{
  "_id": "6a229733cd65a98ecbd56ac8",
  "Package": "adestr",
  "Type": "Package",
  "Title": "Estimation in Optimal Adaptive Two-Stage Designs",
  "Version": "1.0.0",
  "Authors@R": "c(person(\"Jan\", \"Meis\", role = c(\"aut\", \"cre\"), email = \"meis@imbi.uni-heidelberg.de\", comment = c(ORCID = \"0000-0001-5407-7220\")),\nperson(\"Martin\", \"Maechler\", role = c(\"cph\"), email = \"maechler@stat.math.ethz.ch\", comment = c(ORCID = \"0000-0002-8685-9910\", \"Original author of monoSpl.c (from the 'stats' package).\")))",
  "Description": "Methods to evaluate the performance characteristics of\nvarious point and interval estimators for optimal adaptive\ntwo-stage designs as described in Meis et al. (2024)\n<doi:10.1002/sim.10020>. Specifically, this package is written\nto work with trial designs created by the 'adoptr' package\n(Kunzmann et al. (2021) <doi:10.18637/jss.v098.i09>; Pilz et\nal. (2021) <doi:10.1002/sim.8953>)). Apart from the a priori\nevaluation of performance characteristics, this package also\nallows for the evaluation of the implemented estimators on real\ndatasets, and it implements methods to calculate p-values.",
  "License": "GPL (>= 2)",
  "Copyright": "This package contains a modified version of the monotonic\nspline functions from the 'stats' package. Specifically, the\ncode is containted in the files 'R/fastmonoHFC.R',\n'src/fastmonoHFC.c', 'src/modreg.h' and 'src/monoSpl.c'. The R\nCore team and Martin Maechler are the copyright holders of the\noriginal code. Jan Meis is the copyright holder of everything\nelse.",
  "Encoding": "UTF-8",
  "VignetteBuilder": "knitr",
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  "Collate": "'adestr_package.R' 'twostagedesign_with_cache.R' 'analyze.R'\n'estimators.R' 'densities.R' 'evaluate_estimator.R'\n'fastmonoHFC.R' 'fisher_information.R' 'hcubature.R'\n'helper_functions.R' 'integrate_over_sample_space.R'\n'reference_implementation.R' 'mle_distribution.R'\n'mlmse_score.R' 'n2c2_helpers.R' 'plot.R' 'priors.R' 'print.R'",
  "URL": "https://jan-imbi.github.io/adestr/",
  "RdMacros": "Rdpack",
  "Config/pak/sysreqs": "cmake make libicu-dev",
  "Repository": "https://jan-imbi.r-universe.dev",
  "Date/Publication": "2024-07-12 13:12:12 UTC",
  "RemoteUrl": "https://github.com/jan-imbi/adestr",
  "RemoteRef": "HEAD",
  "RemoteSha": "0df8d7d16b5cb0af6fa8d32abd22432d541b8f00",
  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-06-05 09:21:53 UTC",
    "User": "root"
  },
  "Author": "Jan Meis [aut, cre] (ORCID: <https://orcid.org/0000-0001-5407-7220>),\nMartin Maechler [cph] (ORCID: <https://orcid.org/0000-0002-8685-9910>,\nOriginal author of monoSpl.c (from the 'stats' package).)",
  "Maintainer": "Jan Meis <meis@imbi.uni-heidelberg.de>",
  "MD5sum": "c6202b471465419a311a50eecea0fb35",
  "_user": "jan-imbi",
  "_type": "src",
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  "_created": "2026-06-05T09:21:53.000Z",
  "_published": "2026-06-05T09:30:27.161Z",
  "_distro": "noble",
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    "message": "Fixing broken \\link{} in Rd again\n",
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  "_owner": "jan-imbi",
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  "_usedby": 0,
  "_updates": [],
  "_tags": [],
  "_topics": [
    "adaptive",
    "adoptr",
    "confidence",
    "designs",
    "estimation",
    "intervals",
    "optimal",
    "parameter",
    "point",
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    "AdaptivelyWeightedSampleMean",
    "analyze",
    "Bias",
    "BiasReduced",
    "Centrality",
    "Coverage",
    "evaluate_estimator",
    "evaluate_scenarios_parallel",
    "Expectation",
    "FirstStageSampleMean",
    "get_example_design",
    "get_example_statistics",
    "get_stagewise_estimators",
    "IntervalEstimator",
    "LikelihoodRatioOrderingCI",
    "LikelihoodRatioOrderingPValue",
    "LinearShiftRepeatedPValue",
    "MedianUnbiasedLikelihoodRatioOrdering",
    "MedianUnbiasedMLEOrdering",
