculation",
      "topics": [
        "FMANOVA.exact"
      ]
    },
    {
      "page": "FMANOVA.interaction.summary",
      "title": "Prints the summary of the estimation of the interaction in a Mult-FANOVA metric-based model",
      "topics": [
        "FMANOVA.interaction.summary"
      ]
    },
    {
      "page": "FMANOVA.summary",
      "title": "Prints the summary of the estimation of a Mult-FANOVA metric-based model",
      "topics": [
        "FMANOVA.summary"
      ]
    },
    {
      "page": "Ftests",
      "title": "Calculates multiple tests corresponding to the fuzzy response variable",
      "topics": [
        "Ftests"
      ]
    },
    {
      "page": "FTukeyHSD",
      "title": "Calculates the Tukey HSD test corresponding to the fuzzy response variable",
      "topics": [
        "FTukeyHSD"
      ]
    },
    {
      "page": "FUZZ",
      "title": "Fuzzifies a variable modelled by trapezoidal or triangular fuzzy numbers",
      "topics": [
        "FUZZ"
      ]
    },
    {
      "page": "Fuzzy.CI.ML.test",
      "title": "Computes a fuzzy inference test by the fuzzy confidence intervals method calculated by the Likelihood method",
      "topics": [
        "Fuzzy.CI.ML.test"
      ]
    },
    {
      "page": "Fuzzy.CI.test",
      "title": "Computes a fuzzy inference test by the traditional fuzzy confidence intervals",
      "topics": [
        "Fuzzy.CI.test"
      ]
    },
    {
      "page": "Fuzzy.decisions",
      "title": "Computes the fuzzy decisions of a fuzzy inference test by the traditional fuzzy confidence intervals",
      "topics": [
        "Fuzzy.decisions"
      ]
    },
    {
      "page": "Fuzzy.decisions.ML",
      "title": "Computes the fuzzy decisions of a fuzzy inference test by the fuzzy confidence intervals by the likelihood method",
      "topics": [
        "Fuzzy.decisions.ML"
      ]
    },
    {
      "page": "Fuzzy.Difference",
      "title": "Calculates the difference between two fuzzy numbers",
      "topics": [
        "Fuzzy.Difference"
      ]
    },
    {
      "page": "Fuzzy.exact.variance",
      "title": "Calculates the exact variance",
      "topics": [
        "Fuzzy.exact.variance"
      ]
    },
    {
      "page": "Fuzzy.exact.variance.poly.left",
      "title": "Gives the polynomial forms of the numerical alpha-cuts modelling the exact variance",
      "topics": [
        "Fuzzy.exact.variance.poly.left"
      ]
    },
    {
      "page": "Fuzzy.exact.variance.poly.right",
      "title": "Gives the polynomial forms of the numerical alpha-cuts modelling the exact variance",
      "topics": [
        "Fuzzy.exact.variance.poly.right"
      ]
    },
    {
      "page": "Fuzzy.p.value",
      "title": "Computes the fuzzy p-value of a given fuzzy hypothesis test",
      "topics": [
        "Fuzzy.p.value"
      ]
    },
    {
      "page": "Fuzzy.p.value.mean",
      "title": "Computes the fuzzy p-value of a given fuzzy hypothesis test for the mean",
      "topics": [
        "Fuzzy.p.value.mean"
      ]
    },
    {
      "page": "fuzzy.predicted.values",
      "title": "Calculates the fuzzy predicted values",
      "topics": [
        "fuzzy.predicted.values"
      ]
    },
    {
      "page": "fuzzy.residuals",
      "title": "Calculates the fuzzy residuals",
      "topics": [
        "fuzzy.residuals"
      ]
    },
    {
      "page": "Fuzzy.sample.mean",
      "title": "Calculates the fuzzy sample mean",
      "topics": [
        "Fuzzy.sample.mean"
      ]
    },
    {
      "page": "Fuzzy.sample.variance.approximation",
      "title": "Fuzzy sample variance (approx) - general",
      "topics": [
        "Fuzzy.sample.variance.approximation"
      ]
    },
    {
      "page": "Fuzzy.sample.variance.approximation1",
      "title": "Fuzzy sample variance (approx) - method 1",
      "topics": [
        "Fuzzy.sample.variance.approximation1"
