{ "_id": "6a1f2861b401979e73423397", "Package": "crmPack", "Title": "Object-Oriented Implementation of Dose Escalation Designs", "Version": "2.1.0.9000", "Authors@R": "c(\nperson(\"Daniel\", \"Sabanés Bové\", , \"daniel.sabanes_bove@rconis.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-0176-9239\")),\nperson(\"Wai\", \"Yin Yeung\", , \"winnie.yeung@roche.com\", role = \"aut\"),\nperson(\"Burak Kuersad\", \"Guenhan\", , \"burakgunhan@gmail.com\", role = \"aut\"),\nperson(\"Giuseppe\", \"Palermo\", , \"giuseppe.palermo@roche.com\", role = \"aut\"),\nperson(\"Thomas\", \"Jaki\", , \"jaki.thomas@gmail.com\", role = \"aut\"),\nperson(\"Jiawen\", \"Zhu\", , \"zhu.jiawen@gene.com\", role = \"aut\"),\nperson(\"Ziwei\", \"Liao\", , \"ziwei.liao.fdu@gmail.com\", role = \"aut\"),\nperson(\"Dimitris\", \"Kontos\", , \"dimitris.kontos@bayer.com\", role = \"aut\"),\nperson(\"Marlene\", \"Schulte-Goebel\", , \"marlene.schulte-goebel@merckgroup.com\", role = \"aut\"),\nperson(\"Doug\", \"Kelkhoff\", , \"doug.kelkhoff@gmail.com\", role = \"aut\",\ncomment = c(ORCID = \"0009-0003-7845-4061\")),\nperson(\"Oliver\", \"Boix\", , \"oliver.boix@bayer.com\", role = \"aut\"),\nperson(\"Robert\", \"Adams\", , \"robert.adams@bayer.com\", role = \"aut\"),\nperson(\"Clara\", \"Beck\", , \"clara.beck@bayer.com\", role = \"aut\"),\nperson(\"John\", \"Kirkpatrick\", , \"john@puzzledface.net\", role = \"aut\"),\nperson(\"Wojciech\", \"Wójciak\", , \"wojciech.wojciak@gmail.com\", role = \"aut\"),\nperson(\"Guanya\", \"Peng\", , role = \"aut\"),\nperson(\"Prerana\", \"Chandratre\", , role = \"aut\"),\nperson(\"F. Hoffmann-La Roche AG\", role = c(\"cph\", \"fnd\")),\nperson(\"Merck Healthcare KGaA\", role = c(\"cph\", \"fnd\")),\nperson(\"Bayer AG\", role = c(\"cph\", \"fnd\")),\nperson(\"RPACT GmbH\", role = c(\"cph\", \"fnd\"))\n)", "Description": "Implements a wide range of dose escalation designs. The\nfocus is on model-based designs, ranging from classical and\nmodern continual reassessment methods (CRMs) based on\ndose-limiting toxicity endpoints to dual-endpoint designs\ntaking into account a biomarker/efficacy outcome. Bayesian\ninference is performed via MCMC sampling in JAGS, and it is\neasy to setup a new design with custom JAGS code. However, it\nis also possible to implement 3+3 designs for comparison or\nmodels with non-Bayesian estimation. The whole package is\nwritten in a modular form in the S4 class system, making it\nvery flexible for adaptation to new models, escalation or\nstopping rules. Further details are presented in Sabanés Bové\net al. (2019) .", "License": "GPL (>= 2)", "URL": "https://docs.crmpack.org/", "BugReports": "https://github.com/openpharma/crmPack/issues", "VignetteBuilder": "knitr, quarto", "Config/testthat/edition": "3", "Encoding": "UTF-8", "Language": "en-US", "LazyLoad": "yes", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.3", "RdMacros": "Rdpack", "Collate": "'Backfill-validity.R' 'CrmPackClass-class.R'\n'Rules-validity.R' 'helpers.R' 'Rules-class.R'\n'Backfill-class.R' 'Backfill-methods.R'\n'CrmPackClass-methods.R' 'Data-validity.R' 'Data-class.R'\n'helpers_data.R' 'Data-methods.R' 'ModelParams-validity.R'\n'ModelParams-class.R' 'Model-validity.R' 'helpers_jags.R'\n'Model-class.R' 'Design-validity.R' 'Design-class.R'\n'McmcOptions-validity.R' 'McmcOptions-class.R'\n'McmcOptions-methods.R' 'Samples-validity.R' 'Samples-class.R'\n'logger.R' 'helpers_covr.R' 'mcmc.R' 'Simulations-validity.R'\n'Simulations-class.R' 'helpers_broom.R' 'helpers_rules.R'\n'Model-methods.R' 'checkmate.R' 'Rules-methods.R'\n'Design-methods.R' 'fromQuantiles.R' 'Samples-methods.R'\n'Simulations-methods.R' 'crmPack-package.R'\n'helpers_backfill.R' 'helpers_design.R' 'helpers_knitr.R'\n'helpers_knitr_Backfill.R' 'helpers_knitr_CohortSize.R'\n'helpers_knitr_Design.R' 'helpers_knitr_GeneralData.R'\n'helpers_knitr_GeneralModel.R' 'helpers_knitr_Increments.R'\n'helpers_knitr_NextBest.R' 'helpers_knitr_Opening.R'\n'helpers_knitr_Recruitment.R' 'helpers_knitr_SafetyWindow.R'\n'helpers_knitr_Stopping.R' 'helpers_model.R'\n'helpers_samples.R' 'helpers_simulations.R' 'utils-pipe.R'\n'utils.R'", "Config/pak/sysreqs": "cmake libfontconfig1-dev libfreetype6-dev\nlibfribidi-dev make libharfbuzz-dev jags libicu-dev libpng-dev\nlibuv1-dev libxml2-dev", "Repository": "https://openpharma.r-universe.dev", "Date/Publication": "2026-03-17 01:14:31 UTC", "RemoteUrl": "https://github.com/openpharma/crmpack", "RemoteRef": "HEAD", "RemoteSha": "38ee7fae924ef77923a758a3adf22fad91297349", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-16 05:20:17 UTC", "User": "root" }, "Author": "Daniel Sabanés Bové [aut, cre] (ORCID:\n),\nWai Yin Yeung [aut],\nBurak Kuersad Guenhan [aut],\nGiuseppe Palermo [aut],\nThomas Jaki [aut],\nJiawen Zhu [aut],\nZiwei Liao [aut],\nDimitris Kontos [aut],\nMarlene Schulte-Goebel [aut],\nDoug Kelkhoff [aut] (ORCID: ),\nOliver Boix [aut],\nRobert Adams [aut],\nClara Beck [aut],\nJohn Kirkpatrick [aut],\nWojciech Wójciak [aut],\nGuanya Peng [aut],\nPrerana Chandratre [aut],\nF. Hoffmann-La Roche AG [cph, fnd],\nMerck Healthcare KGaA [cph, fnd],\nBayer AG [cph, fnd],\nRPACT GmbH [cph, fnd]", "Maintainer": "Daniel Sabanés Bové ", "MD5sum": "0f7352e04ff13ed2d2e3f56ddf7b996c", "_user": "openpharma", "_type": "src", "_file": "crmPack_2.1.0.9000.tar.gz", "_fileid": "21713e8a45a055884010b78187eda5ab27fa402f2feec6c86d67d4a226b4b882", "_filesize": 8035572, "_sha256": "21713e8a45a055884010b78187eda5ab27fa402f2feec6c86d67d4a226b4b882", "_created": "2026-05-16T05:20:17.000Z", "_published": "2026-06-02T19:00:48.982Z", "_distro": "noble", "_jobs": [ { "job": 79149242084, "time": 289, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7030790415" }, { "job": 79149242640, "time": 265, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7030787446" }, { "job": 79149242436, "time": 180, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7030777699" }, { "job": 79149242261, "time": 272, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7030788294" }, { "job": 79149241504, "time": 364, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7030756924" }, { "job": 79149241635, "time": 144, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7367152145" }, { "job": 79149242549, "time": 332, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7030796005" }, { "job": 79149242467, "time": 292, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7030790585" }, { "job": 79149242552, "time": 267, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7030787707" } ], "_buildurl": "https://github.com/r-universe/openpharma/actions/runs/25953626182", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/openpharma/crmpack", "_commit": { "id": "38ee7fae924ef77923a758a3adf22fad91297349", "author": "Daniel Sabanes Bove ", "committer": "GitHub ", "message": "improve knit_print for LogisticNormalMixture (#937)", "time": 1773710071 }, "_maintainer": { "name": "Daniel Sabanés Bové", "email": "daniel.sabanes_bove@rconis.com", "orcid": "0000-0002-0176-9239" }, "_registered": true, "_dependencies": [ { "package": "ggplot2", "version": ">= 3.0.0", "role": "Depends" }, { "package": "graphics", "role": "Depends" }, { "package": "R", "version": ">= 4.1.0", "role": "Depends" }, { "package": "checkmate", "version": ">= 2.2.0", "role": "Imports" }, { "package": "dplyr", "role": "Imports" }, { "package": "futile.logger", "role": "Imports" }, { "package": "GenSA", "role": "Imports" }, { "package": "gridExtra", "role": "Imports" }, { "package": "kableExtra", "role": "Imports" }, { "package": "knitr", "role": "Imports" }, { "package": "lifecycle", "role": "Imports" }, { "package": "magrittr", "role": "Imports" }, { "package": "methods", "role": "Imports" }, { "package": "mvtnorm", "role": "Imports" }, { "package": "parallel", "role": "Imports" }, { "package": "parallelly", "role": "Imports" }, { "package": "Rdpack", "role": "Imports" }, { "package": "rjags", "role": "Imports" }, { "package": "rlang", "role": "Imports" }, { "package": "survival", "role": "Imports" }, { "package": "tibble", "role": "Imports" }, { "package": "tidyselect", "version": ">= 1.2.0", "role": "Imports" }, { "package": "tools", "role": "Imports" }, { "package": "utils", "role": "Imports" }, { "package": "bookdown", "role": "Suggests" }, { "package": "broom", "role": "Suggests" }, { "package": "covr", "role": "Suggests" }, { "package": "data.tree", "role": "Suggests" }, { "package": "DiagrammeR", "role": "Suggests" }, { "package": "ggmcmc", "role": "Suggests" }, { "package": "quarto", "version": ">= 1.4", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "stringr", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" }, { "package": "tidyr", "role": "Suggests" }, { "package": "vdiffr", "role": "Suggests" }, { "package": "withr", "role": "Suggests" } ], "_owner": "openpharma", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2025-38", "n": 3 }, { "week": "2025-39", "n": 3 }, { "week": "2025-42", "n": 1 }, { "week": "2025-43", "n": 3 }, { "week": "2025-44", "n": 3 }, { "week": "2025-46", "n": 1 }, { "week": "2025-47", "n": 7 }, { "week": "2025-48", "n": 10 }, { "week": "2025-49", "n": 1 }, { "week": "2026-01", "n": 1 }, { "week": "2026-02", "n": 2 }, { "week": "2026-04", "n": 2 }, { "week": "2026-05", "n": 2 }, { "week": "2026-07", "n": 1 }, { "week": "2026-08", "n": 1 }, { "week": "2026-09", "n": 1 }, { "week": "2026-12", "n": 1 } ], "_tags": [ { "name": "v2.0.1", "date": "2025-12-04" }, { "name": "v2.0.2", "date": "2026-01-09" }, { "name": "v2.1.0", "date": "2026-01-30" } ], "_stars": 22, "_contributors": [ { "user": "wwojciech", "count": 309, "uuid": 11532997 }, { "user": "puzzled-face", "count": 84, "uuid": 133956382 }, { "user": "danielinteractive", "count": 84, "uuid": 1071227 }, { "user": "palermogp", "count": 42, "uuid": 41475832 }, { "user": "0liver0815", "count": 24, "uuid": 95433070 }, { "user": "pengguanya", "count": 15, "uuid": 4429650 }, { "user": "tjaki", "count": 14, "uuid": 52420591 }, { "user": "cicdguy", "count": 8, "uuid": 26552821 }, { "user": "puzzledface", "count": 7, "uuid": 52156249 }, { "user": "dimitriskontosbay", "count": 6, "uuid": 95923214 }, { "user": "clarabeckbayer", "count": 5, "uuid": 98522410 }, { "user": "marlenesg", "count": 3, "uuid": 106533018 }, { "user": "dimkon5", "count": 3, "uuid": 95762385 }, { "user": "dgkf", "count": 3, "uuid": 18220321 }, { "user": "gunhanb", "count": 2, "uuid": 22449877 }, { "user": "prerana17", "count": 2, "uuid": 82475780 }, { "user": "fenguoerbian", "count": 2, "uuid": 12522724 }, { "user": "puzzledface-astellas", "count": 2, "uuid": 188564520 }, { "user": "pc494", "count": 1, "uuid": 32455330 }, { "user": "robertadamsbayer", "count": 1, "uuid": 97281825 }, { "user": "martynplummer", "count": 1, "uuid": 3372481 }, { "user": "reikoch", "count": 1, "uuid": 8436725 } ], "_userbio": { "uuid": 8436743, "type": "organization", "name": "openpharma", "description": "Further precompetitive collaboration in life sciences" }, "_downloads": { "count": 360, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/crmPack" }, "_mentions": 1, "_devurl": "https://github.com/openpharma/crmpack", "_pkgdown": "https://docs.crmpack.org/", "_searchresults": 232, "_topics": [ "jags", "cpp" ], "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/crmPack.html", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "manual.pdf" ], "_homeurl": "https://github.com/openpharma/crmpack", "_realowner": "openpharma", "_cranurl": true, "_releases": [ { "version": "0.1.5", "date": "2015-11-12" }, { "version": "0.1.6", "date": "2015-12-22" }, { "version": "0.1.7", "date": "2016-01-25" }, { "version": "0.1.8", "date": "2016-02-17" }, { "version": "0.1.9", "date": "2016-02-29" }, { "version": "0.2.0", "date": "2016-07-16" }, { "version": "0.2.1", "date": "2017-05-03" }, { "version": "0.2.6", "date": "2018-02-15" }, { "version": "0.2.7", "date": "2018-03-13" }, { "version": "0.2.9", "date": "2018-12-21" }, { "version": "1.0.0", "date": "2019-06-13" }, { "version": "1.0.2", "date": "2022-04-25" }, { "version": "1.0.3", "date": "2022-09-03" }, { "version": "1.0.4", "date": "2024-01-23" }, { "version": "1.0.5", "date": "2024-02-04" }, { "version": "1.0.6", "date": "2024-06-26" }, { "version": "2.0.0", "date": "2025-11-29" }, { "version": "2.0.1", "date": "2025-12-11" }, { "version": "2.0.2", "date": "2026-01-08" }, { "version": "2.1.0", "date": "2026-01-30" } ], "_exports": [ ".Backfill", ".CohortSizeConst", ".CohortSizeDLT", ".CohortSizeMax", ".CohortSizeMin", ".CohortSizeOrdinal", ".CohortSizeParts", ".CohortSizeRandom", ".CohortSizeRange", ".CrmPackClass", ".DADesign", ".DALogisticLogNormal", ".DASimulations", ".Data", ".DataDA", ".DataDual", ".DataGrouped", ".DataMixture", ".DataOrdinal", ".DataParts", ".DefaultBackfill", ".DefaultCohortSize", ".DefaultCohortSizeConst", ".DefaultCohortSizeDLT", ".DefaultCohortSizeMax", ".DefaultCohortSizeMin", ".DefaultCohortSizeOrdinal", ".DefaultCohortSizeParts", ".DefaultCohortSizeRandom", ".DefaultCohortSizeRange", ".DefaultDADesign", ".DefaultDALogisticLogNormal", ".DefaultDASimulations", ".DefaultData", ".DefaultDataDA", ".DefaultDataDual", ".DefaultDataGeneral", ".DefaultDataGrouped", ".DefaultDataMixture", ".DefaultDataOrdinal", ".DefaultDataParts", ".DefaultDesign", ".DefaultDesignGrouped", ".DefaultDesignOrdinal", ".DefaultDualDesign", ".DefaultDualEndpoint", ".DefaultDualEndpointBeta", ".DefaultDualEndpointEmax", ".DefaultDualEndpointRW", ".DefaultDualResponsesDesign", ".DefaultDualResponsesSamplesDesign", ".DefaultDualSimulations", ".DefaultDualSimulationsSummary", ".DefaultEffFlexi", ".DefaultEffloglog", ".DefaultFractionalCRM", ".DefaultGeneralModel", ".DefaultGeneralSimulations", ".DefaultGeneralSimulationsSummary", ".DefaultIncrements", ".DefaultIncrementsDoseLevels", ".DefaultIncrementsHSRBeta", ".DefaultIncrementsMaxToxProb", ".DefaultIncrementsMin", ".DefaultIncrementsOrdinal", ".DefaultIncrementsRelative", ".DefaultIncrementsRelativeDLT", ".DefaultIncrementsRelativeDLTCurrent", ".DefaultIncrementsRelativeParts", ".DefaultLogisticIndepBeta", ".DefaultLogisticKadane", ".DefaultLogisticKadaneBetaGamma", ".DefaultLogisticLogNormal", ".DefaultLogisticLogNormalGrouped", ".DefaultLogisticLogNormalMixture", ".DefaultLogisticLogNormalOrdinal", ".DefaultLogisticLogNormalSub", ".DefaultLogisticNormal", ".DefaultLogisticNormalFixedMixture", ".DefaultLogisticNormalMixture", ".DefaultMcmcOptions", ".DefaultModelEff", ".DefaultModelLogNormal", ".DefaultModelParamsNormal", ".DefaultModelPseudo", ".DefaultModelTox", ".DefaultNextBest", ".DefaultNextBestDualEndpoint", ".DefaultNextBestEWOC", ".DefaultNextBestInfTheory", ".DefaultNextBestMaxGain", ".DefaultNextBestMaxGainSamples", ".DefaultNextBestMinDist", ".DefaultNextBestMTD", ".DefaultNextBestNCRM", ".DefaultNextBestNCRMLoss", ".DefaultNextBestOrdinal", ".DefaultNextBestProbMTDLTE", ".DefaultNextBestProbMTDMinDist", ".DefaultNextBestTD", ".DefaultNextBestTDsamples", ".DefaultNextBestThreePlusThree", ".DefaultOneParExpPrior", ".DefaultOneParLogNormalPrior", ".DefaultOpening", ".DefaultOpeningAll", ".DefaultOpeningAny", ".DefaultOpeningList", ".DefaultOpeningMinCohorts", ".DefaultOpeningMinDose", ".DefaultOpeningMinResponses", ".DefaultOpeningNone", ".DefaultProbitLogNormal", ".DefaultProbitLogNormalRel", ".DefaultPseudoDualFlexiSimulations", ".DefaultPseudoDualSimulations", ".DefaultPseudoDualSimulationsSummary", ".DefaultPseudoSimulations", ".DefaultPseudoSimulationsSummary", ".DefaultRecruitment", ".DefaultRecruitmentRatio", ".DefaultRecruitmentUnlimited", ".DefaultRuleDesign", ".DefaultRuleDesignOrdinal", ".DefaultSafetyWindow", ".DefaultSafetyWindowConst", ".DefaultSafetyWindowSize", ".DefaultSamples", ".DefaultSimulations", ".DefaultSimulationsSummary", ".DefaultStoppingAll", ".DefaultStoppingAny", ".DefaultStoppingCohortsNearDose", ".DefaultStoppingExternal", ".DefaultStoppingHighestDose", ".DefaultStoppingList", ".DefaultStoppingLowestDoseHSRBeta", ".DefaultStoppingMaxGainCIRatio", ".DefaultStoppingMinCohorts", ".DefaultStoppingMinPatients", ".DefaultStoppingMissingDose", ".DefaultStoppingMTDCV", ".DefaultStoppingMTDdistribution", ".DefaultStoppingOrdinal", ".DefaultStoppingPatientsNearDose", ".DefaultStoppingSpecificDose", ".DefaultStoppingTargetBiomarker", ".DefaultStoppingTargetProb", ".DefaultStoppingTDCIRatio", ".DefaultTDDesign", ".DefaultTDsamplesDesign", ".DefaultTITELogisticLogNormal", ".Design", ".DesignGrouped", ".DesignOrdinal", ".DualDesign", ".DualEndpoint", ".DualEndpointBeta", ".DualEndpointEmax", ".DualEndpointRW", ".DualResponsesDesign", ".DualResponsesSamplesDesign", ".DualSimulations", ".DualSimulationsSummary", ".EffFlexi", ".Effloglog", ".FractionalCRM", ".GeneralData", ".GeneralModel", ".GeneralSimulations", ".GeneralSimulationsSummary", ".IncrementsDoseLevels", ".IncrementsHSRBeta", ".IncrementsMaxToxProb", ".IncrementsMin", ".IncrementsOrdinal", ".IncrementsRelative", ".IncrementsRelativeDLT", ".IncrementsRelativeDLTCurrent", ".IncrementsRelativeParts", ".LogisticIndepBeta", ".LogisticKadane", ".LogisticKadaneBetaGamma", ".LogisticLogNormal", ".LogisticLogNormalGrouped", ".LogisticLogNormalMixture", ".LogisticLogNormalOrdinal", ".LogisticLogNormalSub", ".LogisticNormal", ".LogisticNormalFixedMixture", ".LogisticNormalMixture", ".McmcOptions", ".ModelEff", ".ModelLogNormal", ".ModelParamsNormal", ".ModelPseudo", ".ModelTox", ".NextBestDualEndpoint", ".NextBestEWOC", ".NextBestInfTheory", ".NextBestMaxGain", ".NextBestMaxGainSamples", ".NextBestMinDist", ".NextBestMTD", ".NextBestNCRM", ".NextBestNCRMLoss", ".NextBestOrdinal", ".NextBestProbMTDLTE", ".NextBestProbMTDMinDist", ".NextBestTD", ".NextBestTDsamples", ".NextBestThreePlusThree", ".OneParExpPrior", ".OneParLogNormalPrior", ".Opening", ".OpeningAll", ".OpeningAny", ".OpeningList", ".OpeningMinCohorts", ".OpeningMinDose", ".OpeningMinResponses", ".OpeningNone", ".ProbitLogNormal", ".ProbitLogNormalRel", ".PseudoDualFlexiSimulations", ".PseudoDualSimulations", ".PseudoDualSimulationsSummary", ".PseudoSimulations", ".PseudoSimulationsSummary", ".Recruitment", ".RecruitmentRatio", ".RecruitmentUnlimited", ".RuleDesign", ".RuleDesignOrdinal", ".SafetyWindowConst", ".SafetyWindowSize", ".Samples", ".Simulations", ".SimulationsSummary", ".StoppingAll", ".StoppingAny", ".StoppingCohortsNearDose", ".StoppingExternal", ".StoppingHighestDose", ".StoppingList", ".StoppingLowestDoseHSRBeta", ".StoppingMaxGainCIRatio", ".StoppingMinCohorts", ".StoppingMinPatients", ".StoppingMissingDose", ".StoppingMTDCV", ".StoppingMTDdistribution", ".StoppingOrdinal", ".StoppingPatientsNearDose", ".StoppingSpecificDose", ".StoppingTargetBiomarker", ".StoppingTargetProb", ".StoppingTDCIRatio", ".TDDesign", ".TDsamplesDesign", ".TITELogisticLogNormal", "%>%", "approximate", "assert_equal", "assert_format", "assert_length", "assert_probabilities", "assert_probability", "assert_probability_range", "assert_range", "Backfill", "biomarker", "check_equal", "check_format", "check_length", "check_probabilities", "check_probability", "check_probability_range", "check_range", "CohortSizeConst", "CohortSizeDLT", "CohortSizeMax", "CohortSizeMin", "CohortSizeOrdinal", "CohortSizeParts", "CohortSizeRandom", "CohortSizeRange", "crmPackExample", "crmPackHelp", "DADesign", "DALogisticLogNormal", "dapply", "DASimulations", "Data", "DataDA", "DataDual", "DataGrouped", "DataMixture", "DataOrdinal", "DataParts", "Design", "DesignGrouped", "DesignOrdinal", "disable_logging", "dose", "dose_grid_range", "doseFunction", "DualDesign", "DualEndpoint", "DualEndpointBeta", "DualEndpointEmax", "DualEndpointRW", "DualResponsesDesign", "DualResponsesSamplesDesign", "DualSimulations", "EffFlexi", "efficacy", "efficacyFunction", "Effloglog", "enable_logging", "examine", "expect_format", "expect_probabilities", "expect_probability", "expect_probability_range", "expect_range", "fit", "fitGain", "fitPEM", "FractionalCRM", "gain", "GeneralSimulations", "get", "getEff", "h_all_equivalent", "h_check_fun_formals", "h_convert_ordinal_data", "h_convert_ordinal_model", "h_convert_ordinal_samples", "h_default_if_empty", "h_find_interval", "h_format_number", "h_in_range", "h_info_theory_dist", "h_is_positive_definite", "h_jags_add_dummy", "h_jags_extract_samples", "h_jags_get_data", "h_jags_get_model_inits", "h_jags_join_models", "h_jags_write_model", "h_model_dual_endpoint_beta", "h_model_dual_endpoint_rho", "h_model_dual_endpoint_sigma2betaw", "h_model_dual_endpoint_sigma2w", "h_next_best_eligible_doses", "h_next_best_mg_ci", "h_next_best_mg_doses_at_grid", "h_next_best_mg_plot", "h_next_best_mgsamples_plot", "h_next_best_ncrm_loss_plot", "h_next_best_td_plot", "h_next_best_tdsamples_plot", "h_null_if_na", "h_rapply", "h_slots", "h_test_named_numeric", "h_validate_combine_results", "IncrementsDoseLevels", "IncrementsHSRBeta", "IncrementsMaxToxProb", "IncrementsMin", "IncrementsOrdinal", "IncrementsRelative", "IncrementsRelativeDLT", "IncrementsRelativeDLTCurrent", "IncrementsRelativeParts", "is_logging_enabled", "log_trace", "LogisticIndepBeta", "LogisticKadane", "LogisticKadaneBetaGamma", "LogisticLogNormal", "LogisticLogNormalGrouped", "LogisticLogNormalMixture", "LogisticLogNormalOrdinal", "LogisticLogNormalSub", "LogisticNormal", "LogisticNormalFixedMixture", "LogisticNormalMixture", "logit", "match_within_tolerance", "maxDose", "maxRecruits", "maxSize", "mcmc", "McmcOptions", "MinimalInformative", "minSize", "ModelLogNormal", "ModelParamsNormal", "nextBest", "NextBestDualEndpoint", "NextBestEWOC", "NextBestInfTheory", "NextBestMaxGain", "NextBestMaxGainSamples", "NextBestMinDist", "NextBestMTD", "NextBestNCRM", "NextBestNCRMLoss", "NextBestOrdinal", "NextBestProbMTDLTE", "NextBestProbMTDMinDist", "NextBestTD", "NextBestTDsamples", "NextBestThreePlusThree", "ngrid", "OneParExpPrior", "OneParLogNormalPrior", "openCohort", "OpeningAll", "OpeningAny", "OpeningList", "OpeningMinCohorts", "OpeningMinDose", "OpeningMinResponses", "OpeningNone", "plot", "plotDualResponses", "plotGain", "prob", "probFunction", "probit", "ProbitLogNormal", "ProbitLogNormalRel", "PseudoDualFlexiSimulations", "PseudoDualSimulations", "PseudoSimulations", "Quantiles2LogisticNormal", "RecruitmentRatio", "RecruitmentUnlimited", "RuleDesign", "RuleDesignOrdinal", "SafetyWindowConst", "SafetyWindowSize", "Samples", "saveSample", "set_seed", "show", "simulate", "Simulations", "size", "StoppingAll", "StoppingAny", "StoppingCohortsNearDose", "StoppingExternal", "StoppingHighestDose", "StoppingList", "StoppingLowestDoseHSRBeta", "StoppingMaxGainCIRatio", "StoppingMinCohorts", "StoppingMinPatients", "StoppingMissingDose", "StoppingMTDCV", "StoppingMTDdistribution", "StoppingOrdinal", "StoppingPatientsNearDose", "StoppingSpecificDose", "StoppingTargetBiomarker", "StoppingTargetProb", "StoppingTDCIRatio", "stopTrial", "summary", "TDDesign", "TDsamplesDesign", "test_format", "test_length", "test_probabilities", "test_probability", "test_probability_range", "test_range", "ThreePlusThreeDesign", "tidy", "TITELogisticLogNormal", "update", "windowLength" ], "_help": [ { "page": "CohortSize-class", "title": "'CohortSize'", "topics": [ ".DefaultCohortSize", "CohortSize", "CohortSize-class" ] }, { "page": "and-Stopping-Stopping-method", "title": "Combine Two Stopping Rules with AND", "topics": [ "&,Stopping,Stopping-method", "and-Stopping-Stopping" ] }, { "page": "and-Stopping-StoppingAll-method", "title": "Combine an Atomic Stopping Rule and a Stopping List with AND", "topics": [ "&,Stopping,StoppingAll-method", "and-Stopping-StoppingAll" ] }, { "page": "and-StoppingAll-Stopping-method", "title": "Combine a Stopping List and an Atomic Stopping Rule with AND", "topics": [ "&,StoppingAll,Stopping-method", "and-StoppingAll-Stopping" ] }, { "page": "and-Opening-Opening-method", "title": "Logical AND Operator for Opening Objects", "topics": [ "&,Opening,Opening-method", "and,Opening,Opening-method" ] }, { "page": "approximate", "title": "Approximate posterior with (log) normal distribution", "topics": [ "approximate", "approximate,Samples-method" ] }, { "page": "assertions", "title": "Additional Assertions for 'checkmate'", "topics": [ "assertions" ] }, { "page": "Backfill-class", "title": "'Backfill' class", "topics": [ ".Backfill", ".DefaultBackfill", "Backfill", "Backfill-class" ] }, { "page": "biomarker", "title": "Get the Biomarker Levels for a Given Dual-Endpoint Model, Given Dose Levels and Samples", "topics": [ "biomarker", "biomarker,integer,DualEndpoint,Samples-method", "biomarker-DualEndpoint" ] }, { "page": "check_equal", "title": "Check if All Arguments Are Equal", "topics": [ "assert_equal", "check_equal" ] }, { "page": "check_format", "title": "Check that an argument is a valid format specification", "topics": [ "assert_format", "check_format", "expect_format", "test_format" ] }, { "page": "check_length", "title": "Check if vectors are of compatible lengths", "topics": [ "assert_length", "check_length", "test_length" ] }, { "page": "check_probabilities", "title": "Check if an argument is a probability vector", "topics": [ "assert_probabilities", "check_probabilities", "expect_probabilities", "test_probabilities" ] }, { "page": "check_probability", "title": "Check if an argument is a single probability value", "topics": [ "assert_probability", "check_probability", "expect_probability", "test_probability" ] }, { "page": "check_probability_range", "title": "Check if an argument is a probability range", "topics": [ "assert_probability_range", "check_probability_range", "expect_probability_range", "test_probability_range" ] }, { "page": "check_range", "title": "Check