{
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  "Package": "fitdistrplus",
  "Title": "Help to Fit of a Parametric Distribution to Non-Censored or\nCensored Data",
  "Version": "1.2-6",
  "Authors@R": "c(person(\"Marie-Laure\", \"Delignette-Muller\", role = \"aut\", email = \"marielaure.delignettemuller@vetagro-sup.fr\", comment = c(ORCID = \"0000-0001-5453-3994\")),\nperson(\"Christophe\", \"Dutang\", role = \"aut\", email = \"christophe.dutang@ensimag.fr\", comment = c(ORCID = \"0000-0001-6732-1501\")),\nperson(\"Regis\", \"Pouillot\", role = \"ctb\"),\nperson(\"Jean-Baptiste\", \"Denis\", role = \"ctb\"),\nperson(\"Aurélie\", \"Siberchicot\", role = c(\"aut\", \"cre\"), email = \"aurelie.siberchicot@univ-lyon1.fr\", comment = c(ORCID = \"0000-0002-7638-8318\")))",
  "Description": "Extends the fitdistr() function (of the MASS package) with\nseveral functions to help the fit of a parametric distribution\nto non-censored or censored data. Censored data may contain\nleft censored, right censored and interval censored values,\nwith several lower and upper bounds. In addition to maximum\nlikelihood estimation (MLE), the package provides moment\nmatching (MME), quantile matching (QME), maximum\ngoodness-of-fit estimation (MGE) and maximum spacing estimation\n(MSE) methods (available only for non-censored data). Weighted\nversions of MLE, MME, QME and MSE are available. See e.g.\nCasella & Berger (2002), Statistical inference, Pacific Grove,\nfor a general introduction to parametric estimation.",
  "VignetteBuilder": "knitr",
  "BuildVignettes": "true",
  "License": "GPL (>= 2)",
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  "URL": "https://lbbe-software.github.io/fitdistrplus/,\nhttps://lbbe.univ-lyon1.fr/fr/fitdistrplus,\nhttps://github.com/lbbe-software/fitdistrplus",
  "BugReports": "https://github.com/lbbe-software/fitdistrplus/issues",
  "Contact": "Marie-Laure Delignette-Muller\n<marielaure.delignettemuller@vetagro-sup.fr> or Christophe\nDutang <christophe.dutang@ensimag.fr>",
  "Repository": "https://lbbe-software.r-universe.dev",
  "Date/Publication": "2026-05-17 19:20:39 UTC",
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  "Packaged": {
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    "User": "root"
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  "Author": "Marie-Laure Delignette-Muller [aut] (ORCID:\n<https://orcid.org/0000-0001-5453-3994>),\nChristophe Dutang [aut] (ORCID:\n<https://orcid.org/0000-0001-6732-1501>),\nRegis Pouillot [ctb],\nJean-Baptiste Denis [ctb],\nAurélie Siberchicot [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-7638-8318>)",
  "Maintainer": "Aurélie Siberchicot <aurelie.siberchicot@univ-lyon1.fr>",
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  "_created": "2026-05-17T20:18:38.000Z",
  "_published": "2026-06-02T16:53:33.548Z",
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    "ppcompcens",
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    "qqcomp",
    "qqcompcens",
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    {
      "page": "fitdistrplus",
      "title": "Overview of the 'fitdistrplus' package",
      "topics": [
        "fitdistrplus-package",
        "fitdistrplus"
      ]
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      "title": "Bootstrap simulation of uncertainty for non-censored data",
      "topics": [
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        "density.bootdist",
        "plot.bootdist",
        "plot.density.bootdist",
        "print.bootdist",
        "print.density.bootdist",
        "summary.bootdist"
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      "title": "Bootstrap simulation of uncertainty for censored data",
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        "density.bootdistcens",
        "plot.bootdistcens",
        "plot.density.bootdistcens",
        "print.bootdistcens",
        "print.density.bootdistcens",
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        "danishuni"
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        "dataFAQscale1",
        "dataFAQscale2"
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      "title": "Species Sensitivity Distribution (SSD) for endosulfan",
      "topics": [
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        "BIC.fitdist",
        "coef.fitdist",
        "fitdist",
        "logLik.fitdist",
        "plot.fitdist",
        "print.fitdist",
        "summary.fitdist",
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        "BIC.fitdistcens",
        "coef.fitdistcens",
        "fitdistcens",
        "logLik.fitdistcens",
        "plot.fitdistcens",
