{
  "_id": "6ac10ab8552a13804ac7eca8",
  "Package": "PhysioTwin",
  "Title": "Digital-Twin Simulation and Data Assimilation for Physiological\nand Movement Systems",
  "Version": "0.1.2",
  "Authors@R": "person(\"Yusuke\", \"Matsui\", email = \"mail.to.matsui@gmail.com\", role = c(\"aut\",\"cre\"))",
  "Description": "A digital-twin engine that couples mechanistic simulation\nof movement and physiology with realistic sensor emulation and\ndata assimilation, so a subject-specific model can be\npersonalised from real measurements and then used for in-silico\nexperiments. It ships a parameterised limb model (forward\ndynamics) and mechanistic physiological and network generators\n-- a Jansen-Rit neural-mass EEG column with an optional 1/f\naperiodic background, a McSharry limit-cycle electrocardiogram,\na Windkessel arterial blood-pressure model with a closed-loop\nbaroreflex, a respiration waveform and\nrespiratory-sinus-arrhythmia heart-rate-variability generator,\nand Kuramoto coupled-oscillator and Matsuoka\ncentral-pattern-generator rhythms. A forward sensor-emulation\nlayer turns clean signals into realistic recordings\n(inertial-measurement-unit, optical-marker occlusion,\nsurface-electromyography electrode and electrocardiogram lead),\nwith richer chains for multi-channel electromyography crosstalk\nand amplifier characteristics, multi-lead electrocardiogram and\nanalogue-to-digital conversion. A data-assimilation layer\npersonalises the twin to recorded data with a family of\nstate-space estimators (unscented, extended, particle and\nensemble Kalman filters), likelihood-free Bayesian calibration\n(rejection and sequential Monte-Carlo approximate Bayesian\ncomputation), Markov-chain Monte- Carlo, a Gaussian-process\nsurrogate and GP-accelerated (Bayesian-optimisation)\ncalibration -- including personalising the closed-loop\ncardiovascular- respiratory twin to measured heart-rate and\nblood-pressure variability -- as well as particle Markov-chain\nMonte-Carlo and particle Gibbs (with ancestor sampling) for\nwhole-waveform Bayesian fitting -- including a whole-record fit\nof the closed-loop cardiovascular model that identifies the\nbaroreflex and respiratory gains a summary fit cannot, with\neither a resonant Mayer-oscillator or a broadband\nfirst-order-autoregressive low-frequency component for records\nwithout a distinct Mayer peak -- and Fisher-information optimal\nexperimental design for choosing the most informative\nexperimental condition. Applications include single- and\npopulation-level in-silico intervention (perturb parameters and\npredict the change, with effect size, responder rate and\nstatistical power), multi-modal coupling, and labelled\nsynthetic training-data generation with sensor domain\nrandomisation, all backed by a simulation\nvalidity-and-verification harness (identifiability, global\nsensitivity and validation against real recordings). A clinical\ndecision loop ties these together as research tooling: a\npersonalised twin is certified by a validation gate (goodness\nof fit and parameter identifiability) before it may advise,\ncandidate interventions are scored with their predictive\nuncertainty and ranked on multiple criteria, mechanistic\npredictions are reconciled with external evidence rather than\noverriding it, and a follow-up measurement is checked against\nthe predicted interval. A closed-loop\ncardiovascular-respiratory model couples a delayed baroreflex\nwith respiration to produce emergent Mayer waves and\nrespiratory sinus arrhythmia, and an optional bridge holds the\ntwin's predictions to account against a real single-case\n(SCED/MCID/ICF) clinical evaluation.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "Roxygen": "list(markdown = TRUE)",
  "RoxygenNote": "7.3.3",
  "Maintainer": "Yusuke Matsui <mail.to.matsui@gmail.com>",
  "URL": "https://github.com/x-biosignal/PhysioTwin,\nhttps://x-biosignal.r-universe.dev/PhysioTwin",
  "BugReports": "https://github.com/x-biosignal/PhysioTwin/issues",
  "VignetteBuilder": "knitr",
  "Additional_repositories": "https://x-biosignal.r-universe.dev",
  "Config/testthat/edition": "3",
  "Repository": "https://x-biosignal.r-universe.dev",
  "Date/Publication": "2026-10-03 13:11:50 UTC",
  "RemoteUrl": "https://github.com/x-biosignal/PhysioTwin",
  "RemoteRef": "HEAD",
  "RemoteSha": "f98025227960bb7ea910dd0083a4b8c81dcf64c2",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-10-03 13:43:08 UTC",
    "User": "root"
  },
  "Author": "Yusuke Matsui [aut, cre]",
  "_user": "x-biosignal",
  "_type": "src",
  "_file": "PhysioTwin_0.1.2.tar.gz",
  "_fileid": "https://r2.ropensci.org/307492c668e3f65d1b7e8ecf59514b44c06361046f089cea470fd9589aba1abc",
  "_filesize": 439091,
  "_sha256": "307492c668e3f65d1b7e8ecf59514b44c06361046f089cea470fd9589aba1abc",
  "_expires": "2027-01-11T14:01:26.000Z",
  "_created": "2026-10-03T13:43:08.000Z",
  "_published": "2026-10-03T14:01:27.978Z",
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  "_host": "GitHub-Actions",
  "_buildurl": "https://github.com/r-universe/x-biosignal/actions/runs/37126929193",
  "_status": "success",
  "_upstream": "https://github.com/x-biosignal/PhysioTwin",
  "_commit": {
    "id": "f98025227960bb7ea910dd0083a4b8c81dcf64c2",
    "author": "Yusuke Matsui <mail.to.matsui@gmail.com>",
    "committer": "Yusuke Matsui <mail.to.matsui@gmail.com>",
    "message": "Release snapshot\n",
    "time": 1791033110
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  "_maintainer": {
    "name": "Yusuke Matsui",
    "email": "mail.to.matsui@gmail.com",
    "login": "ymatts",
    "description": "Data scientist",
    "uuid": 5744286
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  "_distro": "resolute",
  "_registered": true,
  "_dependencies": [
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  "_updates": [
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    "name": "x-biosignal",
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    "description": "Open-source R packages for biosignal analysis, biomechanics, clinical research, and rehabilitation."
