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  "Package": "RsqMed",
  "Title": "Total Mediation Effect Size Measure for High-Dimensional\nMediators",
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  "Authors@R": "c(\nperson(given = \"Tianzhong\",\nfamily = \"Yang\",\nrole = c(\"aut\", \"cre\"),\nemail = \"yang3704@umn.edu\"),\nperson(given = \"Chunlin\",\nfamily = \"Li\",\nrole = \"aut\",\nemail = \"chunlin@iastate.edu\"),\nperson(given = \"Zhichao\",\nfamily =  \"Xu\",\nrole = \"ctb\",\nemail = \"ZXu7@mdanderson.org\"))",
  "Description": "An implementation of calculating the R-squared measure as\na total mediation effect size measure and its confidence\ninterval for moderate- or high-dimensional mediator models. It\ngives an option to filter out non-mediators using variable\nselection methods. The original R package is directly related\nto the paper Yang et al (2021) \"Estimation of mediation effect\nfor high-dimensional omics mediators with application to the\nFramingham Heart Study\" <doi:10.1101/774877>. The new version\ncontains a choice of using cross-fitting, which is\ncomputationally faster. The details of the cross-fitting method\nare available in the paper Xu et al (2023) \"Speeding up\ninterval estimation for R2-based mediation effect of\nhigh-dimensional mediators via cross-fitting\"\n<doi:10.1101/2023.02.06.527391>.",
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  "Author": "Tianzhong Yang [aut, cre], Chunlin Li [aut], Zhichao Xu [ctb]",
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    {
      "page": "CF_Rsq.measure",
      "title": "Function to calculate the Rsq function as a total effect size measure for mediation effect using cross-fitted estimation",
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        "CF_Rsq.measure"
      ]
    },
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      "title": "Functions to generate the confidence interval of the Rsq measure using nonparametric bootstrap.",
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      ]
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      "title": "Function to calculate the Rsq function as a total mediation effect size measure (Gaussian outcome only). If method = 'iSIS', a two-step procedure is performed, where the first step filters the non-mediators based on the first p proportion of the data and the second step calculates the point estimates for Rsq using random-effect models on the remaining data. If method = 'ALL', Rsq is calculated based on all subjects and mediators (assuming all mediators are the true mediators). It is optional to adding filtering step on putative mediators to exclude M1 type of non-mediators (See Yang et al, BMC bioinformatics, 2021).",
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