    "MedianUnbiasedNeymanPearsonOrdering",
    "MedianUnbiasedScoreTestOrdering",
    "MedianUnbiasedStagewiseCombinationFunctionOrdering",
    "MidpointLikelihoodRatioOrderingCI",
    "MidpointMLEOrderingCI",
    "MidpointNeymanPearsonOrderingCI",
    "MidpointScoreTestOrderingCI",
    "MidpointStagewiseCombinationFunctionOrderingCI",
    "MinimizePeakVariance",
    "MLEOrderingCI",
    "MLEOrderingPValue",
    "MSE",
    "NaiveCI",
    "NaivePValue",
    "NeymanPearsonOrderingCI",
    "NeymanPearsonOrderingPValue",
    "NormalPrior",
    "OverestimationProbability",
    "plot",
    "plot_p",
    "PointEstimator",
    "PseudoRaoBlackwell",
    "PValue",
    "RaoBlackwell",
    "RepeatedCI",
    "SampleMean",
    "ScoreTestOrderingCI",
    "ScoreTestOrderingPValue",
    "SoftCoverage",
    "StagewiseCombinationFunctionOrderingCI",
    "StagewiseCombinationFunctionOrderingPValue",
    "TestAgreement",
    "UniformPrior",
    "Variance",
    "WeightedSampleMean",
    "Width"
  ],
  "_help": [
    {
      "page": "adestr",
      "title": "adestr",
      "topics": [
        "adestr-package",
        "adestr"
      ]
    },
    {
      "page": "analyze",
      "title": "Analyze a dataset",
      "topics": [
        "analyze",
        "analyze,data.frame-method"
      ]
    },
    {
      "page": "c-EstimatorScoreResult-method",
      "title": "Combine EstimatoreScoreResult objects into a list",
      "topics": [
        "c,EstimatorScoreResult-method"
      ]
    },
    {
      "page": "c-EstimatorScoreResultList-method",
      "title": "Combine EstimatoreScoreResult objects into a list",
      "topics": [
        "c,EstimatorScoreResultList-method"
      ]
    },
    {
      "page": "c2_extrapol",
      "title": "Calculate the second-stage critical value for a design with cached spline parameters",
      "topics": [
        "c2_extrapol"
      ]
    },
    {
      "page": "EstimatorScore-class",
      "title": "Performance scores for point and interval estimators",
      "topics": [
        "Bias",
        "Centrality",
        "Coverage",
        "EstimatorScore",
        "EstimatorScore-class",
        "Expectation",
        "MSE",
        "OverestimationProbability",
        "SoftCoverage",
        "TestAgreement",
        "Variance",
        "Width"
      ]
    },
    {
      "page": "evaluate_estimator",
      "title": "Evaluate performance characteristics of an estimator",
      "topics": [
        "evaluate_estimator"
      ]
    },
    {
      "page": "evaluate_estimator-methods",
      "title": "Evaluate performance characteristics of an estimator",
      "topics": [
        "evaluate_estimator,Bias,PointEstimator-method",
        "evaluate_estimator,Centrality,PointEstimator-method",
        "evaluate_estimator,Coverage,IntervalEstimator-method",
        "evaluate_estimator,Expectation,PointEstimator-method",
        "evaluate_estimator,IntervalEstimatorScore,PointEstimator-method",
        "evaluate_estimator,list,Estimator-method",
        "evaluate_estimator,MSE,PointEstimator-method",
        "evaluate_estimator,OverestimationProbability,PointEstimator-method",
        "evaluate_estimator,PointEstimatorScore,IntervalEstimator-method",
        "evaluate_estimator,SoftCoverage,IntervalEstimator-method",
        "evaluate_estimator,TestAgreement,IntervalEstimator-method",
        "evaluate_estimator,TestAgreement,PValue-method",
        "evaluate_estimator,Variance,PointEstimator-method",
        "evaluate_estimator,Width,IntervalEstimator-method",
        "evaluate_estimator-methods"
      ]
    },
    {
      "page": "evaluate_scenarios_parallel",
      "title": "Evaluate different scenarios in parallel",
      "topics": [
        "evaluate_scenarios_parallel"
      ]
    },
    {
      "page": "get_example_design",
      "title": "Generate an exemplary adaptive design",
      "topics": [
        "get_example_design"
      ]
    },
    {
      "page": "get_example_statistics",
      "title": "Generate a list of estimators and p-values to use in examples",