      ]
    },
    {
      "page": "Fuzzy.sample.variance.approximation2",
      "title": "Fuzzy sample variance (approx) - method 2",
      "topics": [
        "Fuzzy.sample.variance.approximation2"
      ]
    },
    {
      "page": "Fuzzy.sample.variance.approximation3",
      "title": "Fuzzy sample variance (approx) - method 3",
      "topics": [
        "Fuzzy.sample.variance.approximation3"
      ]
    },
    {
      "page": "Fuzzy.sample.variance.approximation4",
      "title": "Fuzzy sample variance (approx) - method 4",
      "topics": [
        "Fuzzy.sample.variance.approximation4"
      ]
    },
    {
      "page": "Fuzzy.sample.variance.approximation5",
      "title": "Fuzzy sample variance (approx) - method 5",
      "topics": [
        "Fuzzy.sample.variance.approximation5"
      ]
    },
    {
      "page": "Fuzzy.Square",
      "title": "Calculates numerically the square of a fuzzy number",
      "topics": [
        "Fuzzy.Square"
      ]
    },
    {
      "page": "Fuzzy.Square.poly.left",
      "title": "Gives the polynomial expression of the left alpha-levels of the numerical square of a fuzzy number",
      "topics": [
        "Fuzzy.Square.poly.left"
      ]
    },
    {
      "page": "Fuzzy.Square.poly.right",
      "title": "Gives the polynomial expression of the right alpha-levels of the numerical square of a fuzzy number",
      "topics": [
        "Fuzzy.Square.poly.right"
      ]
    },
    {
      "page": "Fuzzy.variance",
      "title": "Calculates the variance by a chosen method: distance, exact or approximation",
      "topics": [
        "Fuzzy.variance"
      ]
    },
    {
      "page": "GaussianBellFuzzyNumber",
      "title": "Creates a Gaussian two-sided bell fuzzy number",
      "topics": [
        "GaussianBellFuzzyNumber"
      ]
    },
    {
      "page": "GaussianFuzzyNumber",
      "title": "Creates a Gaussian fuzzy number",
      "topics": [
        "GaussianFuzzyNumber"
      ]
    },
    {
      "page": "GFUZZ",
      "title": "Fuzzifies a variable modelled by any type of fuzzy numbers",
      "topics": [
        "GFUZZ"
      ]
    },
    {
      "page": "GLOB.EVAL",
      "title": "Calculates the global evaluation of a linguistic questionnaire",
      "topics": [
        "GLOB.EVAL"
      ]
    },
    {
      "page": "GLOB.EVAL.mean",
      "title": "Calculates the weighted mean of the set of individual evaluations",
      "topics": [
        "GLOB.EVAL.mean"
      ]
    },
    {
      "page": "GSGD",
      "title": "Calculates the generalized signed distance between fuzzy numbers",
      "topics": [
        "GSGD"
      ]
    },
    {
      "page": "IND.EVAL",
      "title": "Calculates the individual evaluations of a linguistic questionnaire",
      "topics": [
        "IND.EVAL"
      ]
    },
    {
      "page": "int.0",
      "title": "Numerical integration by the trivial method - method 1",
      "topics": [
        "int.0"
      ]
    },
    {
      "page": "int.ct",
      "title": "Numerical integration by the composite trapezoidal method - method 3",
      "topics": [
        "int.ct"
      ]
    },
    {
      "page": "int.simpson",
      "title": "Numerical integration by the Simpson method - method 4",
      "topics": [
        "int.simpson"
      ]
    },
    {
      "page": "int.t",
      "title": "Numerical integration - method 2",
      "topics": [
        "int.t"
      ]
    },
    {
      "page": "integrate.num",
      "title": "Numerical integration by a particular method",
      "topics": [
        "integrate.num"
      ]
    },
    {
      "page": "is.alphacuts",
      "title": "Verifies if a matrix is set of left and right alpha-cuts",
      "topics": [
        "is.alphacuts"
      ]
    },
    {
      "page": "is.balanced",
      "title": "Verifies if a design is balanced",
      "topics": [
        "is.balanced"
      ]