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        "vcov.fitdistcens"
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      "title": "Fictive survival dataset of a french Male population",
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        "fremale"
      ]
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      "title": "Goodness-of-fit statistics",
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        "print.gofstat.fitdist",
        "print.gofstat.fitdistcens"
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      "topics": [
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        "denscomp",
        "graphcomp",
        "ppcomp",
        "qqcomp"
      ]
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    {
      "page": "graphcompcens",
      "title": "Graphical comparison of multiple fitted distributions for censored data",
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        "cdfcompcens",
        "denscompcens",
        "graphcompcens",
        "ppcompcens",
        "qqcompcens"
      ]
    },
    {
      "page": "groundbeef",
      "title": "Ground beef serving size data set",
      "topics": [
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      ]
    },
    {
      "page": "logLik-plot",
      "title": "(Log)likelihood plot for a fit using maximum likelihood",
      "topics": [
        "llplot"
      ]
    },
    {
      "page": "logLik-surface",
      "title": "(Log)likelihood surfaces or (log)likelihood curves",
      "topics": [
        "llcurve",
        "llsurface"
      ]
    },
    {
      "page": "mgedist",
      "title": "Maximum goodness-of-fit fit of univariate continuous distributions",
      "topics": [
        "mge",
        "mgedist"
      ]
    },
    {
      "page": "mledist",
      "title": "Maximum likelihood fit of univariate distributions",
      "topics": [
        "mle",
        "mledist"
      ]
    },
    {
      "page": "mmedist",
      "title": "Matching moment fit of univariate distributions",
      "topics": [
        "mme",
        "mmedist"
      ]
    },
    {
      "page": "msedist",
      "title": "Maximum spacing estimation of univariate distributions",
      "topics": [
        "mse",
        "msedist"
      ]
    },
    {
      "page": "plotdist",
      "title": "Plot of empirical and theoretical distributions for non-censored data",
      "topics": [
        "plotdist"
      ]
    },
    {
      "page": "plotdistcens",
      "title": "Plot of empirical and theoretical distributions for censored data",
      "topics": [
        "plotdistcens"
      ]
    },
    {
      "page": "prefit",
      "title": "Pre-fitting procedure",
      "topics": [
        "prefit"
      ]
    },
    {
      "page": "qmedist",
      "title": "Quantile matching fit of univariate distributions",
      "topics": [
        "qme",
        "qmedist"
      ]
    },
    {
      "page": "quantile",
      "title": "Quantile estimation from a fitted distribution",
      "topics": [
        "print.quantile.bootdist",
        "print.quantile.bootdistcens",
        "print.quantile.fitdist",
        "print.quantile.fitdistcens",
        "quantile",
        "quantile.bootdist",
        "quantile.bootdistcens",
        "quantile.fitdist",
        "quantile.fitdistcens"
      ]
    },
    {
      "page": "salinity",
      "title": "Species-Sensitivity Distribution (SSD) for salinity tolerance",
      "topics": [
        "salinity"
      ]
    },
    {
      "page": "smokedfish",
      "title": "Contamination data of Listeria monocytogenes in smoked fish",
      "topics": [
        "smokedfish"
      ]
    },
    {
      "page": "Surv2fitdistcens",
      "title": "Handling of data formated as in the survival package for use in fitdistcens()",
      "topics": [
        "Surv2fitdistcens"
      ]
    },
    {
      "page": "toxocara",
      "title": "Parasite abundance in insular feral cats",
      "topics": [
        "toxocara"
      ]
    }
  ],
  "_readme": "https://github.com/lbbe-software/fitdistrplus/raw/HEAD/README.md",
  "_rundeps": [
    "lattice",
    "MASS",
    "Matrix",
    "rlang",
    "survival"
  ],
  "_vignettes": [
    {
      "source": "FAQ.Rmd",
      "filename": "FAQ.html",
      "title": "Frequently Asked Questions",
      "author": "Marie Laure Delignette Muller, Christophe Dutang",
      "engine": "knitr::rmarkdown",
      "headings": [
        "1. Questions regarding distributions",
        "1.1. How do I know the root name of a distribution?",
        "1.2. How do I find \"non standard\" distributions?",
        "1.3. How do I set (or find) initial values for non standard distributions?",
        "1.4. Is it possible to fit a distribution with at least 3 parameters?",
