{
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  "Package": "BIGL",
  "Type": "Package",
  "Title": "Biochemically Intuitive Generalized Loewe Model",
  "Version": "1.8.0",
  "Date": "2023-06-13",
  "Author": "Heather Turner, Annelies Tourny, Olivier Thas, Maxim Nazarov,\nRytis Bagdziunas, Stijn Hawinkel, Javier Franco Pérez",
  "Maintainer": "Maxim Nazarov <maxim.nazarov@openanalytics.eu>",
  "Description": "Response surface methods for drug synergy analysis.\nAvailable methods include generalized and classical Loewe\nformulations as well as Highest Single Agent methodology.\nResponse surfaces can be plotted in an interactive 3-D plot and\nformal statistical tests for presence of synergistic effects\nare available. Implemented methods and tests are described in\nthe article \"BIGL: Biochemically Intuitive Generalized Loewe\nnull model for prediction of the expected combined effect\ncompatible with partial agonism and antagonism\" by Koen Van der\nBorght, Annelies Tourny, Rytis Bagdziunas, Olivier Thas, Maxim\nNazarov, Heather Turner, Bie Verbist & Hugo Ceulemans (2017)\n<doi:10.1038/s41598-017-18068-5>.",
  "License": "GPL-3",
  "VignetteBuilder": "knitr, rmarkdown",
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  "URL": "https://github.com/openanalytics/BIGL",
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  "Repository": "https://openanalytics.r-universe.dev",
  "Date/Publication": "2023-06-13 13:52:34 UTC",
  "RemoteUrl": "https://github.com/openanalytics/bigl",
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  "_published": "2026-06-02T18:32:32.045Z",
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    "message": "Merge branch 'release/1.8.0'",
    "time": 1686664354
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    "extra/contents.json",
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    "fitMarginals",
    "fitSurface",
    "generateData",
    "getTransformations",
    "initialMarginal",
    "isobologram",
    "optim.boxcox",
    "outsidePoints",
    "plotConfInt",
    "plotMeanVarFit",
    "plotResponseSurface",
    "predictOffAxis",
    "runBIGL",
    "simulateNull",
    "synergy_plot_bycomp"
  ],
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      "title": "Partial data with combination experiments of direct-acting antivirals",
      "object": "directAntivirals",
      "class": [
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      ],
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        "cpd1",
        "cpd2",
        "effect",
        "d1",
        "d2"
      ],
      "rows": 3520,
      "table": true,
      "tojson": true
    },
    {
      "name": "directAntivirals_ALL",
      "title": "Full data with combination experiments of direct-acting antivirals",
      "object": "directAntivirals_ALL",
      "class": [
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      ],
      "fields": [
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        "cpd1",
        "cpd2",
        "effect",
        "d1",
        "d2"
      ],
      "rows": 4224,
      "table": true,
      "tojson": true
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  "_help": [
    {
      "page": "addResids",
      "title": "Add residuals by adding to mean effects",
      "topics": [
        "addResids"
      ]
    },
    {
      "page": "backscaleResids",
      "title": "Backscale residuals",
      "topics": [
        "backscaleResids"
      ]
    },
    {
      "page": "Blissindependence",
      "title": "Bliss Independence Model",
      "topics": [
        "Blissindependence"
      ]
    },
    {
      "page": "bootConfInt",
      "title": "Obtain confidence intervals for the raw effect sizes on every off-axis point and overall",
      "topics": [
        "bootConfInt"
      ]
    },
    {
      "page": "boxcox.transformation",
      "title": "Apply two-parameter Box-Cox transformation",
      "topics": [
        "boxcox.transformation"
      ]
    },
    {
      "page": "coef.MarginalFit",
      "title": "Coefficients from marginal model estimation",
      "topics": [
        "coef.MarginalFit"
      ]
    },
    {
      "page": "col2hex",
      "title": "R color to RGB (red/green/blue) conversion.",
      "topics": [
        "col2hex"
      ]
    },
    {
      "page": "constructFormula",
      "title": "Construct a model formula from parameter constraint matrix",
      "topics": [
        "constructFormula"
      ]
    },
    {
      "page": "contour.ResponseSurface",
      "title": "Method for plotting of contours based on maxR statistics",
      "topics": [
        "contour.ResponseSurface"
      ]
    },
    {
      "page": "df.residual.MarginalFit",