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  "_rbuild": "4.6.1",
  "_assets": [
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  "_exports": [
    "abcCalibration",
    "abcSMC",
    "adcQuantize",
    "adcSample",
    "aperiodicNoise",
    "bandPower",
    "baroreflex",
    "cardioRespiratory",
    "cardioStateSpace",
    "cardioSummary",
    "clinicalDecision",
    "cpgMatsuoka",
    "crosstalkMatrix",
    "defaultInterventions",
    "detectRpeaks",
    "ecgLead",
    "ecgLeadSet",
    "ecgMeasure",
    "ecgMeasureLeads",
    "ecgsyn",
    "emgArray",
    "emgArrayMeasure",
    "emgElectrode",
    "emgEnvelope",
    "emgMeasure",
    "ensembleKalmanFilter",
    "evaluateIntervention",
    "extendedKalmanFilter",
    "fillMarkerGaps",
    "generateTrainingData",
    "gpCalibrate",
    "gpEmulator",
    "imuMeasure",
    "imuSensor",
    "insilicoIntervention",
    "insilicoInterventionMultimodal",
    "intervention",
    "jansenRit",
    "kuramoto",
    "limbTwin",
    "markerMeasure",
    "markerSensor",
    "metropolis",
    "multimodalTwin",
    "optimalDesign",
    "particleFilter",
    "particleGibbs",
    "particleMCMC",
    "personalizeCardio",
    "personalizeCardioWaveform",
    "personalizeTwin",
    "populationIntervention",
    "profileLikelihood",
    "quantizationNoise",
    "reconcileEvidence",
    "rehabEvaluate",
    "respiration",
    "rsaTachogram",
    "scoreInterventions",
    "simulateMultimodal",
    "simulateTwin",
    "sobolIndices",
    "twinReadiness",
    "unscentedKalmanFilter",
    "validateTwin",
    "windkessel"
  ],
  "_help": [
    {
      "page": "abcCalibration",
      "title": "Approximate Bayesian Computation (likelihood-free parameter calibration)",
      "topics": [
        "abcCalibration"
      ]
    },
    {
      "page": "abcSMC",
      "title": "Sequential Monte-Carlo ABC (likelihood-free posterior by tolerance annealing)",
      "topics": [
        "abcSMC"
      ]
    },
    {
      "page": "adcQuantize",
      "title": "Quantise a signal to a finite bit depth",
      "topics": [
        "adcQuantize"
      ]
    },
    {
      "page": "adcSample",
      "title": "Resample a signal to a new rate with optional sampling jitter",
      "topics": [
        "adcSample"
      ]
    },
    {
      "page": "aperiodicNoise",
      "title": "Aperiodic (1/f) background noise",
      "topics": [
        "aperiodicNoise"
      ]
    },
    {
      "page": "bandPower",
      "title": "Relative band power of a simulated EEG",
      "topics": [
        "bandPower"
      ]
    },
    {
      "page": "baroreflex",
      "title": "Baroreflex closed-loop blood-pressure regulation",
      "topics": [
        "baroreflex"
      ]
    },
    {
      "page": "cardioRespiratory",
      "title": "Closed-loop cardiovascular-respiratory twin",
      "topics": [
        "cardioRespiratory"
      ]
    },
    {
      "page": "cardioStateSpace",
      "title": "Closed-loop cardiovascular state-space model (for whole-record fitting)",
      "topics": [
        "cardioStateSpace"
      ]
    },
    {
      "page": "cardioSummary",
      "title": "Summarise a closed-loop cardiovascular-respiratory recording",
      "topics": [
        "cardioSummary"
      ]
    },
    {
      "page": "clinicalDecision",
      "title": "Run the clinical decision loop end-to-end",
      "topics": [
        "clinicalDecision"
      ]
    },
    {
      "page": "cpgMatsuoka",
      "title": "Matsuoka central pattern generator (half-centre oscillator)",
      "topics": [
        "cpgMatsuoka"
      ]
    },
    {
      "page": "crosstalkMatrix",
      "title": "Electrode crosstalk mixing matrix",
      "topics": [
        "crosstalkMatrix"
      ]
    },
    {
      "page": "defaultInterventions",
      "title": "A default library of movement-twin interventions",
      "topics": [
        "defaultInterventions"
      ]
    },
    {
