{
  "_id": "6ac8a641de46216d5edd5140",
  "Package": "WebPower",
  "Title": "Basic and Advanced Statistical Power Analysis",
  "Version": "0.9.7",
  "Date": "2024-08-20",
  "Authors@R": "c(person(\"Zhiyong\", \"Zhang\", role = c(\"aut\", \"cre\"),\nemail = \"johnnyzhz@gmail.com\"),\nperson(\"Yujiao\", \"Mai\", role = \"aut\"),\nperson(\"Miao\", \"Yang\", role = \"ctb\",\nemail = \"Miao.Yang.100@nd.edu\"),\nperson(\"Ziqian\", \"Xu\", role = \"ctb\"),\nperson(\"Conor\", \"McNamara\", role = \"ctb\"))",
  "Author": "Zhiyong Zhang [aut, cre], Yujiao Mai [aut], Miao Yang [ctb],\nZiqian Xu [ctb], Conor McNamara [ctb]",
  "Maintainer": "Zhiyong Zhang <johnnyzhz@gmail.com>",
  "License": "GPL (>= 3)",
  "Description": "This is a collection of tools for conducting both basic\nand advanced statistical power analysis including correlation,\nproportion, t-test, one-way ANOVA, two-way ANOVA, linear\nregression, logistic regression, Poisson regression, mediation\nanalysis, longitudinal data analysis, structural equation\nmodeling and multilevel modeling. It also serves as the engine\nfor conducting power analysis online at\n<https://webpower.psychstat.org>.",
  "URL": "https://webpower.psychstat.org",
  "Encoding": "UTF-8",
  "LazyLoad": "yes",
  "LazyData": "yes",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-10-09 08:23:05 UTC",
    "User": "root"
  },
  "RoxygenNote": "7.2.2",
  "Config/pak/sysreqs": "cmake make",
  "Repository": "https://johnnyzhz.r-universe.dev",
  "Date/Publication": "2024-09-10 20:12:17 UTC",
  "RemoteUrl": "https://github.com/johnnyzhz/webpower",
  "RemoteRef": "HEAD",
  "RemoteSha": "4a3589680c680f4df7ee05a31ff45a1f8ca11dfd",
  "_user": "johnnyzhz",
  "_type": "src",
  "_file": "WebPower_0.9.7.tar.gz",
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  "_filesize": 337576,
  "_sha256": "151cd6edbdf46e1ee211d9fecf9981b5a947a537220b2f7292fbcaac279ff52a",
  "_expires": "2027-01-17T08:30:55.000Z",
  "_created": "2026-10-09T08:23:05.000Z",
  "_published": "2026-10-09T08:30:56.978Z",
  "_buildurl": "https://github.com/r-universe/johnnyzhz/actions/runs/37904354157",
  "_status": "success",
  "_host": "GitHub-Actions",
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  "_commit": {
    "id": "4a3589680c680f4df7ee05a31ff45a1f8ca11dfd",
    "author": "Ziqian Xu <38840988+iasnobmatsu@users.noreply.github.com>",
    "committer": "Ziqian Xu <38840988+iasnobmatsu@users.noreply.github.com>",
    "message": "add index of moderated mediation\n",
    "time": 1725999137
  },
  "_maintainer": {
    "name": "Zhiyong Zhang",
    "email": "johnnyzhz@gmail.com",
    "login": "johnnyzhz",
    "description": "Zhang is Professor of Quantitative Psychology and Director of Lab for Big Data Methodology at University of Notre Dame.\n",
    "uuid": 4248963
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  "_distro": "resolute",
  "_registered": true,
  "_dependencies": [
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      "version": ">= 3.2.5",
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      "role": "Depends"
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    {
      "package": "grDevices",
      "role": "Depends"
    },
    {
      "package": "graphics",
      "role": "Depends"
    },
    {
      "package": "lme4",
      "role": "Depends"
    },
    {
      "package": "lavaan",
      "role": "Depends"
    },
    {
      "package": "parallel",
      "role": "Depends"
    },
    {
      "package": "PearsonDS",
      "role": "Depends"
    },
    {
      "package": "dplyr",
      "role": "Imports"
    },
    {
      "package": "stats",
      "role": "Imports"
    }
  ],
  "_owner": "johnnyzhz",
  "_selfowned": true,
  "_usedby": 1,
  "_updates": [],
  "_tags": [],
  "_stars": 7,
  "_contributors": [
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      "count": 33,
      "uuid": 4248963
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  "_userbio": {
    "uuid": 4248963,
    "type": "user",
    "name": "Zhiyong Johnny Zhang",