that an argument is a numerical range", "topics": [ "assert_range", "check_range", "expect_range", "test_range" ] }, { "page": "CohortSizeConst-class", "title": "'CohortSizeConst'", "topics": [ ".CohortSizeConst", ".DefaultCohortSizeConst", "CohortSizeConst", "CohortSizeConst-class" ] }, { "page": "CohortSizeDLT-class", "title": "'CohortSizeDLT'", "topics": [ ".CohortSizeDLT", ".DefaultCohortSizeDLT", "CohortSizeDLT", "CohortSizeDLT-class" ] }, { "page": "CohortSizeMax-class", "title": "'CohortSizeMax'", "topics": [ ".CohortSizeMax", ".DefaultCohortSizeMax", "CohortSizeMax", "CohortSizeMax-class" ] }, { "page": "CohortSizeMin-class", "title": "'CohortSizeMin'", "topics": [ ".CohortSizeMin", ".DefaultCohortSizeMin", "CohortSizeMin", "CohortSizeMin-class" ] }, { "page": "CohortSizeOrdinal-class", "title": "'CohortSizeOrdinal'", "topics": [ ".CohortSizeOrdinal", ".DefaultCohortSizeOrdinal", "CohortSizeOrdinal", "CohortSizeOrdinal-class" ] }, { "page": "CohortSizeParts-class", "title": "'CohortSizeParts'", "topics": [ ".CohortSizeParts", ".DefaultCohortSizeParts", "CohortSizeParts", "CohortSizeParts-class" ] }, { "page": "CohortSizeRandom-class", "title": "'CohortSizeRandom'", "topics": [ ".CohortSizeRandom", ".DefaultCohortSizeRandom", "CohortSizeRandom", "CohortSizeRandom-class" ] }, { "page": "CohortSizeRange-class", "title": "'CohortSizeRange'", "topics": [ ".CohortSizeRange", ".DefaultCohortSizeRange", "CohortSizeRange", "CohortSizeRange-class" ] }, { "page": "crmPack", "title": "Object-oriented implementation of CRM designs", "topics": [ "crmPack-package", "crmPack" ] }, { "page": "CrmPackClass", "title": "'CrmPackClass'", "topics": [ ".CrmPackClass", "CrmPackClass", "CrmPackClass-class" ] }, { "page": "crmPackExample", "title": "Open the Example PDF for crmPack", "topics": [ "crmPackExample" ] }, { "page": "crmPackHelp", "title": "Open the Browser with Help Pages for crmPack", "topics": [ "crmPackHelp" ] }, { "page": "DADesign-class", "title": "'DADesign'", "topics": [ ".DADesign", ".DefaultDADesign", "DADesign", "DADesign-class" ] }, { "page": "DALogisticLogNormal-class", "title": "'DALogisticLogNormal'", "topics": [ ".DALogisticLogNormal", ".DefaultDALogisticLogNormal", "DALogisticLogNormal", "DALogisticLogNormal-class" ] }, { "page": "dapply", "title": "Apply a Function to Subsets of Data Frame.", "topics": [ "dapply" ] }, { "page": "DASimulations-class", "title": "'DASimulations'", "topics": [ ".DASimulations", ".DefaultDASimulations", "DASimulations", "DASimulations-class" ] }, { "page": "Data-class", "title": "'Data'", "topics": [ ".Data", ".DefaultData", "Data", "Data-class" ] }, { "page": "DataDA-class", "title": "'DataDA'", "topics": [ ".DataDA", ".DefaultDataDA", "DataDA", "DataDA-class" ] }, { "page": "DataDual-class", "title": "'DataDual'", "topics": [ ".DataDual", ".DefaultDataDual", "DataDual", "DataDual-class" ] }, { "page": "DataGrouped-class", "title": "'DataGrouped'", "topics": [ ".DataGrouped", ".DefaultDataGrouped", "DataGrouped", "DataGrouped-class" ] }, { "page": "DataMixture-class", "title": "'DataMixture'", "topics": [ ".DataMixture", ".DefaultDataMixture", "DataMixture", "DataMixture-class" ] }, { "page": "DataOrdinal-class", "title": "'DataOrdinal'", "topics": [ ".DataOrdinal", ".DefaultDataOrdinal", "DataOrdinal", "DataOrdinal-class" ] }, { "page": "DataParts-class", "title": "'DataParts'", "topics": [ ".DataParts", ".DefaultDataParts", "DataParts", "DataParts-class" ] }, { "page": "Design-class", "title": "'Design'", "topics": [ ".DefaultDesign", ".Design", "Design", "Design-class" ] }, { "page": "DesignGrouped-class", "title": "'DesignGrouped'", "topics": [ ".DefaultDesignGrouped", ".DesignGrouped", "DesignGrouped", "DesignGrouped-class" ] }, { "page": "DesignOrdinal-class", "title": "'DesignOrdinal'", "topics": [ ".DefaultDesignOrdinal", ".DesignOrdinal", "DesignOrdinal", "DesignOrdinal-class" ] }, { "page": "dose", "title": "Computing the Doses for a given independent variable, Model and Samples", "topics": [ "dose", "dose,numeric,DualEndpoint,Samples-method", "dose,numeric,EffFlexi,Samples-method", "dose,numeric,Effloglog,missing-method", "dose,numeric,LogisticIndepBeta,missing-method", "dose,numeric,LogisticIndepBeta,Samples-method", "dose,numeric,LogisticKadane,Samples-method", "dose,numeric,LogisticKadaneBetaGamma,Samples-method", "dose,numeric,LogisticLogNormal,Samples-method", "dose,numeric,LogisticLogNormalGrouped,Samples-method", "dose,numeric,LogisticLogNormalMixture,Samples-method", "dose,numeric,LogisticLogNormalOrdinal,Samples-method", "dose,numeric,LogisticLogNormalSub,Samples-method", "dose,numeric,LogisticNormal,Samples-method", "dose,numeric,LogisticNormalFixedMixture,Samples-method", "dose,numeric,LogisticNormalMixture,Samples-method", "dose,numeric,OneParExpPrior,Samples-method", "dose,numeric,OneParLogNormalPrior,Samples-method", "dose,numeric,ProbitLogNormal,Samples-method", "dose,numeric,ProbitLogNormalRel,Samples-method", "dose-DualEndpoint", "dose-EffFlexi", "dose-Effloglog-noSamples", "dose-LogisticIndepBeta", "dose-LogisticIndepBeta-noSamples", "dose-LogisticKadane", "dose-LogisticKadaneBetaGamma", "dose-LogisticLogNormal", "dose-LogisticLogNormalGrouped", "dose-LogisticLogNormalMixture", "dose-LogisticLogNormalOrdinal", "dose-LogisticLogNormalSub", "dose-LogisticNormal", "dose-LogisticNormalFixedMixture", "dose-LogisticNormalMixture", "dose-OneParExpPrior", "dose-OneParLogNormalPrior", "dose-ProbitLogNormal", "dose-ProbitLogNormalRel" ] }, { "page": "dose_grid_range", "title": "Getting the Dose Grid Range", "topics": [ "dose_grid_range", "dose_grid_range,Data-method", "dose_grid_range,DataOrdinal-method", "dose_grid_range-Data" ] }, { "page": "doseFunction", "title": "Getting the Dose Function for a Given Model Type", "topics": [ "doseFunction", "doseFunction,GeneralModel-method", "doseFunction,LogisticLogNormalOrdinal-method", "doseFunction,ModelPseudo-method", "doseFunction-GeneralModel", "doseFunction-LogisticLogNormalOrdinal", "doseFunction-ModelPseudo" ] }, { "page": "DualDesign-class", "title": "'DualDesign'", "topics": [ ".DefaultDualDesign", ".DualDesign", "DualDesign", "DualDesign-class" ] }, { "page": "DualEndpoint-class", "title": "'DualEndpoint'", "topics": [ ".DefaultDualEndpoint", ".DualEndpoint", "DualEndpoint", "DualEndpoint-class" ] }, { "page": "DualEndpointBeta-class", "title": "'DualEndpointBeta'", "topics": [ ".DefaultDualEndpointBeta", ".DualEndpointBeta", "DualEndpointBeta", "DualEndpointBeta-class" ] }, { "page": "DualEndpointEmax-class", "title": "'DualEndpointEmax'", "topics": [ ".DefaultDualEndpointEmax", ".DualEndpointEmax", "DualEndpointEmax", "DualEndpointEmax-class" ] }, { "page": "DualEndpointRW-class", "title": "'DualEndpointRW'", "topics": [ ".DefaultDualEndpointRW", ".DualEndpointRW", "DualEndpointRW", "DualEndpointRW-class" ] }, { "page": "DualResponsesDesign-class", "title": "'DualResponsesDesign.R'", "topics": [ ".DefaultDualResponsesDesign", ".DualResponsesDesign", "DualResponsesDesign", "DualResponsesDesign-class" ] }, { "page": "DualResponsesSamplesDesign-class", "title": "'DualResponsesSamplesDesign'", "topics": [ ".DefaultDualResponsesSamplesDesign", ".DualResponsesSamplesDesign", "DualResponsesSamplesDesign", "DualResponsesSamplesDesign-class" ] }, { "page": "DualSimulations-class", "title": "'DualSimulations'", "topics": [ ".DefaultDualSimulations", ".DualSimulations", "DualSimulations", "DualSimulations-class" ] }, { "page": "DualSimulationsSummary-class", "title": "'DualSimulationsSummary'", "topics": [ ".DefaultDualSimulationsSummary", ".DualSimulationsSummary", "DualSimulationsSummary", "DualSimulationsSummary-class" ] }, { "page": "EffFlexi-class", "title": "'EffFlexi'", "topics": [ ".DefaultEffFlexi", ".EffFlexi", "EffFlexi", "EffFlexi-class" ] }, { "page": "efficacy", "title": "Computing Expected Efficacy for a Given Dose, Model and Samples", "topics": [ "efficacy", "efficacy,numeric,EffFlexi,Samples-method", "efficacy,numeric,Effloglog,missing-method", "efficacy,numeric,Effloglog,Samples-method", "efficacy-EffFlexi", "efficacy-Effloglog", "efficacy-Effloglog-noSamples" ] }, { "page": "efficacyFunction", "title": "Getting the Efficacy Function for a Given Model Type", "topics": [ "efficacyFunction", "efficacyFunction,ModelEff-method", "efficacyFunction-ModelEff" ] }, { "page": "Effloglog-class", "title": "'Effloglog'", "topics": [ ".DefaultEffloglog", ".Effloglog", "Effloglog", "Effloglog-class" ] }, { "page": "enable_logging", "title": "Verbose Logging", "topics": [ "disable_logging", "enable_logging", "is_logging_enabled", "log_trace" ] }, { "page": "examine", "title": "Obtain Hypothetical Trial Course Table for a Design", "topics": [ "examine", "examine,DADesign-method", "examine,Design-method", "examine,RuleDesign-method" ] }, { "page": "fit", "title": "Fit method for the Samples class", "topics": [ "fit", "fit,Samples,DualEndpoint,DataDual-method", "fit,Samples,EffFlexi,DataDual-method", "fit,Samples,Effloglog,DataDual-method", "fit,Samples,GeneralModel,Data-method", "fit,Samples,LogisticIndepBeta,Data-method", "fit,Samples,LogisticLogNormalOrdinal,DataOrdinal-method" ] }, { "page": "fitGain", "title": "Get the fitted values for the gain values at all dose levels based on a given pseudo DLE model, DLE sample, a pseudo efficacy model, a Efficacy sample and data. This method returns a data frame with dose, middle, lower and upper quantiles of the gain value samples", "topics": [ "fitGain", "fitGain,ModelTox,Samples,ModelEff,Samples,DataDual-method" ] }, { "page": "fitPEM", "title": "Get the fitted DLT free survival (piecewise exponential model). This function returns a data frame with dose, middle, lower and upper quantiles for the 'PEM' curve. If hazard=TRUE,", "topics": [ "fitPEM", "fitPEM,Samples,DALogisticLogNormal,DataDA-method" ] }, { "page": "FractionalCRM-class", "title": "'FractionalCRM'", "topics": [ ".DefaultFractionalCRM", ".FractionalCRM", "FractionalCRM", "FractionalCRM-class" ] }, { "page": "gain", "title": "Compute Gain Values based on Pseudo DLE and a Pseudo Efficacy Models and Using Optional Samples.", "topics": [ "gain", "gain,numeric,ModelTox,missing,Effloglog,missing-method", "gain,numeric,ModelTox,Samples,ModelEff,Samples-method", "gain-ModelTox-Effloglog-noSamples", "gain-ModelTox-ModelEff" ] }, { "page": "GeneralData-class", "title": "'GeneralData'", "topics": [ ".DefaultDataGeneral", ".GeneralData", "GeneralData", "GeneralData-class" ] }, { "page": "GeneralModel-class", "title": "'GeneralModel'", "topics": [ ".DefaultGeneralModel", ".GeneralModel", "GeneralModel", "GeneralModel-class" ] }, { "page": "GeneralSimulations-class", "title": "'GeneralSimulations'", "topics": [ ".DefaultGeneralSimulations", ".GeneralSimulations", "GeneralSimulations", "GeneralSimulations-class" ] }, { "page": "GeneralSimulationsSummary-class", "title": "'GeneralSimulationsSummary'", "topics": [ ".DefaultGeneralSimulationsSummary", ".GeneralSimulationsSummary", "GeneralSimulationsSummary", "GeneralSimulationsSummary-class" ] }, { "page": "get-Samples-character-method", "title": "Get specific parameter samples and produce a data.frame", "topics": [ "get,Samples,character-method" ] }, { "page": "getEff", "title": "Extracting Efficacy Responses for Subjects Categorized by the DLT", "topics": [ "getEff", "getEff,DataDual-method", "getEff-DataDual" ] }, { "page": "h_all_equivalent", "title": "Comparison with Numerical Tolerance and Without Name Comparison", "topics": [ "h_all_equivalent" ] }, { "page": "h_blind_plot_data", "title": "Helper Function to Blind Plot Data", "topics": [ "h_blind_plot_data" ] }, { "page": "h_calc_report_label_percentage", "title": "Helper function to calculate percentage of true stopping rules for report label output calculates true column means and converts output into percentages before combining the output with the report label; output is passed to 'show()' and output with cat to console", "topics": [ "h_calc_report_label_percentage" ] }, { "page": "h_check_fun_formals", "title": "Checking Formals of a Function", "topics": [ "h_check_fun_formals" ] }, { "page": "h_convert_ordinal_data", "title": "Convert a Ordinal Data to the Equivalent Binary Data for a Specific Grade", "topics": [ "h_convert_ordinal_data" ] }, { "page": "h_convert_ordinal_model", "title": "Convert an ordinal CRM model to the Equivalent Binary CRM Model for a Specific Grade", "topics": [ "h_convert_ordinal_model" ] }, { "page": "h_convert_ordinal_samples", "title": "Convert a Samples Object from an ordinal Model to the Equivalent Samples Object from a Binary Model", "topics": [ "h_convert_ordinal_samples" ] }, { "page": "h_default_if_empty", "title": "Getting the default value for an empty object", "topics": [ "h_default_if_empty" ] }, { "page": "h_find_interval", "title": "Find