{
  "_id": "6a16957dacfb0bcc41d7b043",
  "Package": "esvis",
  "Type": "Package",
  "Title": "Visualization and Estimation of Effect Sizes",
  "Version": "0.3.1",
  "Authors@R": "person(\"Daniel\", \"Anderson\", email = \"daniela@uoregon.edu\", \nrole = c(\"aut\", \"cre\"))",
  "Description": "A variety of methods are provided to estimate and\nvisualize distributional differences in terms of effect sizes.\nParticular emphasis is upon evaluating differences between two\nor more distributions across the entire scale, rather than at a\nsingle point (e.g., differences in means). For example,\nProbability-Probability (PP) plots display the difference\nbetween two or more distributions, matched by their empirical\nCDFs (see Ho and Reardon, 2012;\n<doi:10.3102/1076998611411918>), allowing for examinations of\nwhere on the scale distributional differences are largest or\nsmallest. The area under the PP curve (AUC) is an effect-size\nmetric, corresponding to the probability that a randomly\nselected observation from the x-axis distribution will have a\nhigher value than a randomly selected observation from the\ny-axis distribution. Binned effect size plots are also\navailable, in which the distributions are split into bins (set\nby the user) and separate effect sizes (Cohen's d) are produced\nfor each bin - again providing a means to evaluate the\nconsistency (or lack thereof) of the difference between two or\nmore distributions at different points on the scale. Evaluation\nof empirical CDFs is also provided, with built-in arguments for\nproviding annotations to help evaluate distributional\ndifferences at specific points (e.g., semi-transparent\nshading). All function take a consistent argument structure.\nCalculation of specific effect sizes is also possible. The\nfollowing effect sizes are estimable: (a) Cohen's d, (b)\nHedges' g, (c) percentage above a cut, (d) transformed\n(normalized) percentage above a cut, (e) area under the PP\ncurve, and (f) the V statistic (see Ho, 2009;\n<doi:10.3102/1076998609332755>), which essentially transforms\nthe area under the curve to standard deviation units. By\ndefault, effect sizes are calculated for all possible pairwise\ncomparisons, but a reference group (distribution) can be\nspecified.",
  "URL": "https://github.com/datalorax/esvis",
  "BugReports": "https://github.com/datalorax/esvis/issues",
  "License": "MIT + file LICENSE",
  "LazyData": "true",
  "RoxygenNote": "7.0.2",
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  "Repository": "https://datalorax.r-universe.dev",
  "Date/Publication": "2020-04-30 20:03:53 UTC",
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    "description": "Data scientist at Abl (https://github.com/ablschools). Formerly I was a Research Associate Professor in the College of Education at the University of Oregon."
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    "ecdf_plot",
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      "object": "benchmarks",
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        "cohort",
        "sped",
        "ethnicity",
        "frl",
        "ell",
        "season",
        "reading",
        "math"
      ],
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    },
    {
      "name": "seda",
      "title": "Portion of the Stanford Educational Data Archive (SEDA).",
      "object": "seda",
      "class": [
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      ],
      "fields": [
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        "leaname",
        "stateabb",
        "year",
        "grade",
        "subject",
        "mean",
        "se"
      ],
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    },
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      "title": "Data from the Tennessee class size experiment",
      "object": "star",
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        "data.frame"
      ],
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        "schid",
        "condition",
        "tch_experience",
        "sex",
        "freelunch",
        "race",
        "math",
        "reading"
      ],
      "rows": 5748,
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    }
  ],
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    {
      "page": "auc",
      "title": "Compute the Area Under the pp_plot Curve Calculates the area under the 'pp' curve. The area under the curve is also a useful effect-size like statistic, representing the probability that a randomly selected individual from the 'x' distribution will have a higher value than a randomly selected individual from the 'y' distribution.",
      "topics": [
        "auc"
      ]
    },
    {
      "page": "benchmarks",
      "title": "Synthetic benchmark screening data",
      "topics": [
        "benchmarks"
      ]
    },
    {
      "page": "binned_es",
      "title": "Calculate binned effect sizes",
      "topics": [
        "binned_es"
      ]
    },
    {
      "page": "binned_plot",
      "title": "Quantile-binned effect size plot",
      "topics": [
        "binned_plot"
      ]
    },
    {
      "page": "coh_d",
      "title": "Compute Cohen's _d_",
      "topics": [
        "coh_d"
      ]
    },
    {
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      "title": "Empirical Cumulative Distribution Plot",
      "topics": [
        "ecdf_plot"
      ]
    },
    {
      "page": "hedg_g",
      "title": "Compute Hedges' _g_ This function calculates effect sizes in terms of Hedges' _g_, also called the corrected (for sample size) effect size. See 'coh_d' for the uncorrected version. Also see Lakens (2013) for a discussion on different types of effect sizes and their interpretation. Note that missing data are removed from the calculations of the means and standard deviations.",
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        "hedg_g"
      ]
    },
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      "title": "Compute the proportion above a specific cut location",
      "topics": [
        "pac"
      ]
    },
    {
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      "title": "Compute the difference in the proportion above a specific cut location",
      "topics": [
        "pac_compare"
      ]
    },
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      "title": "Produces the paired probability plot for two groups",
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        "pp_plot"
      ]
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      "topics": [
        "seda"
      ]
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      "title": "Data from the Tennessee class size experiment",
      "topics": [
        "star"
      ]
    },
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      "page": "tpac",
      "title": "Transformed proportion above the cut",
      "topics": [
        "tpac"
      ]
    },
    {
      "page": "tpac_compare",
      "title": "Compare Transformed Proportion Above the Cut",
      "topics": [
        "tpac_compare"
      ]
    },
    {
      "page": "v",
      "title": "Calculate the V effect size statistic",
      "topics": [
        "v"
      ]
    }
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