    "followers": 14,
    "description": "Zhang is Professor of Quantitative Psychology and Director of Lab for Big Data Methodology at University of Notre Dame.\r\n"
  },
  "_downloads": {
    "count": 1664,
    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/WebPower"
  },
  "_mentions": 17,
  "_devurl": "https://github.com/johnnyzhz/webpower",
  "_searchresults": 212,
  "_rbuild": "4.6.1",
  "_assets": [
    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/manual.pdf",
    "extra/WebPower.html"
  ],
  "_homeurl": "https://github.com/johnnyzhz/webpower",
  "_realowner": "johnnyzhz",
  "_cranurl": true,
  "_releases": [
    {
      "version": "0.5",
      "date": "2018-04-16"
    },
    {
      "version": "0.5.2",
      "date": "2018-11-29"
    },
    {
      "version": "0.6",
      "date": "2021-05-18"
    },
    {
      "version": "0.7",
      "date": "2022-05-21"
    },
    {
      "version": "0.8.6",
      "date": "2022-08-15"
    },
    {
      "version": "0.8.7",
      "date": "2023-01-14"
    },
    {
      "version": "0.9.0",
      "date": "2023-04-20"
    },
    {
      "version": "0.9.2",
      "date": "2023-05-10"
    },
    {
      "version": "0.9.3",
      "date": "2023-05-18"
    },
    {
      "version": "0.9.4",
      "date": "2023-10-14"
    }
  ],
  "_exports": [
    "estCRT2arm",
    "estCRT3arm",
    "estMRT2arm",
    "estMRT3arm",
    "nuniroot",
    "sem.effect.size",
    "wp.anova",
    "wp.anova.binary",
    "wp.anova.count",
    "wp.blcsm",
    "wp.correlation",
    "wp.crt2arm",
    "wp.crt3arm",
    "wp.effect.CRT2arm",
    "wp.effect.CRT3arm",
    "wp.effect.MRT2arm",
    "wp.effect.MRT3arm",
    "wp.kanova",
    "wp.lcsm",
    "wp.logistic",
    "wp.mc.chisq.diff",
    "wp.mc.sem.basic",
    "wp.mc.sem.boot",
    "wp.mc.sem.power.curve",
    "wp.mc.t",
    "wp.mediation",
    "wp.mmrm",
    "wp.mmrm.ar1",
    "wp.modmed.m14",
    "wp.modmed.m15",
    "wp.modmed.m58",
    "wp.modmed.m7",
    "wp.modmed.m8",
    "wp.mrt2arm",
    "wp.mrt3arm",
    "wp.poisson",
    "wp.popPar",
    "wp.prop",
    "wp.regression",
    "wp.rmanova",
    "wp.sem.chisq",
    "wp.sem.rmsea",
    "wp.t"
  ],
  "_datasets": [
    {
      "name": "CRT2",
      "title": "Example Data For CRT With 2 Arms",
      "object": "webpower",
      "class": [
        "data.frame"
      ],
      "fields": [
        "ID",
        "cluster",
        "score",
        "group"
      ],
      "rows": 8,
      "table": true,
      "tojson": true
    },
    {
      "name": "CRT3",
      "title": "Example Data For CRT With 3 Arms",
      "object": "webpower",
      "class": [
        "data.frame"
      ],
      "fields": [
        "id",
        "cluster",
        "score",
        "group"
      ],
      "rows": 30,
      "table": true,
      "tojson": true
    },
    {
      "name": "MRT2",
      "title": "Example Data For MRT With 2 Arms",
      "object": "webpower",
      "class": [
        "data.frame"
      ],
      "fields": [
        "id",
        "cluster",
        "score",
        "group"
      ],
      "rows": 16,
      "table": true,
      "tojson": true
    },
    {
      "name": "MRT3",
      "title": "Example Data For MRT With 3 Arms",
      "object": "webpower",
      "class": [
        "data.frame"
      ],
      "fields": [
        "id",
        "cluster",
        "score",
        "group"
      ],
      "rows": 24,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "WebPower-package",
      "title": "Basic and Advanced Statistical Power Analysis",
      "topics": [
        "WebPower-package",
        "WebPower"
      ]
    },
    {
      "page": "CRT2",
      "title": "Example Data For CRT With 2 Arms",
      "topics": [
        "CRT2"
      ]
    },
    {
      "page": "CRT3",
      "title": "Example Data For CRT With 3 Arms",
      "topics": [
        "CRT3"
      ]
    },
    {
      "page": "estCRT2arm",
      "title": "Estimate multilevel effect size from data",
      "topics": [
        "estCRT2arm",
        "estCRT3arm",
        "estMRT2arm",