Interval Numbers or Indices and Return Custom Number For 0.", "topics": [ "h_find_interval" ] }, { "page": "h_format_number", "title": "Conditional Formatting Using C-style Formats", "topics": [ "h_format_number" ] }, { "page": "h_in_range", "title": "Check which elements are in a given range", "topics": [ "h_in_range" ] }, { "page": "h_info_theory_dist", "title": "Calculating the Information Theoretic Distance", "topics": [ "h_info_theory_dist" ] }, { "page": "h_is_positive_definite", "title": "Testing Matrix for Positive Definiteness", "topics": [ "h_is_positive_definite" ] }, { "page": "h_jags_add_dummy", "title": "Appending a Dummy Number for Selected Slots in Data", "topics": [ "h_jags_add_dummy" ] }, { "page": "h_jags_extract_samples", "title": "Extracting Samples from 'JAGS' 'mcarray' Object", "topics": [ "h_jags_extract_samples" ] }, { "page": "h_jags_get_data", "title": "Getting Data for 'JAGS'", "topics": [ "h_jags_get_data" ] }, { "page": "h_jags_get_model_inits", "title": "Setting Initial Values for 'JAGS' Model Parameters", "topics": [ "h_jags_get_model_inits" ] }, { "page": "h_jags_join_models", "title": "Joining 'JAGS' Models", "topics": [ "h_jags_join_models" ] }, { "page": "h_jags_write_model", "title": "Writing JAGS Model to a File", "topics": [ "h_jags_write_model" ] }, { "page": "h_model_dual_endpoint_beta", "title": "Update certain components of 'DualEndpoint' model with regard to parameters of the function that models dose-biomarker relationship defined in the 'DualEndpointBeta' class.", "topics": [ "h_model_dual_endpoint_beta" ] }, { "page": "h_model_dual_endpoint_rho", "title": "Update 'DualEndpoint' class model components with regard to DLT and biomarker correlation.", "topics": [ "h_model_dual_endpoint_rho" ] }, { "page": "h_model_dual_endpoint_sigma2betaw", "title": "Update certain components of 'DualEndpoint' model with regard to prior variance factor of the random walk.", "topics": [ "h_model_dual_endpoint_sigma2betaw" ] }, { "page": "h_model_dual_endpoint_sigma2w", "title": "Update 'DualEndpoint' class model components with regard to biomarker regression variance.", "topics": [ "h_model_dual_endpoint_sigma2w" ] }, { "page": "h_next_best_eligible_doses", "title": "Get Eligible Doses from the Dose Grid.", "topics": [ "h_next_best_eligible_doses" ] }, { "page": "h_next_best_mg_ci", "title": "Credibility Intervals for Max Gain and Target Doses at 'nextBest-NextBestMaxGain' Method.", "topics": [ "h_next_best_mg_ci" ] }, { "page": "h_next_best_mg_doses_at_grid", "title": "Get Closest Grid Doses for a Given Target Doses for 'nextBest-NextBestMaxGain' Method.", "topics": [ "h_next_best_mg_doses_at_grid" ] }, { "page": "h_next_best_mg_plot", "title": "Building the Plot for 'nextBest-NextBestMaxGain' Method.", "topics": [ "h_next_best_mg_plot" ] }, { "page": "h_next_best_mgsamples_plot", "title": "Building the Plot for 'nextBest-NextBestMaxGainSamples' Method.", "topics": [ "h_next_best_mgsamples_plot" ] }, { "page": "h_next_best_ncrm_loss_plot", "title": "Building the Plot for 'nextBest-NextBestNCRMLoss' Method.", "topics": [ "h_next_best_ncrm_loss_plot" ] }, { "page": "h_next_best_td_plot", "title": "Building the Plot for 'nextBest-NextBestTD' Method.", "topics": [ "h_next_best_td_plot" ] }, { "page": "h_next_best_tdsamples_plot", "title": "Building the Plot for 'nextBest-NextBestTDsamples' Method.", "topics": [ "h_next_best_tdsamples_plot" ] }, { "page": "h_null_if_na", "title": "Getting 'NULL' for 'NA'", "topics": [ "h_null_if_na" ] }, { "page": "h_obtain_dose_grid_range", "title": "Helper Function Containing Common Functionality", "topics": [ "h_obtain_dose_grid_range" ] }, { "page": "h_plot_data_cohort_lines", "title": "Preparing Cohort Lines for Data Plot", "topics": [ "h_plot_data_cohort_lines" ] }, { "page": "plot-Data", "title": "Helper Function for the Plot Method of the Data and DataOrdinal Classes", "topics": [ "h_plot_data_dataordinal", "plot,Data,missing-method", "plot,DataOrdinal,missing-method", "plot-Data" ] }, { "page": "h_plot_data_df", "title": "Preparing Data for Plotting", "topics": [ "h_plot_data_df", "h_plot_data_df,Data-method", "h_plot_data_df,DataOrdinal-method" ] }, { "page": "h_rapply", "title": "Recursively Apply a Function to a List", "topics": [ "h_rapply" ] }, { "page": "h_slots", "title": "Getting the Slots from a S4 Object", "topics": [ "h_slots" ] }, { "page": "h_summarize_add_stats", "title": "Helper function to calculate average across iterations for each additional reporting parameter extracts parameter names as specified by user and averaged the values for each specified parameter to 'show()' and output with cat to console", "topics": [ "h_summarize_add_stats" ] }, { "page": "h_test_named_numeric", "title": "Check that an argument is a named vector of type numeric", "topics": [ "h_test_named_numeric" ] }, { "page": "h_unpack_stopit", "title": "Helper function to recursively unpack stopping rules and return lists with logical value and label given", "topics": [ "h_unpack_stopit" ] }, { "page": "h_validate_combine_results", "title": "Combining S4 Class Validation Results", "topics": [ "h_validate_combine_results" ] }, { "page": "h_validate_common_data_slots", "title": "Helper Function performing validation Common to Data and DataOrdinal", "topics": [ "h_validate_common_data_slots" ] }, { "page": "Increments-class", "title": "'Increments'", "topics": [ ".DefaultIncrements", "Increments", "Increments-class" ] }, { "page": "IncrementsDoseLevels-class", "title": "'IncrementsDoseLevels'", "topics": [ ".DefaultIncrementsDoseLevels", ".IncrementsDoseLevels", "IncrementsDoseLevels", "IncrementsDoseLevels-class" ] }, { "page": "IncrementsHSRBeta-class", "title": "'IncrementsHSRBeta'", "topics": [ ".DefaultIncrementsHSRBeta", ".IncrementsHSRBeta", "IncrementsHSRBeta", "IncrementsHSRBeta-class" ] }, { "page": "IncrementsMaxToxProb-class", "title": "'IncrementsMaxToxProb'", "topics": [ ".DefaultIncrementsMaxToxProb", ".IncrementsMaxToxProb", "IncrementsMaxToxProb", "IncrementsMaxToxProb-class" ] }, { "page": "IncrementsMin-class", "title": "'IncrementsMin'", "topics": [ ".DefaultIncrementsMin", ".IncrementsMin", "IncrementsMin", "IncrementsMin-class" ] }, { "page": "IncrementsOrdinal-class", "title": "'IncrementsOrdinal'", "topics": [ ".DefaultIncrementsOrdinal", ".IncrementsOrdinal", "IncrementsOrdinal", "IncrementsOrdinal-class" ] }, { "page": "IncrementsRelative-class", "title": "'IncrementsRelative'", "topics": [ ".DefaultIncrementsRelative", ".IncrementsRelative", "IncrementsRelative", "IncrementsRelative-class" ] }, { "page": "IncrementsRelativeDLT-class", "title": "'IncrementsRelativeDLT'", "topics": [ ".DefaultIncrementsRelativeDLT", ".IncrementsRelativeDLT", "IncrementsRelativeDLT", "IncrementsRelativeDLT-class" ] }, { "page": "IncrementsRelativeDLTCurrent-class", "title": "'IncrementsRelativeDLTCurrent'", "topics": [ ".DefaultIncrementsRelativeDLTCurrent", ".IncrementsRelativeDLTCurrent", "IncrementsRelativeDLTCurrent", "IncrementsRelativeDLTCurrent-class" ] }, { "page": "IncrementsRelativeParts-class", "title": "'IncrementsRelativeParts'", "topics": [ ".DefaultIncrementsRelativeParts", ".IncrementsRelativeParts", "IncrementsRelativeParts", "IncrementsRelativeParts-class" ] }, { "page": "knit_print", "title": "Render a 'CohortSizeConst' Object", "topics": [ "knit_print", "knit_print.Backfill", "knit_print.CohortSizeConst", "knit_print.CohortSizeDLT", "knit_print.CohortSizeMax", "knit_print.CohortSizeMin", "knit_print.CohortSizeOrdinal", "knit_print.CohortSizeParts", "knit_print.CohortSizeRandom", "knit_print.CohortSizeRange", "knit_print.DADesign", "knit_print.DataParts", "knit_print.Design", "knit_print.DesignGrouped", "knit_print.DesignOrdinal", "knit_print.DualDesign", "knit_print.DualEndpoint", "knit_print.DualResponsesDesign", "knit_print.DualResponsesSamplesDesign", "knit_print.Effloglog", "knit_print.GeneralData", "knit_print.GeneralModel", "knit_print.IncrementsDoseLevels", "knit_print.IncrementsHSRBeta", "knit_print.IncrementsMin", "knit_print.IncrementsOrdinal", "knit_print.IncrementsRelative", "knit_print.IncrementsRelativeDLT", "knit_print.IncrementsRelativeDLTCurrent", "knit_print.IncrementsRelativeParts", "knit_print.LogisticIndepBeta", "knit_print.LogisticKadane", "knit_print.LogisticKadaneBetaGamma", "knit_print.LogisticLogNormal", "knit_print.LogisticLogNormalGrouped", "knit_print.LogisticLogNormalMixture", "knit_print.LogisticLogNormalOrdinal", "knit_print.LogisticLogNormalSub", "knit_print.LogisticNormalFixedMixture", "knit_print.LogisticNormalMixture", "knit_print.ModelParamsNormal", "knit_print.NextBestDualEndpoint", "knit_print.NextBestInfTheory", "knit_print.NextBestMaxGain", "knit_print.NextBestMaxGainSamples", "knit_print.NextBestMinDist", "knit_print.NextBestMTD", "knit_print.NextBestNCRM", "knit_print.NextBestNCRMLoss", "knit_print.NextBestOrdinal", "knit_print.NextBestProbMTDLTE", "knit_print.NextBestProbMTDMinDist", "knit_print.NextBestTD", "knit_print.NextBestTDsamples", "knit_print.NextBestThreePlusThree", "knit_print.OneParExpPrior", "knit_print.OneParLogNormalPrior", "knit_print.OpeningAll", "knit_print.OpeningAny", "knit_print.OpeningList", "knit_print.OpeningMinCohorts", "knit_print.OpeningMinDose", "knit_print.OpeningMinResponses", "knit_print.OpeningNone", "knit_print.RecruitmentRatio", "knit_print.RecruitmentUnlimited", "knit_print.RuleDesign", "knit_print.RuleDesignOrdinal", "knit_print.SafetyWindow", "knit_print.SafetyWindowConst", "knit_print.SafetyWindowSize", "knit_print.StartingDose", "knit_print.StoppingAll", "knit_print.StoppingAny", "knit_print.StoppingCohortsNearDose", "knit_print.StoppingHighestDose", "knit_print.StoppingList", "knit_print.StoppingLowestDoseHSRBeta", "knit_print.StoppingMaxGainCIRatio", "knit_print.StoppingMinCohorts", "knit_print.StoppingMinPatients", "knit_print.StoppingMissingDose", "knit_print.StoppingMTDCV", "knit_print.StoppingMTDdistribution", "knit_print.StoppingOrdinal", "knit_print.StoppingPatientsNearDose", "knit_print.StoppingSpecificDose", "knit_print.StoppingTargetBiomarker", "knit_print.StoppingTargetProb", "knit_print.StoppingTDCIRatio", "knit_print.TDDesign", "knit_print.TDsamplesDesign" ] }, { "page": "LogisticIndepBeta-class", "title": "'LogisticIndepBeta'", "topics": [ ".DefaultLogisticIndepBeta", ".LogisticIndepBeta", "LogisticIndepBeta", "LogisticIndepBeta-class" ] }, { "page": "LogisticKadane-class", "title": "'LogisticKadane'", "topics": [ ".DefaultLogisticKadane", ".LogisticKadane", "LogisticKadane", "LogisticKadane-class" ] }, { "page": "LogisticKadaneBetaGamma-class", "title": "'LogisticKadaneBetaGamma'", "topics": [ ".DefaultLogisticKadaneBetaGamma", ".LogisticKadaneBetaGamma", "LogisticKadaneBetaGamma", "LogisticKadaneBetaGamma-class" ] }, { "page": "LogisticLogNormal-class", "title": "'LogisticLogNormal'", "topics": [ ".DefaultLogisticLogNormal", ".LogisticLogNormal", "LogisticLogNormal", "LogisticLogNormal-class" ] }, { "page": "LogisticLogNormalGrouped-class", "title": "'LogisticLogNormalGrouped'", "topics": [ ".DefaultLogisticLogNormalGrouped", ".LogisticLogNormalGrouped", "LogisticLogNormalGrouped", "LogisticLogNormalGrouped-class" ] }, { "page": "LogisticLogNormalMixture-class", "title": "'LogisticLogNormalMixture'", "topics": [ ".DefaultLogisticLogNormalMixture", ".LogisticLogNormalMixture", "LogisticLogNormalMixture", "LogisticLogNormalMixture-class" ] }, { "page": "LogisticLogNormalOrdinal-class", "title": "'LogisticLogNormalOrdinal'", "topics": [ ".DefaultLogisticLogNormalOrdinal", ".LogisticLogNormalOrdinal", "LogisticLogNormalOrdinal", "LogisticLogNormalOrdinal-class" ] }, { "page": "LogisticLogNormalSub-class", "title": "'LogisticLogNormalSub'", "topics": [ ".DefaultLogisticLogNormalSub", ".LogisticLogNormalSub", "LogisticLogNormalSub", "LogisticLogNormalSub-class" ] }, { "page": "LogisticNormal-class", "title": "'LogisticNormal'", "topics": [ ".DefaultLogisticNormal", ".LogisticNormal", "LogisticNormal", "LogisticNormal-class" ] }, { "page": "LogisticNormalFixedMixture-class", "title": "'LogisticNormalFixedMixture'", "topics": [ ".DefaultLogisticNormalFixedMixture", ".LogisticNormalFixedMixture", "LogisticNormalFixedMixture", "LogisticNormalFixedMixture-class" ] }, { "page": "LogisticNormalMixture-class", "title": "'LogisticNormalMixture'", "topics": [ ".DefaultLogisticNormalMixture", ".LogisticNormalMixture", "LogisticNormalMixture", "LogisticNormalMixture-class" ] }, { "page": "logit", "title": "Shorthand for Logit Function", "topics": [ "logit" ] }, { "page": "match_within_tolerance", "title": "Helper Function for Value Matching with Tolerance", "topics": [ "match_within_tolerance" ] }, { "page": "maxDose", "title": "Determine the Maximum Possible Next Dose", "topics": [ "maxDose", "maxDose,IncrementsDoseLevels,Data-method", "maxDose,IncrementsHSRBeta,Data-method", "maxDose,IncrementsMaxToxProb,Data-method", "maxDose,IncrementsMaxToxProb,DataOrdinal-method", "maxDose,IncrementsMin,Data-method", "maxDose,IncrementsMin,DataOrdinal-method", "maxDose,IncrementsOrdinal,DataOrdinal-method", "maxDose,IncrementsRelative,Data-method", "maxDose,IncrementsRelativeDLT,Data-method", "maxDose,IncrementsRelativeDLTCurrent,Data-method", "maxDose,IncrementsRelativeParts,DataParts-method", "maxDose-IncrementsDoseLevels", "maxDose-IncrementsHSRBeta", "maxDose-IncrementsMaxToxProb", "maxDose-IncrementsMin", "maxDose-IncrementsOrdinal", "maxDose-IncrementsRelative", "maxDose-IncrementsRelativeDLT", "maxDose-IncrementsRelativeDLTCurrent", "maxDose-IncrementsRelativeParts" ] }, { "page": "maxRecruits", "title": "Calculate Maximum Number of Backfill Patients", "topics": [ "maxRecruits", "maxRecruits,RecruitmentRatio-method", "maxRecruits,RecruitmentUnlimited-method", "maxRecruits-RecruitmentRatio", "maxRecruits-RecruitmentUnlimited" ] }, { "page": "maxSize", "title": "\"MAX\" Combination of Cohort Size Rules", "topics": [ "maxSize", "maxSize,CohortSize-method", "maxSize-CohortSize" ] }, { "page": "mcmc", "title": "Obtaining Posterior Samples for all Model Parameters", "topics": [ "mcmc", "mcmc,Data,LogisticIndepBeta,McmcOptions-method", "mcmc,DataDual,EffFlexi,McmcOptions-method", "mcmc,DataDual,Effloglog,McmcOptions-method", "mcmc,DataMixture,GeneralModel,McmcOptions-method", "mcmc,DataOrdinal,LogisticLogNormalOrdinal,McmcOptions-method", "mcmc,GeneralData,DualEndpointBeta,McmcOptions-method", "mcmc,GeneralData,DualEndpointEmax,McmcOptions-method", "mcmc,GeneralData,DualEndpointRW,McmcOptions-method", "mcmc,GeneralData,GeneralModel,McmcOptions-method", "mcmc,GeneralData,OneParExpPrior,McmcOptions-method", "mcmc,GeneralData,OneParLogNormalPrior,McmcOptions-method", "mcmc-Data-LogisticIndepBeta", "mcmc-DataDual-EffFlexi", "mcmc-DataDual-Effloglog", "mcmc-DataMixture", "mcmc-DataOrdinal-LogisticLogNormalOrdinal", "mcmc-GeneralData", "mcmc-GeneralData-DualEndpointBeta", "mcmc-GeneralData-DualEndpointEmax", "mcmc-GeneralData-DualEndpointRW", "mcmc-GeneralData-OneParExpPrior", "mcmc-GeneralData-OneParLogNormalPrior" ] }, { "page": "McmcOptions-class", "title": "'McmcOptions'", "topics": [ ".DefaultMcmcOptions", ".McmcOptions", "McmcOptions", "McmcOptions-class" ] }, { "page": "MinimalInformative", "title": "Construct a Minimally Informative Prior", "topics": [ "MinimalInformative" ] }, { "page": "minSize", "title": "\"MIN\" Combination of Cohort Size Rules", "topics": [ "minSize", "minSize,CohortSize-method", "minSize-CohortSize" ] }, { "page": "ModelEff-class", "title": "'ModelEff'", "topics": [ ".DefaultModelEff", ".ModelEff", "ModelEff", "ModelEff-class" ] }, { "page": "ModelLogNormal-class", "title": "'ModelLogNormal'", "topics": [ ".DefaultModelLogNormal", ".ModelLogNormal", "ModelLogNormal", "ModelLogNormal-class" ] }, { "page": "ModelParamsNormal-class", "title": "'ModelParamsNormal'", "topics": [ ".DefaultModelParamsNormal", ".ModelParamsNormal", "ModelParamsNormal", "ModelParamsNormal-class" ] }, { "page": "ModelPseudo-class", "title": "'ModelPseudo'", "topics": [ ".DefaultModelPseudo", ".ModelPseudo", "ModelPseudo", "ModelPseudo-class" ] }, { "page": "ModelTox-class", "title": "'ModelTox'", "topics": [ ".DefaultModelTox", ".ModelTox", "ModelTox", "ModelTox-class" ] }, { "page": "names-Samples-method", "title": "The Names of the Sampled Parameters", "topics": [ "names,Samples-method", "names-Samples" ] }, { "page": "nextBest", "title": "Finding the Next Best Dose", "topics": [ "nextBest", "nextBest,NextBestDualEndpoint,numeric,Samples,DualEndpoint,Data-method", "nextBest,NextBestEWOC,numeric,Samples,GeneralModel,Data-method", "nextBest,NextBestInfTheory,numeric,Samples,GeneralModel,Data-method", "nextBest,NextBestMaxGain,numeric,missing,ModelTox,DataDual-method", "nextBest,NextBestMaxGainSamples,numeric,Samples,ModelTox,DataDual-method", "nextBest,NextBestMinDist,numeric,Samples,GeneralModel,Data-method", "nextBest,NextBestMTD,numeric,Samples,GeneralModel,Data-method", "nextBest,NextBestNCRM,numeric,Samples,GeneralModel,Data-method", "nextBest,NextBestNCRM,numeric,Samples,GeneralModel,DataParts-method", "nextBest,NextBestNCRMLoss,numeric,Samples,GeneralModel,Data-method", "nextBest,NextBestOrdinal,numeric,Samples,GeneralModel,Data-method", "nextBest,NextBestOrdinal,numeric,Samples,LogisticLogNormalOrdinal,DataOrdinal-method", "nextBest,NextBestProbMTDLTE,numeric,Samples,GeneralModel,Data-method", "nextBest,NextBestProbMTDMinDist,numeric,Samples,GeneralModel,Data-method", "nextBest,NextBestTD,numeric,missing,LogisticIndepBeta,Data-method", "nextBest,NextBestTDsamples,numeric,Samples,LogisticIndepBeta,Data-method", "nextBest,NextBestThreePlusThree,missing,missing,missing,Data-method", "nextBest-NextBestDualEndpoint", "nextBest-NextBestEWOC", "nextBest-NextBestInfTheory", "nextBest-NextBestMaxGain", "nextBest-NextBestMaxGainSamples", "nextBest-NextBestMinDist", "nextBest-NextBestMTD", "nextBest-NextBestNCRM", "nextBest-NextBestNCRM-DataParts", "nextBest-NextBestNCRMLoss", "nextBest-NextBestOrdinal", "nextBest-NextBestProbMTDLTE", "nextBest-NextBestProbMTDMinDist", "nextBest-NextBestTD", "nextBest-NextBestTDsamples", "nextBest-NextBestThreePlusThree" ] }, { "page": "NextBest-class", "title": "'NextBest'", "topics": [ ".DefaultNextBest", "NextBest", "NextBest-class" ] }, { "page": "NextBestDualEndpoint-class", "title": "'NextBestDualEndpoint'", "topics": [ ".DefaultNextBestDualEndpoint", ".NextBestDualEndpoint", "NextBestDualEndpoint", "NextBestDualEndpoint-class" ] }, { "page": "NextBestEWOC-class", "title": "'NextBestEWOC'", "topics": [ ".DefaultNextBestEWOC", ".NextBestEWOC", "NextBestEWOC", "NextBestEWOC-class" ] }, { "page": "NextBestInfTheory-class", "title": "'NextBestInfTheory'", "topics": [ ".DefaultNextBestInfTheory", ".NextBestInfTheory", "NextBestInfTheory", "NextBestInfTheory-class" ] }, { "page": "NextBestMaxGain-class", "title": "'NextBestMaxGain'", "topics": [ ".DefaultNextBestMaxGain", ".NextBestMaxGain", "NextBestMaxGain", "NextBestMaxGain-class" ] }, { "page": "NextBestMaxGainSamples-class", "title": "'NextBestMaxGainSamples'", "topics": [ ".DefaultNextBestMaxGainSamples", ".NextBestMaxGainSamples", "NextBestMaxGainSamples", "NextBestMaxGainSamples-class" ] }, { "page": "NextBestMinDist-class", "title": "'NextBestMinDist'", "topics": [ ".DefaultNextBestMinDist", ".NextBestMinDist", "NextBestMinDist", "NextBestMinDist-class" ] }, { "page": "NextBestMTD-class", "title": "'NextBestMTD'", "topics": [ ".DefaultNextBestMTD", ".NextBestMTD", "NextBestMTD", "NextBestMTD-class" ] }, { "page": "NextBestNCRM-class", "title": "'NextBestNCRM'", "topics": [ ".DefaultNextBestNCRM", ".NextBestNCRM", "NextBestNCRM", "NextBestNCRM-class" ] }, { "page": "NextBestNCRMLoss-class", "title": "'NextBestNCRMLoss'", "topics": [ ".DefaultNextBestNCRMLoss", ".NextBestNCRMLoss", "NextBestNCRMLoss", "NextBestNCRMLoss-class" ] }, { "page": "NextBestOrdinal-class", "title": "'NextBestOrdinal'", "topics": [ ".DefaultNextBestOrdinal", ".NextBestOrdinal", "NextBestOrdinal", "NextBestOrdinal-class" ] }, { "page": "NextBestProbMTDLTE-class", "title": "'NextBestProbMTDLTE'", "topics": [ ".DefaultNextBestProbMTDLTE", ".NextBestProbMTDLTE", "NextBestProbMTDLTE", "NextBestProbMTDLTE-class" ] }, { "page": "NextBestProbMTDMinDist-class", "title": "'NextBestProbMTDMinDist'", "topics": [ ".DefaultNextBestProbMTDMinDist", ".NextBestProbMTDMinDist", "NextBestProbMTDMinDist", "NextBestProbMTDMinDist-class" ] }, { "page": "NextBestTD-class", "title": "'NextBestTD'", "topics": [ ".DefaultNextBestTD", ".NextBestTD", "NextBestTD", "NextBestTD-class" ] }, { "page": "NextBestTDsamples-class", "title": "'NextBestTDsamples'", "topics": [ ".DefaultNextBestTDsamples", ".NextBestTDsamples", "NextBestTDsamples", "NextBestTDsamples-class" ] }, { "page": "NextBestThreePlusThree-class", "title": "'NextBestThreePlusThree'", "topics": [ ".DefaultNextBestThreePlusThree", ".NextBestThreePlusThree", "NextBestThreePlusThree", "NextBestThreePlusThree-class" ] }, { "page": "ngrid", "title": "Number of Doses in Grid", "topics": [ "ngrid", "ngrid,Data-method", "ngrid-Data" ] }, { "page": "OneParExpPrior-class", "title": "'OneParExpPrior'", "topics": [ ".DefaultOneParExpPrior", ".OneParExpPrior", "OneParExpPrior", "OneParExpPrior-class" ] }, { "page": "OneParLogNormalPrior-class", "title": "'OneParLogNormalPrior'", "topics": [ ".DefaultOneParLogNormalPrior", ".OneParLogNormalPrior", "OneParLogNormalPrior", "OneParLogNormalPrior-class" ] }, { "page": "openCohort", "title": "Open / recruit backfill patients into a cohort?", "topics": [ "openCohort", "openCohort,OpeningAll-method", "openCohort,OpeningAny-method", "openCohort,OpeningList-method", "openCohort,OpeningMinCohorts-method", "openCohort,OpeningMinDose-method", "openCohort,OpeningMinResponses-method", "openCohort,OpeningNone-method", "openCohort-OpeningAll", "openCohort-OpeningAny", "openCohort-OpeningList", "openCohort-OpeningMinCohorts", "openCohort-OpeningMinDose", "openCohort-OpeningMinResponses", "openCohort-OpeningNone" ] }, { "page": "Opening-class", "title": "'Opening'", "topics": [ ".DefaultOpening", ".Opening", "Opening", "Opening-class" ] }, { "page": "OpeningAll-class", "title": "'OpeningAll'", "topics": [ ".DefaultOpeningAll", ".OpeningAll", "OpeningAll", "OpeningAll-class" ] }, { "page": "OpeningAny-class", "title": "'OpeningAny'", "topics": [ ".DefaultOpeningAny", ".OpeningAny", "OpeningAny", "OpeningAny-class" ] }, { "page": "OpeningList-class", "title": "'OpeningList'", "topics": [ ".DefaultOpeningList", ".OpeningList", "OpeningList", "OpeningList-class" ] }, { "page": "OpeningMinCohorts-class", "title": "'OpeningMinCohorts'", "topics": [ ".DefaultOpeningMinCohorts", ".OpeningMinCohorts", "OpeningMinCohorts", "OpeningMinCohorts-class" ] }, { "page": "OpeningMinDose-class", "title": "'OpeningMinDose'", "topics": [ ".DefaultOpeningMinDose", ".OpeningMinDose", "OpeningMinDose", "OpeningMinDose-class" ] }, { "page": "OpeningMinResponses-class", "title": "'OpeningMinResponses'", "topics": [ ".DefaultOpeningMinResponses", ".OpeningMinResponses", "OpeningMinResponses", "OpeningMinResponses-class" ] }, { "page": "OpeningNone-class", "title": "'OpeningNone'", "topics": [ ".DefaultOpeningNone", ".OpeningNone", "OpeningNone", "OpeningNone-class" ] }, { "page": "or-Stopping-Stopping", "title": "Combine Two Stopping Rules with OR", "topics": [ "or-Stopping-Stopping", "|,Stopping,Stopping-method" ] }, { "page": "or-Stopping-StoppingAny", "title": "Combine an Atomic Stopping Rule and a Stopping List with OR", "topics": [ "or-Stopping-StoppingAny", "|,Stopping,StoppingAny-method" ] }, { "page": "or-StoppingAny-Stopping", "title": "Combine a Stopping List and an Atomic Stopping Rule with OR", "topics": [ "or-StoppingAny-Stopping", "|,StoppingAny,Stopping-method" ] }, { "page": "or-Opening-Opening-method", "title": "Logical OR Operator for Opening Objects", "topics": [ "or,Opening,Opening-method", "|,Opening,Opening-method" ] }, { "page": "plot-Data-ModelTox-method", "title": "Plot of the fitted dose-tox based with a given pseudo DLE model and data without samples", "topics": [ "plot,Data,ModelTox-method" ] }, { "page": "plot-DataDA-missing-method", "title": "Plot Method for the 'DataDA' Class", "topics": [ "plot,DataDA,missing-method", "plot-DataDA" ] }, { "page": "plot-DataDual-missing-method", "title": "Plot Method for the 'DataDual' Class", "topics": [ "plot,DataDual,missing-method", "plot-DataDual" ] }, { "page": "plot-DataDual-ModelEff-method", "title": "Plot of the fitted dose-efficacy based with a given pseudo efficacy model and data without samples", "topics": [ "plot,DataDual,ModelEff-method" ] }, { "page": "plot-DualSimulations-missing-method", "title": "Plot 'DualSimulations'", "topics": [ "plot,DualSimulations,missing-method", "plot-DualSimulations-missing" ] }, { "page": "plot-DualSimulationsSummary-missing-method", "title": "Plot Dual-Endpoint Design Simulation Summary", "topics": [ "plot,DualSimulationsSummary,missing-method", "plot-DualSimulationsSummary-missing" ] }, { "page": "plot-GeneralSimulations-missing-method", "title": "Plot 'GeneralSimulations'", "topics": [ "plot,GeneralSimulations,missing-method", "plot-GeneralSimulations-missing" ] }, { "page": "plot-GeneralSimulationsSummary-missing-method", "title": "Plot 'GeneralSimulationsSummary'", "topics": [ "plot,GeneralSimulationsSummary,missing-method", "plot-GeneralSimulationsSummary-missing" ] }, { "page": "plot-PseudoDualFlexiSimulations-missing-method", "title": "Plot 'PseudoDualFlexiSimulations'", "topics": [ "plot,PseudoDualFlexiSimulations,missing-method", "plot-PseudoDualFlexiSimulations-missing" ] }, { "page": "plot-PseudoDualSimulations-missing-method", "title": "Plot 