    },
    {
      "page": "is.fuzzification",
      "title": "Verifies if a matrix is a fuzzification matrix",
      "topics": [
        "is.fuzzification"
      ]
    },
    {
      "page": "is.trfuzzification",
      "title": "Verifies if a matrix is a fuzzification matrix of trapezoidal fuzzy numbers",
      "topics": [
        "is.trfuzzification"
      ]
    },
    {
      "page": "Kurtosis",
      "title": "Calculates the excess of kurtosis of a random fuzzy variable",
      "topics": [
        "Kurtosis"
      ]
    },
    {
      "page": "Mid.Spr",
      "title": "Calculates a distance by the d_Mid.Spr between fuzzy numbers",
      "topics": [
        "Mid.Spr"
      ]
    },
    {
      "page": "Moment",
      "title": "Calculates a central sample moment of a random fuzzy variable",
      "topics": [
        "Moment"
      ]
    },
    {
      "page": "n_jk..",
      "title": "Calculates the number of answers by a specific sub-item",
      "topics": [
        "n_jk.."
      ]
    },
    {
      "page": "n_jkq.",
      "title": "Calculates the number of answers by a specific linguistic of a sub-item",
      "topics": [
        "n_jkq."
      ]
    },
    {
      "page": "nbreakpoints",
      "title": "Calculates the number of breakpoints of a numerical matrix of alpha-cuts",
      "topics": [
        "nbreakpoints"
      ]
    },
    {
      "page": "optimal.distance",
      "title": "Calculates the optimal distance between two fuzzy numbers according to the chosen type",
      "topics": [
        "optimal.distance"
      ]
    },
    {
      "page": "p.value.fisher",
      "title": "Calculates the p-value of fuzzy observations taken from a Fisher distribution",
      "topics": [
        "p.value.fisher"
      ]
    },
    {
      "page": "p.value.log",
      "title": "Calculates the p-value of fuzzy observations taken from a Logistic distribution",
      "topics": [
        "p.value.log"
      ]
    },
    {
      "page": "p.value.mean.log",
      "title": "Calculates the p-value of fuzzy observations taken from a logistic distribution for the mean",
      "topics": [
        "p.value.mean.log"
      ]
    },
    {
      "page": "p.value.mean.normal",
      "title": "Calculates the p-value of fuzzy observations taken from a normal distribution for the mean",
      "topics": [
        "p.value.mean.normal"
      ]
    },
    {
      "page": "p.value.mean.poisson",
      "title": "Calculates the p-value of fuzzy observations taken from a Poisson distribution for the mean",
      "topics": [
        "p.value.mean.poisson"
      ]
    },
    {
      "page": "p.value.mean.Student",
      "title": "Calculates the p-value of fuzzy observations taken from a Student distribution for the mean",
      "topics": [
        "p.value.mean.Student"
      ]
    },
    {
      "page": "p.value.normal",
      "title": "Calculates the p-value of fuzzy observations taken from a normal distribution",
      "topics": [
        "p.value.normal"
      ]
    },
    {
      "page": "p.value.poisson",
      "title": "Calculates the p-value of fuzzy observations taken from a Poisson distribution",
      "topics": [
        "p.value.poisson"
      ]
    },
    {
      "page": "p.value.Student",
      "title": "Calculates the p-value of fuzzy observations taken from a Student distribution",
      "topics": [
        "p.value.Student"
      ]
    },
    {
      "page": "R",
      "title": "Calculates the indicator of information's rate of the data base",
      "topics": [
        "R"
      ]
    },
    {
      "page": "Rho1",
      "title": "Calculates a distance by the Rho1 between fuzzy numbers",
      "topics": [
        "Rho1"
      ]
    },
    {
      "page": "Rho2",
      "title": "Calculates a distance by the Rho2 between fuzzy numbers",
      "topics": [
        "Rho2"
      ]
    },
    {
      "page": "Rhop",