      "topics": [
        "get_example_statistics"
      ]
    },
    {
      "page": "get_stagewise_estimators",
      "title": "Conditional representations of an estimator or p-value",
      "topics": [
        "get_stagewise_estimators",
        "get_stagewise_estimators,AdaptivelyWeightedSampleMean,Normal-method",
        "get_stagewise_estimators,BiasReduced,Normal-method",
        "get_stagewise_estimators,IntervalEstimator,DataDistribution-method",
        "get_stagewise_estimators,IntervalEstimator,Student-method",
        "get_stagewise_estimators,LikelihoodRatioOrderingCI,Normal-method",
        "get_stagewise_estimators,LikelihoodRatioOrderingPValue,Normal-method",
        "get_stagewise_estimators,LinearShiftRepeatedPValue,Normal-method",
        "get_stagewise_estimators,MedianUnbiasedLikelihoodRatioOrdering,Normal-method",
        "get_stagewise_estimators,MedianUnbiasedMLEOrdering,Normal-method",
        "get_stagewise_estimators,MedianUnbiasedNeymanPearsonOrdering,Normal-method",
        "get_stagewise_estimators,MedianUnbiasedScoreTestOrdering,Normal-method",
        "get_stagewise_estimators,MedianUnbiasedStagewiseCombinationFunctionOrdering,Normal-method",
        "get_stagewise_estimators,MidpointLikelihoodRatioOrderingCI,Normal-method",
        "get_stagewise_estimators,MidpointMLEOrderingCI,Normal-method",
        "get_stagewise_estimators,MidpointNeymanPearsonOrderingCI,Normal-method",
        "get_stagewise_estimators,MidpointScoreTestOrderingCI,Normal-method",
        "get_stagewise_estimators,MidpointStagewiseCombinationFunctionOrderingCI,Normal-method",
        "get_stagewise_estimators,MinimizePeakVariance,Normal-method",
        "get_stagewise_estimators,MLEOrderingCI,Normal-method",
        "get_stagewise_estimators,MLEOrderingPValue,Normal-method",
        "get_stagewise_estimators,NaiveCI,Normal-method",
        "get_stagewise_estimators,NaivePValue,Normal-method",
        "get_stagewise_estimators,NeymanPearsonOrderingCI,Normal-method",
        "get_stagewise_estimators,NeymanPearsonOrderingPValue,Normal-method",
        "get_stagewise_estimators,PointEstimator,DataDistribution-method",
        "get_stagewise_estimators,PointEstimator,Student-method",
        "get_stagewise_estimators,PseudoRaoBlackwell,Normal-method",
        "get_stagewise_estimators,PValue,DataDistribution-method",
        "get_stagewise_estimators,PValue,Student-method",
        "get_stagewise_estimators,RaoBlackwell,Normal-method",
        "get_stagewise_estimators,RepeatedCI,Normal-method",
        "get_stagewise_estimators,ScoreTestOrderingCI,Normal-method",
        "get_stagewise_estimators,ScoreTestOrderingPValue,Normal-method",
        "get_stagewise_estimators,StagewiseCombinationFunctionOrderingCI,Normal-method",
        "get_stagewise_estimators,StagewiseCombinationFunctionOrderingPValue,Normal-method",
        "get_stagewise_estimators,VirtualIntervalEstimator,ANY-method",
        "get_stagewise_estimators,VirtualIntervalEstimator,Student-method",
        "get_stagewise_estimators,VirtualPointEstimator,ANY-method",
        "get_stagewise_estimators,VirtualPointEstimator,Student-method",
        "get_stagewise_estimators,VirtualPValue,ANY-method",
        "get_stagewise_estimators,VirtualPValue,Student-method"
      ]
    },
    {
      "page": "get_statistics_from_paper",
      "title": "Generate the list of estimators and p-values that were used in the paper",
      "topics": [
        "get_statistics_from_paper"
      ]
    },
    {
      "page": "IntervalEstimator-class",
      "title": "Interval estimators",
      "topics": [
        "ConfidenceInterval",
        "ConfidenceInterval-class",
        "IntervalEstimator",
        "IntervalEstimator-class",
        "LikelihoodRatioOrderingCI",
        "MLEOrderingCI",
        "NaiveCI",
        "NeymanPearsonOrderingCI",
        "RepeatedCI",
        "ScoreTestOrderingCI",
        "StagewiseCombinationFunctionOrderingCI"
      ]
    },
    {
      "page": "n2_extrapol",