        "estMRT3arm"
      ]
    },
    {
      "page": "MRT2",
      "title": "Example Data For MRT With 2 Arms",
      "topics": [
        "MRT2"
      ]
    },
    {
      "page": "MRT3",
      "title": "Example Data For MRT With 3 Arms",
      "topics": [
        "MRT3"
      ]
    },
    {
      "page": "nuniroot",
      "title": "Solve A Single Equation",
      "topics": [
        "nuniroot"
      ]
    },
    {
      "page": "plot.lcs.power",
      "title": "Plot the power curve for Latent Change Score Models",
      "topics": [
        "plot.lcs.power"
      ]
    },
    {
      "page": "plot.webpower",
      "title": "To plot Statistical Power Curve",
      "topics": [
        "plot.webpower"
      ]
    },
    {
      "page": "print.webpower",
      "title": "To Print Statistical Power Analysis Results",
      "topics": [
        "print.webpower"
      ]
    },
    {
      "page": "sem.effect.size",
      "title": "Calculate the Effect Size for SEM",
      "topics": [
        "sem.effect.size"
      ]
    },
    {
      "page": "summary.power",
      "title": "Summary Statistical Power Analysis Results",
      "topics": [
        "summary.power"
      ]
    },
    {
      "page": "wp.anova",
      "title": "Statistical Power Analysis for One-way ANOVA",
      "topics": [
        "wp.anova"
      ]
    },
    {
      "page": "wp.anova.binary",
      "title": "Statistical Power Analysis for One-way ANOVA with Binary Data",
      "topics": [
        "wp.anova.binary"
      ]
    },
    {
      "page": "wp.anova.count",
      "title": "Statistical Power Analysis for One-way ANOVA with Count Data",
      "topics": [
        "wp.anova.count"
      ]
    },
    {
      "page": "wp.blcsm",
      "title": "Statistical Power Curve for Bivariate Latent Change Score Models based on Monte Carlo Simulation",
      "topics": [
        "wp.blcsm"
      ]
    },
    {
      "page": "wp.correlation",
      "title": "Statistical Power Analysis for Correlation",
      "topics": [
        "wp.correlation"
      ]
    },
    {
      "page": "wp.crt2arm",
      "title": "Statistical Power Analysis for Cluster Randomized Trials with 2 Arms",
      "topics": [
        "wp.crt2arm"
      ]
    },
    {
      "page": "wp.crt3arm",
      "title": "Statistical Power Analysis for Cluster Randomized Trials with 3 Arms",
      "topics": [
        "wp.crt3arm"
      ]
    },
    {
      "page": "wp.effect.CRT2arm",
      "title": "Effect size calculatator based on raw data for Cluster Randomized Trials with 2 Arms",
      "topics": [
        "wp.effect.CRT2arm"
      ]
    },
    {
      "page": "wp.effect.CRT3arm",
      "title": "Effect size calculatator based on raw data for Cluster Randomized Trials with 3 Arms",
      "topics": [
        "wp.effect.CRT3arm"
      ]
    },
    {
      "page": "wp.effect.MRT2arm",
      "title": "Effect size calculatator based on raw data for Multisite Randomized Trials with 2 Arms",
      "topics": [
        "wp.effect.MRT2arm"
      ]
    },
    {
      "page": "wp.effect.MRT3arm",
      "title": "Effect size calculatator based on raw data for Multisite Randomized Trials with 3 Arms",
      "topics": [
        "wp.effect.MRT3arm"
      ]
    },
    {
      "page": "wp.kanova",
      "title": "Power analysis for two-way, three-way and k-way ANOVA",
      "topics": [
        "wp.kanova"
      ]
    },
    {
      "page": "wp.lcsm",
      "title": "Statistical Power Curve for Univariate Latent Change Score Models based on Monte Carlo Simulation",
      "topics": [
        "wp.lcsm"
      ]
    },
    {
      "page": "wp.logistic",
      "title": "Statistical Power Analysis for Logistic Regression",
      "topics": [
        "wp.logistic"
      ]
    },
    {
      "page": "wp.mc.chisq.diff",
      "title": "Statistical Power Analysis for SEM Based on Chi-square Difference Test",
      "topics": [
        "wp.mc.chisq.diff"
      ]
    },
    {
      "page": "wp.mc.sem.basic",
      "title": "Statistical Power Analysis for Structural Equation Modeling / Mediation based on Monte Carlo Simulation",