        "1.5. Why there are differences between MLE and MME for the lognormal distribution?",
        "1.6. Can I fit a distribution with positive support when data contains negative values?",
        "1.7. Can I fit a finite-support distribution when data is outside that support?",
        "1.8. Can I fit truncated  distributions?",
        "1.9. Can I fit truncated inflated distributions?",
        "1.10. Can I fit a uniform distribution?",
        "1.11. Can I fit a beta distribution with the same shape parameter?",
        "1.12. How to estimate support parameter? the case of the four-parameter beta",
        "2. Questions regarding goodness-of-fit tests and statistics, Cullen-Frey graph",
        "2.1. Where can we find the results of goodness-of-fit tests ?",
        "2.2. Is it reasonable to use goodness-of-fit tests to validate the fit of a distribution ?",
        "2.2.1. Should I reject a distribution because a goodness-of-fit test rejects it ?",
        "2.2.2. Should I accept a distribution because goodness-of-fit tests do not reject it ?",
        "2.3. Why all goodness-of-fit tests are not available for every distribution ?",
        "2.4. How can we use goodness-of-fit statistics to compare the fit of different distributions on a same data set ?",
        "2.5. Can we use a test to compare the fit of two distributions on a same data set ?",
        "2.6. Can we get goodness-of-fit statistics for a fit on censored data ?",
        "2.7. Why Cullen-Frey graph may be misleading?",
        "3. Questions regarding optimization procedures",
        "3.1. How to choose optimization method?",
        "3.2. The optimization algorithm stops with error code, e.g., 100. What shall I do?",
        "3.3 Why distribution with a log argument may converge better?",
        "3.4. What to do when there is a scaling issue?",
        "3.5. How do I set bounds on parameters when optimizing?",
        "3.5.1. Setting bounds for scale parameters",
        "3.5.2. Setting bounds for shape parameters",
        "3.5.3. Setting bounds for probability parameters",
        "3.5.4. Setting bounds for boundary parameters",
        "3.5.5. Setting linear inequality bounds",
        "3.6. How does quantile matching estimation work for discrete distributions?",
        "3.7. Why setting a parameter to the true value does not lead to the expected result for other parameters?",
        "4. Questions regarding uncertainty",
        "4.1. Can we compute marginal confidence intervals on parameter estimates from their reported standard error ?",
        "4.2. How can we compute confidence intervals on quantiles from the fit of a distribution ?",
        "4.3. How can we compute confidence intervals on any function of the parameters of the fitted distribution ?",
        "4.4. How do we choose the bootstrap number?",
        "5. How to personalize plots",
        "5.1. Can I personalize the default plot given for an object of class fitdist or fitdistcens?",
        "5.2. How to personalize goodness-of-fit plots ?",
        "5.3. Is it possible to obtain ggplot2 plots ?",
        "5.4. Is it possible to add the names of the observations in a goodness-of-fit plot,  e.g. the names of the species in the plot of the Species Sensitivity Distribution (SSD) classically used in ecotoxicology ?",
        "6. Questions regarding (left, right and/or interval) censored data",
        "6.1. How to code censored data in fitdistrplus ?",
        "6.2. How do I prepare the input of fitdistcens() with Surv2fitdistcens()?",
        "6.3. How to represent an empirical distribution from censored data ?",
        "6.4. How to assess the goodness-of-fit of a distribution fitted on censored data ?"
      ],
      "created": "2016-04-14 08:52:22",
      "modified": "2026-01-20 16:07:31",
      "commits": 72
    },
    {
      "source": "fitdistrplus_vignette.Rmd",
      "filename": "fitdistrplus_vignette.html",
      "title": "Overview of the fitdistrplus package",
      "author": "Marie Laure Delignette Muller, Christophe Dutang",
      "engine": "knitr::rmarkdown",
      "headings": [
        "1. Introduction",
        "2. Fitting distributions to continuous non-censored data",
        "2.1. Choice of candidate distributions",
        "2.2. Fit of distributions by maximum likelihood estimation",
        "2.3. Uncertainty in parameter estimates",
        "3. Advanced topics",
        "3.1. Alternative methods for parameter estimation",
        "3.3. Fitting distributions to other types of data",
        "4. Conclusion",
        "Acknowledgments",
        "References"
      ],
      "created": "2023-03-28 09:21:44",
      "modified": "2026-01-20 16:07:31",
      "commits": 13
    },
    {