      "title": "Residual degrees of freedom in marginal model estimation",
      "topics": [
        "df.residual.MarginalFit"
      ]
    },
    {
      "page": "directAntivirals",
      "title": "Partial data with combination experiments of direct-acting antivirals",
      "topics": [
        "directAntivirals"
      ]
    },
    {
      "page": "directAntivirals_ALL",
      "title": "Full data with combination experiments of direct-acting antivirals",
      "topics": [
        "directAntivirals_ALL"
      ]
    },
    {
      "page": "fitMarginals",
      "title": "Fit two 4-parameter log-logistic functions for a synergy experiment",
      "topics": [
        "fitMarginals"
      ]
    },
    {
      "page": "fitSurface",
      "title": "Fit response surface model and compute meanR and maxR statistics",
      "topics": [
        "fitSurface"
      ]
    },
    {
      "page": "fitted.MarginalFit",
      "title": "Compute fitted values from monotherapy estimation",
      "topics": [
        "fitted.MarginalFit"
      ]
    },
    {
      "page": "fitted.ResponseSurface",
      "title": "Predicted values of the response surface according to the given null model",
      "topics": [
        "fitted.ResponseSurface"
      ]
    },
    {
      "page": "generalizedLoewe",
      "title": "Compute combined predicted response from drug doses according to standard or generalized Loewe model.",
      "topics": [
        "generalizedLoewe"
      ]
    },
    {
      "page": "generateData",
      "title": "Generate data from parameters of marginal monotherapy model",
      "topics": [
        "generateData"
      ]
    },
    {
      "page": "get.abs_tval",
      "title": "Return absolute t-value, used in optimization call in 'optim.boxcox'",
      "topics": [
        "get.abs_tval"
      ]
    },
    {
      "page": "get.summ.data",
      "title": "Summarize data by factor",
      "topics": [
        "get.summ.data"
      ]
    },
    {
      "page": "getCP",
      "title": "Estimate CP matrix from bootstraps",
      "topics": [
        "getCP"
      ]
    },
    {
      "page": "getd1d2",
      "title": "A function to get the d1d2 identifier",
      "topics": [
        "getd1d2"
      ]
    },
    {
      "page": "getR",
      "title": "Helper functions for the test statistics",
      "topics": [
        "getR"
      ]
    },
    {
      "page": "GetStartGuess",
      "title": "Estimate initial values for dose-response curve fit",
      "topics": [
        "GetStartGuess"
      ]
    },
    {
      "page": "getTransformations",
      "title": "Return a list with transformation functions",
      "topics": [
        "getTransformations"
      ]
    },
    {
      "page": "harbronLoewe",
      "title": "Alternative Loewe generalization",
      "topics": [
        "harbronLoewe"
      ]
    },
    {
      "page": "hsa",
      "title": "Highest Single Agent model",
      "topics": [
        "hsa"
      ]
    },
    {
      "page": "initialMarginal",
      "title": "Estimate initial values for fitting marginal dose-response curves",
      "topics": [
        "initialMarginal"
      ]
    },
    {
      "page": "isobologram",
      "title": "Isobologram of the response surface predicted by the null model",
      "topics": [
        "isobologram"
      ]
    },
    {
      "page": "L4",
      "title": "4-parameter logistic dose-response function",
      "topics": [
        "L4"
      ]
    },
    {
      "page": "marginalNLS",
      "title": "Fit two 4-parameter log-logistic functions with non-linear least squares",
      "topics": [
        "marginalNLS"
      ]
    },
    {
      "page": "marginalOptim",
      "title": "Fit two 4-parameter log-logistic functions with common baseline",
      "topics": [
        "marginalOptim"
      ]
    },
    {
      "page": "maxR",
      "title": "Compute maxR statistic for each off-axis dose combination",
      "topics": [
        "maxR"
      ]
    },
    {
      "page": "meanR",
      "title": "Compute meanR statistic for the estimated model",
      "topics": [
        "meanR"
      ]
    },
    {
      "page": "modelVar",
      "title": "Calculate model variance, assuming variance increases linearly with mean",
      "topics": [
        "modelVar"
      ]
    },
    {
      "page": "optim.boxcox",
      "title": "Find optimal Box-Cox transformation parameters",
      "topics": [
        "optim.boxcox"
      ]
    },
    {
      "page": "outsidePoints",
      "title": "List non-additive points",
      "topics": [
        "outsidePoints"
      ]
    },
    {
      "page": "plot.BIGLconfInt",
      "title": "Plot confidence intervals in a contour plot",