      "title": "Calculates a distance by the d_Rhop between fuzzy numbers",
      "topics": [
        "Rhop"
      ]
    },
    {
      "page": "Ri",
      "title": "Calculates the indicator of information's rate of the data base for a given unit",
      "topics": [
        "Ri"
      ]
    },
    {
      "page": "Sample.variance",
      "title": "Calculates the sample variance by a convenient metric",
      "topics": [
        "Sample.variance"
      ]
    },
    {
      "page": "SEQ.ORDERING",
      "title": "Calculates the sequential sums of squares by a convenient metric",
      "topics": [
        "SEQ.ORDERING"
      ]
    },
    {
      "page": "SEQ.ORDERING.APPROXIMATION",
      "title": "Calculates the sequential sums of squares by an approximation",
      "topics": [
        "SEQ.ORDERING.APPROXIMATION"
      ]
    },
    {
      "page": "SEQ.ORDERING.EXACT",
      "title": "Calculates the sequential sums of squares by an exact calculation",
      "topics": [
        "SEQ.ORDERING.EXACT"
      ]
    },
    {
      "page": "SGD",
      "title": "Calculates a distance by the SGD between fuzzy numbers",
      "topics": [
        "SGD"
      ]
    },
    {
      "page": "Skewness",
      "title": "Calculates the skewness of a random fuzzy variable",
      "topics": [
        "Skewness"
      ]
    },
    {
      "page": "square",
      "title": "Square a number",
      "topics": [
        "square"
      ]
    },
    {
      "page": "tr.gfuzz",
      "title": "Fuzzifies a variable modelled by trapezoidal or triangular fuzzy numbers",
      "topics": [
        "tr.gfuzz"
      ]
    },
    {
      "page": "wabl",
      "title": "Calculates a distance by the d_wabl between fuzzy numbers",
      "topics": [
        "wabl"
      ]
    },
    {
      "page": "Weighted.fuzzy.mean",
      "title": "Calculates the weighted fuzzy sample mean",
      "topics": [
        "Weighted.fuzzy.mean"
      ]
    }
  ],
  "_readme": "https://github.com/cran/FuzzySTs/raw/HEAD/README.md",
  "_rundeps": [
    "FuzzyNumbers",
    "polynom"
  ],
  "_vignettes": [
    {
      "source": "Vignette_D.Rmd",
      "filename": "Vignette_D.html",
      "title": "On evaluations of linguistic questionnaires",
      "engine": "knitr::rmarkdown",
      "headings": [
        "adjusted.weight.SI(): Calculates the adjusted weight for a given sub-item of a linguistic questionnaire",
        "adjusted.weight.MI(): Calculates the adjusted weight for a given main-item of a linguistic questionnaire",
        "IND.EVAL(): Calculates the individual evaluations of a linguistic questionnaire",
        "GLOB.EVAL(): Calculates the global evaluation of a linguistic questionnaire",
        "R(): Calculates the indicator of information's rate of the data base",
        "Ri(): Calculates the indicator of information's rate of the data base for a given unit"
      ],
      "created": "2020-11-23 12:50:03",
      "modified": "2020-11-23 12:50:03",
      "commits": 1
    },
    {
      "source": "Vignette_A.Rmd",
      "filename": "Vignette_A.html",
      "title": "On fuzzification tools, fuzzy arithmetics and metrics",
      "engine": "knitr::rmarkdown",
      "headings": [
        "is.alphacuts(): Verifies if a matrix is set of left and right alpha-cuts",
        "nbreakpoints(): Calculates the number of breakpoints of a numerical matrix of alpha-cuts",
        "GaussianFuzzyNumber(): Creates a Gaussian fuzzy number",
        "GaussianBellFuzzyNumber(): Creates a Gaussian two-sided bell fuzzy number",
        "Fuzzy.Difference(): Calculates the difference between two fuzzy numbers",
        "Fuzzy.Square(): Calculates numerically the square of a fuzzy number",
        "is.fuzzification(): Verifies if a matrix is a fuzzification matrix",
        "is.trfuzzification(): Verifies if a matrix is a fuzzification matrix of trapezoidal fuzzy numbers",