      "topics": [
        "wp.mc.sem.basic"
      ]
    },
    {
      "page": "wp.mc.sem.boot",
      "title": "Statistical Power Analysis for Structural Equation Modeling / Mediation based on Monte Carlo Simulation: bootstrap method",
      "topics": [
        "wp.mc.sem.boot"
      ]
    },
    {
      "page": "wp.mc.sem.power.curve",
      "title": "Statistical Power Curve for Structural Equation Modeling / Mediation based on Monte Carlo Simulation",
      "topics": [
        "wp.mc.sem.power.curve"
      ]
    },
    {
      "page": "wp.mc.t",
      "title": "Power analysis for t-test based on Monte Carlo simulation",
      "topics": [
        "wp.mc.t"
      ]
    },
    {
      "page": "wp.mediation",
      "title": "Statistical Power Analysis for Simple Mediation",
      "topics": [
        "wp.mediation"
      ]
    },
    {
      "page": "wp.mmrm",
      "title": "Power analysis for longitudinal data analysis",
      "topics": [
        "wp.mmrm",
        "wp.mmrm.ar1"
      ]
    },
    {
      "page": "wp.modmed.m14",
      "title": "model14",
      "topics": [
        "wp.modmed.m14"
      ]
    },
    {
      "page": "wp.modmed.m15",
      "title": "model15",
      "topics": [
        "wp.modmed.m15"
      ]
    },
    {
      "page": "wp.modmed.m58",
      "title": "model58",
      "topics": [
        "wp.modmed.m58"
      ]
    },
    {
      "page": "wp.modmed.m7",
      "title": "model7",
      "topics": [
        "wp.modmed.m7"
      ]
    },
    {
      "page": "wp.modmed.m8",
      "title": "model8",
      "topics": [
        "wp.modmed.m8"
      ]
    },
    {
      "page": "wp.mrt2arm",
      "title": "Statistical Power Analysis for Multisite Randomized Trials with 2 Arms",
      "topics": [
        "wp.mrt2arm"
      ]
    },
    {
      "page": "wp.mrt3arm",
      "title": "Statistical Power Analysis for Multisite Randomized Trials with 3 Arms",
      "topics": [
        "wp.mrt3arm"
      ]
    },
    {
      "page": "wp.poisson",
      "title": "Statistical Power Analysis for Poisson Regression",
      "topics": [
        "wp.poisson"
      ]
    },
    {
      "page": "wp.popPar",
      "title": "Extract Population Value Table",
      "topics": [
        "wp.popPar"
      ]
    },
    {
      "page": "wp.prop",
      "title": "Statistical Power Analysis for Tests of Proportions",
      "topics": [
        "wp.prop"
      ]
    },
    {
      "page": "wp.regression",
      "title": "Statistical Power Analysis for Linear Regression",
      "topics": [
        "wp.regression"
      ]
    },
    {
      "page": "wp.rmanova",
      "title": "Statistical Power Analysis for Repeated Measures ANOVA",
      "topics": [
        "wp.rmanova"
      ]
    },
    {
      "page": "wp.sem.chisq",
      "title": "Statistical Power Analysis for Structural Equation Modeling based on Chi-Squared Test",
      "topics": [
        "wp.sem.chisq"
      ]
    },
    {
      "page": "wp.sem.rmsea",
      "title": "Statistical Power Analysis for Structural Equation Modeling based on RMSEA",
      "topics": [
        "wp.sem.rmsea"
      ]
    },
    {
      "page": "wp.t",
      "title": "Statistical Power Analysis for t-Tests",
      "topics": [
        "wp.t"
      ]
    }
  ],
  "_rundeps": [
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    "cli",
    "dplyr",
    "generics",
    "glue",
    "lattice",
    "lavaan",
    "lifecycle",
    "lme4",
    "magrittr",
    "MASS",
    "Matrix",
    "minqa",
    "mnormt",
    "nlme",
    "nloptr",
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    "pbivnorm",
    "PearsonDS",
    "pillar",
    "pkgconfig",
    "quadprog",
    "R6",
    "rbibutils",
    "Rcpp",
    "RcppEigen",
    "Rdpack",
    "reformulas",
    "rlang",
    "tibble",
    "tidyselect",
    "utf8",
    "vctrs",
    "withr"
  ],
  "_score": 6.170738473281357,
  "_indexed": true,
  "_nocasepkg": "webpower",
  "_universes": [
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