      "topics": [
        "plot.BIGLconfInt"
      ]
    },
    {
      "page": "plot.effect-size",
      "title": "Plot of effect-size object",
      "topics": [
        "plot.effect-size"
      ]
    },
    {
      "page": "plot.MarginalFit",
      "title": "Plot monotherapy curve estimates",
      "topics": [
        "plot.MarginalFit"
      ]
    },
    {
      "page": "plot.maxR",
      "title": "Plot of maxR object",
      "topics": [
        "plot.maxR"
      ]
    },
    {
      "page": "plot.meanR",
      "title": "Plot bootstrapped cumulative distribution function of meanR null distribution",
      "topics": [
        "plot.meanR"
      ]
    },
    {
      "page": "plot.ResponseSurface",
      "title": "Method for plotting response surface objects",
      "topics": [
        "plot.ResponseSurface"
      ]
    },
    {
      "page": "plotConfInt",
      "title": "Plot confidence intervals from BIGL object in a contour plot",
      "topics": [
        "plotConfInt"
      ]
    },
    {
      "page": "plotMeanVarFit",
      "title": "Make a mean-variance plot",
      "topics": [
        "plotMeanVarFit"
      ]
    },
    {
      "page": "plotResponseSurface",
      "title": "Plot response surface",
      "topics": [
        "plotResponseSurface"
      ]
    },
    {
      "page": "predict.MarginalFit",
      "title": "Predict values on the dose-response curve",
      "topics": [
        "predict.MarginalFit"
      ]
    },
    {
      "page": "predictOffAxis",
      "title": "Compute off-axis predictions",
      "topics": [
        "predictOffAxis"
      ]
    },
    {
      "page": "predictResponseSurface",
      "title": "Predict the entire response surface, so including on-axis points, and return the result as a matrix. For plotting purposes.",
      "topics": [
        "predictResponseSurface"
      ]
    },
    {
      "page": "predictVar",
      "title": "Predict variance",
      "topics": [
        "predictVar"
      ]
    },
    {
      "page": "print.summary.BIGLconfInt",
      "title": "Print summary of BIGLconfInt object",
      "topics": [
        "print.summary.BIGLconfInt"
      ]
    },
    {
      "page": "print.summary.MarginalFit",
      "title": "Print method for summary of 'MarginalFit' object",
      "topics": [
        "print.summary.MarginalFit"
      ]
    },
    {
      "page": "print.summary.maxR",
      "title": "Print summary of maxR object",
      "topics": [
        "print.summary.maxR"
      ]
    },
    {
      "page": "print.summary.meanR",
      "title": "Print summary of meanR object",
      "topics": [
        "print.summary.meanR"
      ]
    },
    {
      "page": "print.summary.ResponseSurface",
      "title": "Print method for the summary function of 'ResponseSurface' object",
      "topics": [
        "print.summary.ResponseSurface"
      ]
    },
    {
      "page": "residuals.MarginalFit",
      "title": "Residuals from marginal model estimation",
      "topics": [
        "residuals.MarginalFit"
      ]
    },
    {
      "page": "runBIGL",
      "title": "Run the BIGL application for demonstrating response surfaces",
      "topics": [
        "runBIGL"
      ]
    },
    {
      "page": "sampleResids",
      "title": "Sample residuals according to a new model",
      "topics": [
        "sampleResids"
      ]
    },
    {
      "page": "scaleResids",
      "title": "Functions for scaling, and rescaling residuals. May lead to unstable behaviour in practice",
      "topics": [
        "scaleResids"
      ]
    },
    {
      "page": "simulateNull",
      "title": "Simulate data from a given null model and monotherapy coefficients",
      "topics": [
        "simulateNull"
      ]
    },
    {
      "page": "summary.BIGLconfInt",
      "title": "Summary of confidence intervals object",
      "topics": [
        "summary.BIGLconfInt"
      ]
    },
    {
      "page": "summary.MarginalFit",
      "title": "Summary of 'MarginalFit' object",
      "topics": [
        "summary.MarginalFit"
      ]
    },
    {
      "page": "summary.maxR",
      "title": "Summary of maxR object",
      "topics": [
        "summary.maxR"
      ]
    },
    {
      "page": "summary.meanR",
      "title": "Summary of meanR object",
      "topics": [
        "summary.meanR"
      ]
    },
    {
      "page": "summary.ResponseSurface",
      "title": "Summary of 'ResponseSurface' object",
      "topics": [
        "summary.ResponseSurface"
      ]
    },
    {
      "page": "synergy_plot_bycomp",
      "title": "Plot 2D cross section of response surface",
      "topics": [
        "synergy_plot_bycomp"
      ]
    },
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