        "tr.gfuzz(): Transforms a trapezoidal fuzzification matrix into a numerical one",
        "FUZZ(): Fuzzifies a variable modelled by trapezoidal or triangular fuzzy numbers",
        "GFUZZ(): Fuzzifies a variable modelled by any type of fuzzy numbers",
        "distance(): Calculates a distance between fuzzy numbers"
      ],
      "created": "2020-07-03 09:50:13",
      "modified": "2020-11-23 12:50:03",
      "commits": 2
    },
    {
      "source": "Vignette_E.Rmd",
      "filename": "Vignette_E.html",
      "title": "On fuzzy analysis of variance",
      "engine": "knitr::rmarkdown",
      "headings": [
        "FMANOVA(): Computes a fuzzy multi-ways analysis of variance (Mult-FANOVA) model",
        "is.balanced(): Verifies if a design is balanced",
        "SEQ.ORDERING(): Calculates the sequential sums of squares",
        "FTukeyHSD(): Calculates the Tukey HSD test corresponding to the fuzzy response variable",
        "Ftests(): Calculates multiple tests corresponding to the fuzzy response variable"
      ],
      "created": "2020-11-23 12:50:03",
      "modified": "2020-11-23 12:50:03",
      "commits": 1
    },
    {
      "source": "Vignette_C.Rmd",
      "filename": "Vignette_C.html",
      "title": "On statistical inference",
      "engine": "knitr::rmarkdown",
      "headings": [
        "boot.mean.ml(): Estimates the bootstrap distribution of the likelihood ratio LR",
        "fci.ml.boot(): Estimates a fuzzy confidence interval by the likelihood method and the bootstrap technique",
        "Fuzzy.decisions(): Computes the fuzzy decisions of a fuzzy inference test by the traditional fuzzy confidence intervals",
        "Fuzzy.CI.test(): Computes a fuzzy inference test by the traditional fuzzy confidence intervals",
        "Fuzzy.CI.ML.test(): Computes a fuzzy inference test by the fuzzy confidence intervals method calculated by the Likelihood method and the bootstrap technique",
        "Fuzzy.p.value(): Computes the fuzzy p-value of a given fuzzy hypothesis test"
      ],
      "created": "2020-11-23 12:50:03",
      "modified": "2020-11-23 12:50:03",
      "commits": 1
    },
    {
      "source": "Vignette_B.Rmd",
      "filename": "Vignette_B.html",
      "title": "On statistical measures",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Fuzzy.sample.mean(): Calculates the fuzzy sample mean",
        "Weighted.fuzzy.mean(): Calculates the weighted fuzzy sample mean",
        "Moment(): Calculates a central sample moment of a random fuzzy variable",
        "Skewness(): Calculates the skewness of a random fuzzy variable",
        "Kurtosis(): Calculates the excess of kurtosis of a random fuzzy variable",
        "Fuzzy.variance(): Calculates the variance of a fuzzy variable"
      ],
      "created": "2020-11-23 12:50:03",
      "modified": "2020-11-23 12:50:03",
      "commits": 1
    }
  ],
  "_score": 3.439332693830263,
  "_indexed": true,
  "_nocasepkg": "fuzzysts",
  "_universes": [
    "cran"
  ],
  "_binaries": [
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      "os": "linux",
      "version": "0.4",
      "date": "2026-05-27T09:32:33.000Z",
      "distro": "noble",
      "commit": "f8016d74e6caf0d1328d3dd46d06a544ae0523ef",
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      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/26502854605"
    },
    {
      "r": "4.6.0",
      "os": "linux",
      "version": "0.4",
      "date": "2026-05-27T09:32:43.000Z",
      "distro": "noble",
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      "status": "success",
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      "buildurl": "https://github.com/r-universe/cran/actions/runs/26502854605"
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    {
      "r": "4.6.0",
      "os": "wasm",
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  ]
}