      "title": "Calculate the second-stage sample size for a design with cached spline parameters",
      "topics": [
        "n2_extrapol"
      ]
    },
    {
      "page": "NormalPrior",
      "title": "Normal prior distribution for the parameter mu",
      "topics": [
        "NormalPrior"
      ]
    },
    {
      "page": "plot_p",
      "title": "Plot p-values and implied rejection boundaries",
      "topics": [
        "plot_p"
      ]
    },
    {
      "page": "plot-EstimatorScoreResult-method",
      "title": "Plot performance scores for point and interval estimators",
      "topics": [
        "plot,EstimatorScoreResult-method"
      ]
    },
    {
      "page": "plot-EstimatorScoreResultList-method",
      "title": "Plot performance scores for point and interval estimators",
      "topics": [
        "plot,EstimatorScoreResultList-method"
      ]
    },
    {
      "page": "plot-list-method",
      "title": "Plot performance scores for point and interval estimators",
      "topics": [
        "plot,list-method"
      ]
    },
    {
      "page": "PointEstimator-class",
      "title": "Point estimators",
      "topics": [
        "AdaptivelyWeightedSampleMean",
        "BiasReduced",
        "FirstStageSampleMean",
        "MedianUnbiasedLikelihoodRatioOrdering",
        "MedianUnbiasedMLEOrdering",
        "MedianUnbiasedNeymanPearsonOrdering",
        "MedianUnbiasedScoreTestOrdering",
        "MedianUnbiasedStagewiseCombinationFunctionOrdering",
        "MidpointLikelihoodRatioOrderingCI",
        "MidpointMLEOrderingCI",
        "MidpointNeymanPearsonOrderingCI",
        "MidpointScoreTestOrderingCI",
        "MidpointStagewiseCombinationFunctionOrderingCI",
        "MinimizePeakVariance",
        "PointEstimator",
        "PointEstimator-class",
        "PseudoRaoBlackwell",
        "RaoBlackwell",
        "SampleMean",
        "WeightedSampleMean"
      ]
    },
    {
      "page": "PValue-class",
      "title": "P-values",
      "topics": [
        "LikelihoodRatioOrderingPValue",
        "LinearShiftRepeatedPValue",
        "MLEOrderingPValue",
        "NaivePValue",
        "NeymanPearsonOrderingPValue",
        "PValue",
        "PValue-class",
        "ScoreTestOrderingPValue",
        "StagewiseCombinationFunctionOrderingPValue"
      ]
    },
    {
      "page": "Statistic-class",
      "title": "Statistics and Estimators of the adestr package",
      "topics": [
        "Estimator",
        "Statistic",
        "Statistic-class",
        "Statistics"
      ]
    },
    {
      "page": "TwoStageDesignWithCache",
      "title": "TwoStageDesignWithCache constructor function",
      "topics": [
        "TwoStageDesignWithCache"
      ]
    },
    {
      "page": "UniformPrior",
      "title": "Uniform prior distribution for the parameter mu",
      "topics": [
        "UniformPrior"
      ]
    }
  ],
  "_readme": "https://github.com/jan-imbi/adestr/raw/HEAD/README.md",
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      "title": "Introduction to adestr",
      "author": "Jan Meis",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Fitting a design with adoptr",
        "Evaluating point estimators",
        "Evaluating the mean squared of the sample mean and the weighted sample mean",
        "Evaluating median unbiased estimators",
        "Evaluating bias-reduced unbiased estimators",
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        "Evaluating agreement with the primary test decision of the design for an interval estimator",
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        "Evaluating agreement with the primary test decision of the design for a p-value",
        "Conducting larger scale investigations in the performance characteristics of end-of-trial statistics",
        "Analyzing datasets"
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      "created": "2023-04-06 14:58:53",
      "modified": "2024-07-11 13:29:48",
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