'PseudoDualSimulations'", "topics": [ "plot,PseudoDualSimulations,missing-method", "plot-PseudoDualSimulations-missing" ] }, { "page": "plot-PseudoDualSimulationsSummary-missing-method", "title": "Plot 'PseudoDualSimulationsSummary'", "topics": [ "plot,PseudoDualSimulationsSummary,missing-method", "plot-PseudoDualSimulationsSummary-missing" ] }, { "page": "plot-PseudoSimulationsSummary-missing-method", "title": "Plot 'PseudoSimulationsSummary'", "topics": [ "plot,PseudoSimulationsSummary,missing-method", "plot-PseudoSimulationsSummary-missing" ] }, { "page": "plot-Samples-DALogisticLogNormal-method", "title": "Plotting dose-toxicity model fits", "topics": [ "plot,Samples,DALogisticLogNormal-method" ] }, { "page": "plot-Samples-DualEndpoint-method", "title": "Plotting dose-toxicity and dose-biomarker model fits", "topics": [ "plot,Samples,DualEndpoint-method" ] }, { "page": "plot-Samples-GeneralModel-method", "title": "Plotting dose-toxicity model fits", "topics": [ "plot,Samples,GeneralModel-method" ] }, { "page": "plot-Samples-ModelEff-method", "title": "Plot the fitted dose-efficacy curve using a model from 'ModelEff' class with samples", "topics": [ "plot,Samples,ModelEff-method" ] }, { "page": "plot-Samples-ModelTox-method", "title": "Plot the fitted dose-DLE curve using a 'ModelTox' class model with samples", "topics": [ "plot,Samples,ModelTox-method" ] }, { "page": "plot-SimulationsSummary-missing-method", "title": "Plot Model-Based Design Simulation Summary", "topics": [ "plot,SimulationsSummary,missing-method", "plot-SimulationsSummary-missing" ] }, { "page": "plot.gtable", "title": "Plot 'gtable' Objects", "topics": [ "plot.gtable", "print.gtable" ] }, { "page": "plotDualResponses", "title": "Plot of the DLE and efficacy curve side by side given a DLE pseudo model, a DLE sample, an efficacy pseudo model and a given efficacy sample", "topics": [ "plotDualResponses", "plotDualResponses,ModelTox,missing,ModelEff,missing-method", "plotDualResponses,ModelTox,Samples,ModelEff,Samples-method" ] }, { "page": "plotGain", "title": "Plot the gain curve in addition with the dose-DLE and dose-efficacy curve using a given DLE pseudo model, a DLE sample, a given efficacy pseudo model and an efficacy sample", "topics": [ "plotGain", "plotGain,ModelTox,missing,ModelEff,missing-method", "plotGain,ModelTox,Samples,ModelEff,Samples-method" ] }, { "page": "positive_number", "title": "'positive_number'", "topics": [ "positive_number" ] }, { "page": "prob", "title": "Computing Toxicity Probabilities for a Given Dose, Model and Samples", "topics": [ "prob", "prob,numeric,DualEndpoint,Samples-method", "prob,numeric,LogisticIndepBeta,missing-method", "prob,numeric,LogisticIndepBeta,Samples-method", "prob,numeric,LogisticKadane,Samples-method", "prob,numeric,LogisticKadaneBetaGamma,Samples-method", "prob,numeric,LogisticLogNormal,Samples-method", "prob,numeric,LogisticLogNormalGrouped,Samples-method", "prob,numeric,LogisticLogNormalMixture,Samples-method", "prob,numeric,LogisticLogNormalOrdinal,Samples-method", "prob,numeric,LogisticLogNormalSub,Samples-method", "prob,numeric,LogisticNormal,Samples-method", "prob,numeric,LogisticNormalFixedMixture,Samples-method", "prob,numeric,LogisticNormalMixture,Samples-method", "prob,numeric,OneParExpPrior,Samples-method", "prob,numeric,OneParLogNormalPrior,Samples-method", "prob,numeric,ProbitLogNormal,Samples-method", "prob,numeric,ProbitLogNormalRel,Samples-method", "prob-DualEndpoint", "prob-LogisticIndepBeta", "prob-LogisticIndepBeta-noSamples", "prob-LogisticKadane", "prob-LogisticKadaneBetaGamma", "prob-LogisticLogNormal", "prob-LogisticLogNormalGrouped", "prob-LogisticLogNormalMixture", "prob-LogisticLogNormalOrdinal", "prob-LogisticLogNormalSub", "prob-LogisticNormal", "prob-LogisticNormalFixedMixture", "prob-LogisticNormalMixture", "prob-OneParExpPrior", "prob-OneParLogNormalPrior", "prob-ProbitLogNormal", "prob-ProbitLogNormalRel" ] }, { "page": "probFunction", "title": "Getting the Prob Function for a Given Model Type", "topics": [ "probFunction", "probFunction,GeneralModel-method", "probFunction,LogisticLogNormalOrdinal-method", "probFunction,ModelTox-method", "probFunction-GeneralModel", "probFunction-LogisticLogNormalOrdinal", "probFunction-ModelTox" ] }, { "page": "probit", "title": "Shorthand for Probit Function", "topics": [ "probit" ] }, { "page": "ProbitLogNormal-class", "title": "'ProbitLogNormal'", "topics": [ ".DefaultProbitLogNormal", ".ProbitLogNormal", "ProbitLogNormal", "ProbitLogNormal-class", "ProbitLogNormalLogDose" ] }, { "page": "ProbitLogNormalRel-class", "title": "'ProbitLogNormalRel'", "topics": [ ".DefaultProbitLogNormalRel", ".ProbitLogNormalRel", "ProbitLogNormalRel", "ProbitLogNormalRel-class" ] }, { "page": "PseudoDualFlexiSimulations-class", "title": "'PseudoDualFlexiSimulations'", "topics": [ ".DefaultPseudoDualFlexiSimulations", ".PseudoDualFlexiSimulations", "PseudoDualFlexiSimulations", "PseudoDualFlexiSimulations-class" ] }, { "page": "PseudoDualSimulations-class", "title": "'PseudoDualSimulations'", "topics": [ ".DefaultPseudoDualSimulations", ".PseudoDualSimulations", "PseudoDualSimulations", "PseudoDualSimulations-class" ] }, { "page": "PseudoDualSimulationsSummary-class", "title": "'PseudoDualSimulationsSummary'", "topics": [ ".DefaultPseudoDualSimulationsSummary", ".PseudoDualSimulationsSummary", "PseudoDualSimulationsSummary", "PseudoDualSimulationsSummary-class" ] }, { "page": "PseudoSimulations-class", "title": "'PseudoSimulations'", "topics": [ ".DefaultPseudoSimulations", ".PseudoSimulations", "PseudoSimulations", "PseudoSimulations-class" ] }, { "page": "PseudoSimulationsSummary-class", "title": "'PseudoSimulationsSummary'", "topics": [ ".DefaultPseudoSimulationsSummary", ".PseudoSimulationsSummary", "PseudoSimulationsSummary", "PseudoSimulationsSummary-class" ] }, { "page": "Quantiles2LogisticNormal", "title": "Convert Prior Quantiles to Logistic (Log) Normal Model", "topics": [ "Quantiles2LogisticNormal" ] }, { "page": "Recruitment-class", "title": "'Recruitment'", "topics": [ ".DefaultRecruitment", ".Recruitment", "Recruitment", "Recruitment-class" ] }, { "page": "RecruitmentRatio-class", "title": "'RecruitmentRatio'", "topics": [ ".DefaultRecruitmentRatio", ".RecruitmentRatio", "RecruitmentRatio", "RecruitmentRatio-class" ] }, { "page": "RecruitmentUnlimited-class", "title": "'RecruitmentUnlimited'", "topics": [ ".DefaultRecruitmentUnlimited", ".RecruitmentUnlimited", "RecruitmentUnlimited", "RecruitmentUnlimited-class" ] }, { "page": "RuleDesign-class", "title": "'RuleDesign'", "topics": [ ".DefaultRuleDesign", ".RuleDesign", "RuleDesign", "RuleDesign-class", "ThreePlusThreeDesign" ] }, { "page": "RuleDesignOrdinal-class", "title": "'RuleDesignOrdinal'", "topics": [ ".DefaultRuleDesignOrdinal", ".RuleDesignOrdinal", "RuleDesignOrdinal", "RuleDesignOrdinal-class" ] }, { "page": "SafetyWindow-class", "title": "'SafetyWindow'", "topics": [ ".DefaultSafetyWindow", "SafetyWindow", "SafetyWindow-class" ] }, { "page": "SafetyWindowConst-class", "title": "'SafetyWindowConst'", "topics": [ ".DefaultSafetyWindowConst", ".SafetyWindowConst", "SafetyWindowConst", "SafetyWindowConst-class" ] }, { "page": "SafetyWindowSize-class", "title": "'SafetyWindowSize'", "topics": [ ".DefaultSafetyWindowSize", ".SafetyWindowSize", "SafetyWindowSize", "SafetyWindowSize-class" ] }, { "page": "Samples-class", "title": "'Samples'", "topics": [ ".DefaultSamples", ".Samples", "Samples", "Samples-class" ] }, { "page": "saveSample", "title": "Determining if this Sample Should be Saved", "topics": [ "saveSample", "saveSample,McmcOptions-method", "saveSample-McmcOptions" ] }, { "page": "set_seed", "title": "Helper Function to Set and Save the RNG Seed", "topics": [ "set_seed" ] }, { "page": "show-DualSimulationsSummary-method", "title": "Show the Summary of Dual-Endpoint Simulations", "topics": [ "show,DualSimulationsSummary-method", "show-DualSimulationsSummary" ] }, { "page": "show-GeneralSimulations-method", "title": "Show 'Simulations' Objects", "topics": [ "show,GeneralSimulations-method", "show-GeneralSimulations" ] }, { "page": "show-GeneralSimulationsSummary-method", "title": "Show the Summary of the Simulations", "topics": [ "show,GeneralSimulationsSummary-method", "show-GeneralSimulationsSummary" ] }, { "page": "show-PseudoDualSimulationsSummary-method", "title": "Show the Summary of 'PseudoDualSimulations'", "topics": [ "show,PseudoDualSimulationsSummary-method", "show-PseudoDualSimulationsSummary" ] }, { "page": "show-PseudoSimulationsSummary-method", "title": "Show the Summary of 'PseudoSimulations'", "topics": [ "show,PseudoSimulationsSummary-method", "show-PseudoSimulationsSummary" ] }, { "page": "show-SimulationsSummary-method", "title": "Show the Summary of Model-Based Design Simulations", "topics": [ "show,SimulationsSummary-method", "show-SimulationsSummary" ] }, { "page": "simulate-DADesign-method", "title": "Simulate outcomes from a time-to-DLT augmented CRM design", "topics": [ "simulate,DADesign-method" ] }, { "page": "simulate-Design-method", "title": "Simulate outcomes from a CRM design", "topics": [ "simulate,Design-method" ] }, { "page": "simulate-DesignGrouped-method", "title": "Simulate Method for the 'DesignGrouped' Class", "topics": [ "simulate,DesignGrouped-method", "simulate-DesignGrouped" ] }, { "page": "simulate-DualDesign-method", "title": "Simulate outcomes from a dual-endpoint design", "topics": [ "simulate,DualDesign-method" ] }, { "page": "simulate-DualResponsesDesign-method", "title": "Simulate dose escalation procedure using both DLE and efficacy responses without samples", "topics": [ "simulate,DualResponsesDesign-method" ] }, { "page": "simulate-DualResponsesSamplesDesign-method", "title": "Simulate dose escalation procedure using DLE and efficacy responses with samples", "topics": [ "simulate,DualResponsesSamplesDesign-method" ] }, { "page": "simulate-RuleDesign-method", "title": "Simulate outcomes from a rule-based design", "topics": [ "simulate,RuleDesign-method" ] }, { "page": "simulate-TDDesign-method", "title": "Simulate dose escalation procedure using DLE responses only without samples", "topics": [ "simulate,TDDesign-method" ] }, { "page": "simulate-TDsamplesDesign-method", "title": "Simulate dose escalation procedure using DLE responses only with DLE samples", "topics": [ "simulate,TDsamplesDesign-method" ] }, { "page": "Simulations-class", "title": "'Simulations'", "topics": [ ".DefaultSimulations", ".Simulations", "Simulations", "Simulations-class" ] }, { "page": "SimulationsSummary-class", "title": "'SimulationsSummary'", "topics": [ ".DefaultSimulationsSummary", ".SimulationsSummary", "SimulationsSummary", "SimulationsSummary-class" ] }, { "page": "size", "title": "Size of an Object", "topics": [ "size", "size,CohortSizeConst-method", "size,CohortSizeDLT-method", "size,CohortSizeMax-method", "size,CohortSizeMin-method", "size,CohortSizeOrdinal-method", "size,CohortSizeParts-method", "size,CohortSizeRandom-method", "size,CohortSizeRange-method", "size,McmcOptions-method", "size,Samples-method", "size-CohortSizeConst", "size-CohortSizeDLT", "size-CohortSizeMax", "size-CohortSizeMin", "size-CohortSizeOrdinal", "size-CohortSizeParts", "size-CohortSizeRandom", "size-CohortSizeRange", "size-McmcOptions", "size-Samples" ] }, { "page": "Stopping-class", "title": "'Stopping'", "topics": [ "Stopping", "Stopping-class" ] }, { "page": "StoppingAll-class", "title": "'StoppingAll'", "topics": [ ".DefaultStoppingAll", ".StoppingAll", "StoppingAll", "StoppingAll-class" ] }, { "page": "StoppingAny-class", "title": "'StoppingAny'", "topics": [ ".DefaultStoppingAny", ".StoppingAny", "StoppingAny", "StoppingAny-class" ] }, { "page": "StoppingCohortsNearDose-class", "title": "'StoppingCohortsNearDose'", "topics": [ ".DefaultStoppingCohortsNearDose", ".StoppingCohortsNearDose", "StoppingCohortsNearDose", "StoppingCohortsNearDose-class" ] }, { "page": "StoppingExternal-class", "title": "'StoppingExternal'", "topics": [ ".DefaultStoppingExternal", ".StoppingExternal", "StoppingExternal", "StoppingExternal-class" ] }, { "page": "StoppingHighestDose-class", "title": "'StoppingHighestDose'", "topics": [ ".DefaultStoppingHighestDose", ".StoppingHighestDose", "StoppingHighestDose", "StoppingHighestDose-class" ] }, { "page": "StoppingList-class", "title": "'StoppingList'", "topics": [ ".DefaultStoppingList", ".StoppingList", "StoppingList", "StoppingList-class" ] }, { "page": "StoppingLowestDoseHSRBeta-class", "title": "'StoppingLowestDoseHSRBeta'", "topics": [ ".DefaultStoppingLowestDoseHSRBeta", ".StoppingLowestDoseHSRBeta", "StoppingLowestDoseHSRBeta", "StoppingLowestDoseHSRBeta-class" ] }, { "page": "StoppingMaxGainCIRatio-class", "title": "'StoppingMaxGainCIRatio'", "topics": [ ".DefaultStoppingMaxGainCIRatio", ".StoppingMaxGainCIRatio", "StoppingMaxGainCIRatio", "StoppingMaxGainCIRatio-class" ] }, { "page": "StoppingMinCohorts-class", "title": "'StoppingMinCohorts'", "topics": [ ".DefaultStoppingMinCohorts", ".StoppingMinCohorts", "StoppingMinCohorts", "StoppingMinCohorts-class" ] }, { "page": "StoppingMinPatients-class", "title": "'StoppingMinPatients'", "topics": [ ".DefaultStoppingMinPatients", ".StoppingMinPatients", "StoppingMinPatients", "StoppingMinPatients-class" ] }, { "page": "StoppingMissingDose-class", "title": "'StoppingMissingDose'", "topics": [ ".DefaultStoppingMissingDose", ".StoppingMissingDose", "StoppingMissingDose", "StoppingMissingDose-class" ] }, { "page": "StoppingMTDCV-class", "title": "'StoppingMTDCV'", "topics": [ ".DefaultStoppingMTDCV", ".StoppingMTDCV", "StoppingMTDCV", "StoppingMTDCV-class" ] }, { "page": "StoppingMTDdistribution-class", "title": "'StoppingMTDdistribution'", "topics": [ ".DefaultStoppingMTDdistribution", ".StoppingMTDdistribution", "StoppingMTDdistribution", "StoppingMTDdistribution-class" ] }, { "page": "StoppingOrdinal-class", "title": "'StoppingOrdinal'", "topics": [ ".DefaultStoppingOrdinal", ".StoppingOrdinal", "StoppingOrdinal", "StoppingOrdinal-class" ] }, { "page": "StoppingPatientsNearDose-class", "title": "'StoppingPatientsNearDose'", "topics": [ ".DefaultStoppingPatientsNearDose", ".StoppingPatientsNearDose", "StoppingPatientsNearDose", "StoppingPatientsNearDose-class" ] }, { "page": "StoppingSpecificDose-class", "title": "'StoppingSpecificDose'", "topics": [ ".DefaultStoppingSpecificDose", ".StoppingSpecificDose", "StoppingSpecificDose", "StoppingSpecificDose-class" ] }, { "page": "StoppingTargetBiomarker-class", "title": "'StoppingTargetBiomarker'", "topics": [ ".DefaultStoppingTargetBiomarker", ".StoppingTargetBiomarker", "StoppingTargetBiomarker", "StoppingTargetBiomarker-class" ] }, { "page": "StoppingTargetProb-class", "title": "'StoppingTargetProb'", "topics": [ ".DefaultStoppingTargetProb", ".StoppingTargetProb", "StoppingTargetProb", "StoppingTargetProb-class" ] }, { "page": "StoppingTDCIRatio-class", "title": "'StoppingTDCIRatio'", "topics": [ ".DefaultStoppingTDCIRatio", ".StoppingTDCIRatio", "StoppingTDCIRatio", "StoppingTDCIRatio-class" ] }, { "page": "stopTrial", "title": "Stop the trial?", "topics": [ "stopTrial", "stopTrial,StoppingAll,ANY,ANY,ANY,ANY-method", "stopTrial,StoppingAny,ANY,ANY,ANY,ANY-method", "stopTrial,StoppingCohortsNearDose,numeric,ANY,ANY,Data-method", "stopTrial,StoppingExternal,numeric,ANY,ANY,ANY-method", "stopTrial,StoppingHighestDose,numeric,ANY,ANY,Data-method", "stopTrial,StoppingList,ANY,ANY,ANY,ANY-method", "stopTrial,StoppingLowestDoseHSRBeta,numeric,Samples,ANY,ANY-method", "stopTrial,StoppingMaxGainCIRatio,ANY,missing,ModelTox,DataDual-method", "stopTrial,StoppingMaxGainCIRatio,ANY,Samples,ModelTox,DataDual-method", "stopTrial,StoppingMinCohorts,ANY,ANY,ANY,Data-method", "stopTrial,StoppingMinPatients,ANY,ANY,ANY,Data-method", "stopTrial,StoppingMissingDose,numeric,ANY,ANY,Data-method", "stopTrial,StoppingMTDCV,numeric,Samples,GeneralModel,ANY-method", "stopTrial,StoppingMTDdistribution,numeric,Samples,GeneralModel,ANY-method", "stopTrial,StoppingOrdinal,numeric,ANY,ANY,ANY-method", "stopTrial,StoppingOrdinal,numeric,ANY,LogisticLogNormalOrdinal,DataOrdinal-method", "stopTrial,StoppingPatientsNearDose,numeric,ANY,ANY,Data-method", "stopTrial,StoppingSpecificDose,numeric,ANY,ANY,Data-method", "stopTrial,StoppingTargetBiomarker,numeric,Samples,DualEndpoint,ANY-method", "stopTrial,StoppingTargetProb,numeric,Samples,GeneralModel,ANY-method", "stopTrial,StoppingTDCIRatio,ANY,missing,ModelTox,ANY-method", "stopTrial,StoppingTDCIRatio,ANY,Samples,ModelTox,ANY-method", "stopTrial-StoppingAll", "stopTrial-StoppingAny", "stopTrial-StoppingCohortsNearDose", "stopTrial-StoppingExternal", "stopTrial-StoppingHighestDose", "stopTrial-StoppingList", "stopTrial-StoppingLowestDoseHSRBeta", "stopTrial-StoppingMaxGainCIRatio", "stopTrial-StoppingMinCohorts", "stopTrial-StoppingMinPatients", "stopTrial-StoppingMissingDose", "stopTrial-StoppingMTDCV", "stopTrial-StoppingMTDdistribution", "stopTrial-StoppingOrdinal", "stopTrial-StoppingPatientsNearDose", "stopTrial-StoppingSpecificDose", "stopTrial-StoppingTargetBiomarker", "stopTrial-StoppingTargetProb", "stopTrial-StoppingTDCIRatio" ] }, { "page": "subset-Data", "title": "Subsetting Operator for the Data Class", "topics": [ "subset-Data", "[,Data,logical,missing,missing-method", "[,Data,numeric,missing,missing-method" ] }, { "page": "summary-DualSimulations-method", "title": "Summarize Dual-Endpoint Design Simulations", "topics": [ "summary,DualSimulations-method", "summary-DualSimulations" ] }, { "page": "summary-GeneralSimulations-method", "title": "Summarize the 'GeneralSimulations', Relative to a Given Truth", "topics": [ "summary,GeneralSimulations-method", "summary-GeneralSimulations" ] }, { "page": "summary-PseudoDualFlexiSimulations-method", "title": "Summarize 'PseudoDualFlexiSimulations'", "topics": [ "summary,PseudoDualFlexiSimulations-method", "summary-PseudoDualFlexiSimulations" ] }, { "page": "summary-PseudoDualSimulations-method", "title": "Summarize 'PseudoDualSimulations'", "topics": [ "summary,PseudoDualSimulations-method", "summary-PseudoDualSimulations" ] }, { "page": "summary-PseudoSimulations-method", "title": "Summarize 'PseudoSimulations'", "topics": [ "summary,PseudoSimulations-method", "summary-PseudoSimulations" ] }, { "page": "summary-Simulations-method", "title": "Summarize Model-Based Design Simulations", "topics": [ "summary,Simulations-method", "summary-Simulations" ] }, { "page": "TDDesign-class", "title": "'TDDesign'", "topics": [ ".DefaultTDDesign", ".TDDesign", "TDDesign", "TDDesign-class" ] }, { "page": "TDsamplesDesign-class", "title": "'TDsamplesDesign'", "topics": [ ".DefaultTDsamplesDesign", ".TDsamplesDesign", "TDsamplesDesign", "TDsamplesDesign-class" ] }, { "page": "tidy", "title": "Tidying 'CrmPackClass' objects", "topics": [ "tidy", "tidy,CohortSizeDLT-method", "tidy,CohortSizeMax-method", "tidy,CohortSizeMin-method", "tidy,CohortSizeParts-method", "tidy,CohortSizeRange-method", "tidy,CrmPackClass-method", "tidy,Data-method", "tidy,DataDA-method", "tidy,DataDual-method", "tidy,DataGrouped-method", "tidy,DataMixture-method", "tidy,DataOrdinal-method", "tidy,DataParts-method", "tidy,DualDesign-method", "tidy,Effloglog-method", "tidy,GeneralData-method", "tidy,IncrementsMaxToxProb-method", "tidy,IncrementsMin-method", "tidy,IncrementsRelative-method", "tidy,IncrementsRelativeDLT-method", "tidy,IncrementsRelativeParts-method", "tidy,LogisticIndepBeta-method", "tidy,NextBestNCRM-method", "tidy,NextBestNCRMLoss-method", "tidy,Samples-method", "tidy,Simulations-method", "tidy-CohortSizeDLT", "tidy-CohortSizeMax", "tidy-CohortSizeMin", "tidy-CohortSizeParts", "tidy-CohortSizeRange", "tidy-CrmPackClass", "tidy-Data", "tidy-DataDA", "tidy-DataDual", "tidy-DataGrouped", "tidy-DataMixture", "tidy-DataOrdinal", "tidy-DataParts", "tidy-DualDesign", "tidy-Effloglog", "tidy-GeneralData", "tidy-IncrementsMaxToxProb", "tidy-IncrementsMin", "tidy-IncrementsRelative", "tidy-IncrementsRelativeDLT", "tidy-IncrementsRelativeParts", "tidy-LogisticIndepBeta", "tidy-NextBestNCRM", "tidy-NextBestNCRMLoss", "tidy-Samples", "tidy-Simulations" ] }, { "page": "TITELogisticLogNormal-class", "title": "'TITELogisticLogNormal'", "topics": [ ".DefaultTITELogisticLogNormal", ".TITELogisticLogNormal", "TITELogisticLogNormal", "TITELogisticLogNormal-class" ] }, { "page": "update-Data-method", "title": "Updating 'Data' Objects", "topics": [ "update,Data-method", "update-Data" ] }, { "page": "update-DataDA-method", "title": "Updating 'DataDA' Objects", "topics": [ "update,DataDA-method", "update-DataDA" ] }, { "page": "update-DataDual-method", "title": "Updating 'DataDual' Objects", "topics": [ "update,DataDual-method", "update-DataDual" ] }, { "page": "update-DataOrdinal-method", "title": "Updating 'DataOrdinal' Objects", "topics": [ "update,DataOrdinal-method", "update-DataOrdinal" ] }, { "page": "update-DataParts-method", "title": "Updating 'DataParts' Objects", "topics": [ "update,DataParts-method", "update-DataParts" ] }, { "page": "update-ModelPseudo-method", "title": "Update method for the 'ModelPseudo' model class. This is a method to update the model class slots (estimates, parameters, variables and etc.), when the new data (e.g. new observations of responses) are available. This method is mostly used to obtain new modal estimates for pseudo model parameters.", "topics": [ "update,ModelPseudo-method", "update-ModelPseudo" ] }, { "page": "v_backfill", "title": "Internal Helper Functions for Validation of 'Backfill' Objects", "topics": [ "v_backfill" ] }, { "page": "v_cohort_size", "title": "Internal Helper Functions for Validation of 'CohortSize' Objects", "topics": [ "v_cohort_size", "v_cohort_size_const", "v_cohort_size_dlt", "v_cohort_size_max", "v_cohort_size_parts", "v_cohort_size_random", "v_cohort_size_range" ] }, { "page": "v_data_objects", "title": "Internal Helper Functions for Validation of 'GeneralData' Objects", "topics": [ "h_doses_unique_per_cohort", "v_data", "v_data_da", "v_data_dual", "v_data_grouped", "v_data_mixture", "v_data_objects", "v_data_ordinal", "v_data_parts", "v_general_data" ] }, { "page": "v_design", "title": "Internal Helper Functions for Validation of 'RuleDesign' Objects", "topics": [ "v_design", "v_design_grouped", "v_rule_design", "v_rule_design_ordinal" ] }, { "page": "v_general_simulations", "title": "Internal Helper Functions for Validation of 'GeneralSimulations' Objects", "topics": [ "v_da_simulations", "v_dual_simulations", "v_general_simulations", "v_simulations" ] }, { "page": "v_increments", "title": "Internal Helper Functions for Validation of 'Increments' Objects", "topics": [ "v_cohort_size_ordinal", "v_increments", "v_increments_dose_levels", "v_increments_hsr_beta", "v_increments_maxtoxprob", "v_increments_min", "v_increments_ordinal", "v_increments_relative", "v_increments_relative_dlt", "v_increments_relative_parts" ] }, { "page": "v_mcmcoptions_objects", "title": "Internal Helper Functions for Validation of 'McmcOptions' Objects", "topics": [ "v_mcmcoptions_objects", "v_mcmc_options" ] }, { "page": "v_model_objects", "title": "Internal Helper Functions for Validation of 'GeneralModel' and 'ModelPseudo' Objects", "topics": [ "v_general_model", "v_logisticlognormalordinal", "v_model_da_logistic_log_normal", "v_model_dual_endpoint", "v_model_dual_endpoint_beta", "v_model_dual_endpoint_emax", "v_model_dual_endpoint_rw", "v_model_eff_flexi", "v_model_eff_log_log", "v_model_logistic_indep_beta", "v_model_logistic_kadane", "v_model_logistic_kadane_beta_gamma", "v_model_logistic_log_normal_mix", "v_model_logistic_normal_fixed_mix", "v_model_logistic_normal_mix", "v_model_objects", "v_model_one_par_exp_normal_prior", "v_model_one_par_exp_prior", "v_model_tite_logistic_log_normal" ] }, { "page": "v_model_params", "title": "Internal Helper Functions for Validation of Model Parameters Objects", "topics": [ "v_model_params", "v_model_params_normal" ] }, { "page": "v_next_best", "title": "Internal Helper Functions for Validation of 'NextBest' Objects", "topics": [ "v_next_best", "v_next_best_dual_endpoint", "v_next_best_ewoc", "v_next_best_inf_theory", "v_next_best_max_gain_samples", "v_next_best_min_dist", "v_next_best_mtd", "v_next_best_ncrm", "v_next_best_ncrm_loss", "v_next_best_ordinal", "v_next_best_prob_mtd_lte", "v_next_best_prob_mtd_min_dist", "v_next_best_td", "v_next_best_td_samples" ] }, { "page": "v_opening", "title": "Internal Helper Functions for Validation of 'Opening' Objects", "topics": [ "v_opening", "v_opening_min_cohorts", "v_opening_min_dose", "v_opening_min_responses" ] }, { "page": "v_pseudo_simulations", "title": "Internal Helper Functions for Validation of 'PseudoSimulations' Objects", "topics": [ "v_pseudo_dual_flex_simulations", "v_pseudo_dual_simulations", "v_pseudo_simulations" ] }, { "page": "v_recruitment", "title": "Internal Helper Functions for Validation of 'Recruitment' Objects", "topics": [ "v_recruitment", "v_recruitment_ratio" ] }, { "page": "v_safety_window", "title": "Internal Helper Functions for Validation of 'SafetyWindow' Objects", "topics": [ "v_safety_window", "v_safety_window_const", "v_safety_window_size" ] }, { "page": "v_samples_objects", "title": "Internal Helper Functions for Validation of 'Samples' Objects", "topics": [ "v_samples", "v_samples_objects" ] }, { "page": "v_stopping", "title": "Internal Helper Functions for Validation of 'Stopping' Objects", "topics": [ "v_stopping", "v_stopping_all", "v_stopping_cohorts_near_dose", "v_stopping_list", "v_stopping_min_cohorts", "v_stopping_min_patients", "v_stopping_mtd_cv", "v_stopping_mtd_distribution", "v_stopping_patients_near_dose", "v_stopping_target_biomarker", "v_stopping_target_prob", "v_stopping_tdci_ratio" ] }, { "page": "Validate", "title": "'Validate'", "topics": [ "Validate" ] }, { "page": "windowLength", "title": "Determine the Safety Window Length of the Next Cohort", "topics": [ "windowLength", "windowLength,SafetyWindowConst-method", "windowLength,SafetyWindowSize-method", "windowLength-SafetyWindowConst", "windowLength-SafetyWindowSize" ] } ], "_pkglogo": "https://github.com/openpharma/crmpack/raw/HEAD/man/figures/logo.png", "_readme": "https://github.com/openpharma/crmpack/raw/HEAD/README.md", "_rundeps": [ "backports", "base64enc", "bslib", "cachem", "checkmate", "cli", "coda", "cpp11", "digest", "dplyr", "evaluate", "farver", "fastmap", "fontawesome", "formatR", "fs", "futile.logger", "futile.options", "generics", "GenSA", "ggplot2", "glue", "gridExtra", "gtable", "highr", "htmltools", "isoband", "jquerylib", "jsonlite", "kableExtra", "knitr", "labeling", "lambda.r", "lattice", "lifecycle", "magrittr", "Matrix", "memoise", "mime", "mvtnorm", "parallelly", "pillar", "pkgconfig", "R6", "rappdirs", "rbibutils", "RColorBrewer", "Rdpack", "rjags", "rlang", "rmarkdown", "rstudioapi", "S7", "sass", "scales", "stringi", "stringr", "survival", "svglite", "systemfonts", "textshaping", "tibble", "tidyselect", "tinytex", "utf8", "vctrs", "viridisLite", "withr", "xfun", "xml2", "yaml" ], "_sysdeps": [ { "shlib": "libjags", "package": "jags", "headers": "jags", "source": "jags", "version": "4.3.2-2.2404.0", "name": "jags", "homepage": "https://mcmc-jags.sourceforge.io", "description": "Just Another Gibbs Sampler for Bayesian MCMC - binary\nJAGS is Just Another Gibbs Sampler. It is a program for analysis of\nBayesian hierarchical models using Markov Chain Monte Carlo (MCMC)\nsimulation not wholly unlike BUGS.\n\nJAGS was written with three aims in mind:\n* To have an engine for the BUGS language that runs on Unix\n* To be extensible, allowing users to write their own functions,\ndistributions and samplers.\n* To be a plaftorm for experimentation with ideas in Bayesian modelling\n\nThis package contains the 'jags' binary as well as the associated\nshared library modules loaded by the binary." }, { "shlib": "libstdc++", "package": "libstdc++6", "source": "gcc", "version": "14.2.0-4ubuntu2~24.04.1", "name": "c++", "homepage": "http://gcc.gnu.org/", "description": "GNU Standard C++ Library v3" } ], "_vignettes": [ { "source": "trial_backfill.Rmd", "filename": "trial_backfill.html", "title": "Backfill cohorts", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Framework", "Algorithm", "Examples", "Standard components", "No backfill cohorts", "Simple backfill cohorts", "More complex backfill cohorts", "Simulations with backfill cohorts", "Investigating single trial data", "Investigating simulation results", "Limitations", "Backwards Compatibility", "References" ], "created": "2026-01-30 12:48:25", "modified": "2026-01-30 12:48:25", "commits": 1 }, { "source": "example.Rmd", "filename": "example.html", "title": "crmPack: Object-oriented implementation of CRM designs", "author": "Ziwei Liao, Daniel Sabanés Bové", "engine": "knitr::rmarkdown", "headings": [ "Installation", "Getting started", "Data", "Structure of the model class", "Model setup", "Logistic model with bivariate (log) normal prior", "Advanced model specification", "Obtaining the posterior", "Plotting the model fit", "Escalation Rules", "Increments rules", "Rules for next best dose recommendation", "Cohort size rules", "Stopping rules", "Simulations", "Examining single trial behavior", "Simulating from a true scenario", "Predicting the future course of the trial", "Simulating 3+3 design outcomes", "Dual-endpoint dose escalation designs", "Dual-endpoint designs with a joint model", "Dual-endpoint designs with separate models", "References" ], "created": "2023-07-06 13:34:57", "modified": "2026-01-30 12:48:25", "commits": 10 }, { "source": "knit_print.Rmd", "filename": "knit_print.html", "title": "Describing crmPack Objects", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "How is this done?", "Using knit_print in crmPack", "Common customisations", "Rendering complex classes", "Design", "Dose toxicity model", "Stopping rule", "Escalation rule", "Use of placebo", "Dose recommendation", "Cohort size", "Observed data", "Starting dose", "Restoring console-like behaviour", "Accessing the output of knit_print", "Providing your own knit_print method", "Class coverage" ], "created": "2024-09-27 14:33:48", "modified": "2025-11-29 07:50:00", "commits": 4 }, { "source": "crmPack-jss-paper.Rmd", "filename": "crmPack-jss-paper.html", "title": "Model-based Dose Escalation Designs in R with crmPack (JSS manuscript)", "engine": "knitr::rmarkdown", "headings": [ "Abstract", "Introduction", "Framework", "Using crmPack", "Implementing a CRM trial", "Dose escalation with safety and efficacy", "Extending crmPack functionality", "Summary", "Acknowledgments", "References" ], "created": "2023-07-06 13:34:57", "modified": "2025-11-29 07:50:00", "commits": 7 }, { "source": "ordinal-crm.Rmd", "filename": "ordinal-crm.html", "title": "Ordinal CRM", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Implementation", "Ordinal data", "The LogisticLogNormalOrdinal class", "Model fitting", "Rules classes for ordinal models", "On the need for a diagonal covariance matrix", "Some observations", "Environment", "References" ], "created": "2023-11-26 09:39:56", "modified": "2024-10-07 08:18:28", "commits": 3 }, { "source": "parallel_computing_with_extensions.Rmd", "filename": "parallel_computing_with_extensions.html", "title": "Parallel computing with extensions", "author": "Oliver Boix", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "High level usage", "Important information for usage", "Information for debugging", "Worked out example", "Alternative: read user code from external file", "Note", "References" ], "created": "2023-03-27 15:55:45", "modified": "2023-07-19 08:57:42", "commits": 6 }, { "source": "rolling-crm.Rmd", "filename": "rolling-crm.html", "title": "Rolling CRM Example", "author": "Jiawen Zhu, Daniel Sabanés Bové", "engine": "knitr::rmarkdown", "headings": [ "Example 1: Recommend a dose for the next cohort", "Setting up the data", "Structure of the model class", "Obtain the posterior", "Use ggmcmc to diagnose", "Plot the model fit", "prior mean curve", "Escalation rules", "Recommended dose for the next cohort", "Example 2: Run a simulation to evaluate operating characteristics", "Set up safety window and DADesign to be completed", "Set up true curves", "Perform the simulations", "Interpret the simulation results" ], "created": "2021-11-15 20:52:26", "modified": "2026-01-30 12:48:25", "commits": 15 }, { "source": "simulations.Rmd", "filename": "simulations.html", "title": "Simulation study example", "author": "Ziwei Liao, Daniel Sabanés Bové", "engine": "knitr::rmarkdown", "headings": [ "Simulation setting", "Design definition", "Simulation run" ], "created": "2021-11-15 20:52:26", "modified": "2026-01-30 12:48:25", "commits": 7 }, { "source": "trial_analysis.Rmd", "filename": "trial_analysis.html", "title": "Trial Analysis", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "The dose grid", "The dose-toxicity model", "The increment rule", "The dose selection rule", "The cohort size", "The stopping rule", "Trial definition", "Analysing a trial", "The single patient run-in", "The first full cohort", "The second full cohort", "The third full cohort", "The fourth full cohort", "The fifth full cohort", "Summarising the trial results", "Note", "References" ], "created": "2023-01-09 11:32:28", "modified": "2026-01-20 13:50:59", "commits": 8 }, { "source": "trial_definition.Rmd", "filename": "trial_definition.html", "title": "Trial Definition", "engine": "knitr::rmarkdown", "headings": [ "Defining the design", "The dose grid", "The dose toxicity model", "The escalation rules", "The maximum increment", "The NextBest rule for recommending the best dose for the next cohort", "The cohort size", "The stopping rules", "The overall trial design", "References" ], "created": "2023-01-09 11:32:28", "modified": "2024-10-16 07:53:53", "commits": 5 }, { "source": "trial_sanity_checks.Rmd", "filename": "trial_sanity_checks.html", "title": "Trial Design: basic sanity checks", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Study definition", "Incoherence and rigidity", "Does the prior make sense?", "Final observation", "References" ], "created": "2023-05-30 13:19:04", "modified": "2025-11-29 07:50:00", "commits": 3 }, { "source": "trial_simulation.Rmd", "filename": "trial_simulation.html", "title": "Trial Simulation", "engine": "knitr::rmarkdown", "headings": [ "Example" ], "created": "2023-01-09 11:32:28", "modified": "2023-07-19 08:57:42", "commits": 2 }, { "source": "upgrading_from_the_old_crmPack.Rmd", "filename": "upgrading_from_the_old_crmPack.html", "title": "Upgrading from crmPack version 1.0", "author": "Wojciech Wójciak, Oliver Boix", "engine": "knitr::rmarkdown", "headings": [ "Class and slot changes", "Naming convention motivation", "New classes", "Renamed classes", "Renamed slots", "Moved dose and prob Functions from Slots to Methods", "Generate data, define a model and get samples", "Dose", "Prob", "New Random Number Generator settings for the MCMC", "New no-argument constructors", "Handling of NA or placebo returned as next dose", "Evaluation of stopping rules at a specific dose", "Further details in class and methods name changes", "Classes", "Methods", "References" ], "created": "2025-11-27 10:43:16", "modified": "2025-11-29 07:50:00", "commits": 2 }, { "source": "tidy_method.Rmd", "filename": "tidy_method.html", "title": "Using tidy", "engine": "knitr::rmarkdown", "headings": [ "Introducing tidy methods to crmPack", "Basic approach", "Exceptions", "Examples", "Using tidy crmPack data", "Cohort size", "Environment" ], "created": "2023-11-06 09:59:00", "modified": "2024-01-17 12:49:36", "commits": 2 } ], "_score": 10.105676921839557, "_indexed": true, "_nocasepkg": "crmpack", "_universes": [ "openpharma" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "2.1.0.9000", "date": "2026-05-16T05:23:34.000Z", "distro": "noble", "commit": "38ee7fae924ef77923a758a3adf22fad91297349", "fileid": "7826ce29170795d36cf9adbe9d8f638c92793a3542c54ac3a65c2f30e1a8b0ea", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/openpharma/actions/runs/25953626182" }, { "r": "4.6.0", "os": "linux", "version": "2.1.0.9000", "date": "2026-05-16T05:23:15.000Z", "distro": "noble", "commit": "38ee7fae924ef77923a758a3adf22fad91297349", "fileid": "e2c4e7f0bfe370b65600aa378e176627d2d816dbc4bc2945c95ffaf34cc1951d", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/openpharma/actions/runs/25953626182" }, { "r": "4.5.3", "os": "mac", "version": "2.1.0.9000", "date": "2026-05-16T05:22:35.000Z", "commit": "38ee7fae924ef77923a758a3adf22fad91297349", "fileid": "728d7b55694300a43bb5b22b011d56bc228830d5aa4c3e45695b0c46ae302c92", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/openpharma/actions/runs/25953626182" }, { "r": "4.6.0", "os": "mac", "version": "2.1.0.9000", "date": "2026-05-16T05:23:13.000Z", "commit": "38ee7fae924ef77923a758a3adf22fad91297349", "fileid": "098fc82f8ec2a42b40ad00bcd7c787619abf2ebb35700687aa2e208f0f2e6cbb", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/openpharma/actions/runs/25953626182" }, { "r": "4.7.0", "os": "win", "version": "2.1.0.9000", "date": "2026-05-16T05:22:29.000Z", "commit": "38ee7fae924ef77923a758a3adf22fad91297349", "fileid": "c33f589c256d4505e8c34f97c74d649d79431d44f4f02234237007a5ad2b7064", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/openpharma/actions/runs/25953626182" }, { "r": "4.5.3", "os": "win", "version": "2.1.0.9000", "date": "2026-05-16T05:22:34.000Z", "commit": "38ee7fae924ef77923a758a3adf22fad91297349", "fileid": "f93d76b73697b297222438d5c0cf30b75242e43eab893534ab2868bce6ee5b06", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/openpharma/actions/runs/25953626182" }, { "r": "4.6.0", "os": "win", "version": "2.1.0.9000", "date": "2026-05-16T05:22:19.000Z", "commit": "38ee7fae924ef77923a758a3adf22fad91297349", "fileid": "69045ebacd8a7078bc79d17b9b02089caad47c28e46f46d1bec99c137565e9bb", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/openpharma/actions/runs/25953626182" }, { "r": "4.6.0", "os": "wasm", "version": "2.1.0.9000", "date": "2026-06-02T18:59:59.000Z", "commit": "38ee7fae924ef77923a758a3adf22fad91297349", "fileid": "e8fd7c50f6aab9701d8444704a292a3909a6f33d65f53c7b2008922d3556088d", "status": "success", "buildurl": "https://github.com/r-universe/openpharma/actions/runs/25953626182" } ] }