      "source": "starting-values.Rmd",
      "filename": "starting-values.html",
      "title": "Starting values used in fitdistrplus",
      "author": "Marie Laure Delignette Muller, Christophe Dutang",
      "engine": "knitr::rmarkdown",
      "headings": [
        "1. Discrete distributions",
        "1.1. Base R distribution",
        "1.1.1. Geometric distribution",
        "1.1.2. Negative binomial distribution",
        "1.1.3. Poisson distribution",
        "1.1.4. Binomial distribution",
        "1.2. logarithmic distribution",
        "1.3. Zero truncated distributions",
        "1.4. Zero modified distributions",
        "1.5. Poisson inverse Gaussian distribution",
        "2. Continuous distributions",
        "2.1. Normal distribution",
        "2.2. Lognormal distribution",
        "2.3. Beta distribution (of the first kind)",
        "2.4. Other continuous distribution in actuar",
        "2.4.1. Log-gamma",
        "2.4.2. Gumbel",
        "2.4.3. Inverse Gaussian distribution",
        "2.4.4. Generalized beta",
        "2.5. Feller-Pareto family",
        "The gradient with respect to $\\theta, \\alpha, \\gamma, \\tau$ is\\begin{equation}\\nabla\\mathcal L(\\mu, \\theta, \\alpha, \\gamma, \\tau)",
        "2.5.1. Transformed beta",
        "2.5.2. Generalized Pareto",
        "2.5.3. Burr",
        "The survival function is$$1-F(x) = (1+(x/\\theta)^\\gamma)^{-\\alpha}.$$Using the median $q_2$, we have$$\\log(1/2) = - \\alpha \\log(1+(q_2/\\theta)^\\gamma).$$The initial value is\\begin{equation}\\alpha",
        "2.5.4. Loglogistic",
        "2.5.5. Paralogistic",
        "2.5.6. Inverse Burr",
        "2.5.7. Inverse paralogistic",
        "2.5.8. Inverse pareto",
        "2.5.9. Pareto IV",
        "The first and third quartiles $q_1$ and $q_3$ verify$$((\\frac34)^{-1/\\alpha}-1)^{1/\\gamma} = \\frac{q_1-\\mu}{\\theta},((\\frac14)^{-1/\\alpha}-1)^{1/\\gamma} = \\frac{q_3-\\mu}{\\theta}.$$Hence we get two useful relations\\begin{equation}\\gamma",
        "\\frac{\\log\\left(\\frac{(\\frac43)^{1/\\alpha}-1}{(4)^{1/\\alpha}-1}\\right)}{\\log\\left(\\frac{q_1-\\mu}{q_3-\\mu}\\right)},(#eq:pareto4gammarelation)\\end{equation}\\begin{equation}\\theta",
        "2.5.10. Pareto III",
        "Pareto III corresponds to Pareto IV with $\\alpha=1$.\\begin{equation}\\gamma",
        "\\begin{equation}\\theta",
        "2.5.11. Pareto II",
        "2.5.12. Pareto I",
        "2.5.13. Pareto",
        "Pareto corresponds to Pareto IV with $\\gamma=1$, $\\mu=0$.\\begin{equation}\\theta",
        "2.6. Transformed gamma family",
        "2.6.1. Transformed gamma distribution",
        "2.6.2. gamma distribution",
        "2.6.3. Weibull distribution",
        "2.6.4. Exponential distribution",
        "2.7. Inverse transformed gamma family",
        "2.7.1. Inverse transformed gamma distribution",
        "2.7.2. Inverse gamma distribution",
        "2.7.3. Inverse Weibull distribution",
        "2.7.4. Inverse exponential",
        "3. Bibliography",
        "3.1. General books",
        "3.2. Books dedicated to a distribution family",
        "3.3. Books with applications"
      ],
      "created": "2024-02-22 17:54:45",
      "modified": "2025-01-10 14:02:36",
      "commits": 8
    },
    {
      "source": "Optimalgo.Rmd",
      "filename": "Optimalgo.html",
      "title": "Which optimization algorithm to choose?",
      "author": "Marie Laure Delignette Muller, Christophe Dutang",
      "engine": "knitr::rmarkdown",
      "headings": [
        "1. Quick overview of main optimization methods",
        "1.1. Derivative-free optimization methods",
        "1.2. Hessian-free optimization methods",
        "1.2.1. Computing the direction $d_k$",
        "1.2.2. Computing the stepsize $t_k$",
        "1.3. Benchmark",
        "2. Numerical illustration with the beta distribution",
        "2.1. Log-likelihood function and its gradient for beta distribution",
        "2.1.1. Theoretical value",
        "2.1.2. R implementation",
        "2.2. Random generation of a sample",
        "2.3 Fit Beta distribution",
        "2.4. Results of the numerical investigation",
        "3. Numerical illustration with the negative binomial distribution",
        "3.1. Log-likelihood function and its gradient for negative binomial distribution",
        "3.1.1. Theoretical value",
        "3.1.2. R implementation",
        "3.2. Random generation of a sample",
        "3.3. Fit a negative binomial distribution",
        "3.4. Results of the numerical investigation",
        "4. Conclusion"
      ],
      "created": "2016-04-14 07:57:09",
      "modified": "2026-01-20 16:07:31",
      "commits": 25
    }
  ],
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