      "page": "detectRpeaks",
      "title": "Remove baseline wander and detect R-peaks in an ECG",
      "topics": [
        "detectRpeaks"
      ]
    },
    {
      "page": "ecgLead",
      "title": "Specify ECG lead / electrode characteristics",
      "topics": [
        "ecgLead"
      ]
    },
    {
      "page": "ecgLeadSet",
      "title": "Specify a multi-lead ECG lead set",
      "topics": [
        "ecgLeadSet"
      ]
    },
    {
      "page": "ecgMeasure",
      "title": "Measure a clean ECG through a lead/electrode (forward model)",
      "topics": [
        "ecgMeasure"
      ]
    },
    {
      "page": "ecgMeasureLeads",
      "title": "Measure a clean ECG as a multi-lead recording (forward model)",
      "topics": [
        "ecgMeasureLeads"
      ]
    },
    {
      "page": "ecgsyn",
      "title": "ECGSYN mechanistic ECG generator (McSharry dynamical model)",
      "topics": [
        "ecgsyn"
      ]
    },
    {
      "page": "emgArray",
      "title": "Specify a surface-EMG array (crosstalk + amplifier)",
      "topics": [
        "emgArray"
      ]
    },
    {
      "page": "emgArrayMeasure",
      "title": "Measure per-muscle activations with a surface-EMG array (forward model)",
      "topics": [
        "emgArrayMeasure"
      ]
    },
    {
      "page": "emgElectrode",
      "title": "Specify surface-EMG electrode/amplifier characteristics",
      "topics": [
        "emgElectrode"
      ]
    },
    {
      "page": "emgEnvelope",
      "title": "EMG linear envelope (rectify + low-pass)",
      "topics": [
        "emgEnvelope"
      ]
    },
    {
      "page": "emgMeasure",
      "title": "Measure muscle activation with a surface-EMG electrode (forward model)",
      "topics": [
        "emgMeasure"
      ]
    },
    {
      "page": "ensembleKalmanFilter",
      "title": "Ensemble Kalman filter (Monte-Carlo covariance state estimator)",
      "topics": [
        "ensembleKalmanFilter"
      ]
    },
    {
      "page": "evaluateIntervention",
      "title": "Evaluate a delivered intervention against its prediction (close the loop)",
      "topics": [
        "evaluateIntervention"
      ]
    },
    {
      "page": "extendedKalmanFilter",
      "title": "Extended Kalman filter (Jacobian-linearised state estimator)",
      "topics": [
        "extendedKalmanFilter"
      ]
    },
    {
      "page": "fillMarkerGaps",
      "title": "Fill occlusion gaps in a measured marker trajectory",
      "topics": [
        "fillMarkerGaps"
      ]
    },
    {
      "page": "generateTrainingData",
      "title": "Generate a labelled synthetic training set from the twin",
      "topics": [
        "generateTrainingData"
      ]
    },
    {
      "page": "gpCalibrate",
      "title": "GP-accelerated calibration (Bayesian optimisation of an expensive loss)",
      "topics": [
        "gpCalibrate"
      ]
    },
    {
      "page": "gpEmulator",
      "title": "Gaussian-process emulator (surrogate model)",
      "topics": [
        "gpEmulator"
      ]
    },
    {
      "page": "imuMeasure",
      "title": "Measure a movement twin with an IMU (forward sensor model)",
      "topics": [
        "imuMeasure"
      ]
    },
    {
      "page": "imuSensor",
      "title": "Specify IMU sensor characteristics",
      "topics": [
        "imuSensor"
      ]
    },
    {
      "page": "insilicoIntervention",
      "title": "Predict the effect of an in-silico intervention",
      "topics": [
        "insilicoIntervention"
      ]
    },
    {
      "page": "insilicoInterventionMultimodal",
      "title": "Multi-modal in-silico intervention",
      "topics": [
        "insilicoInterventionMultimodal"
      ]
    },
    {
      "page": "intervention",
      "title": "Define a candidate in-silico intervention",
      "topics": [
        "intervention"
      ]
    },
    {
      "page": "jansenRit",
      "title": "Jansen-Rit neural-mass EEG generator",
      "topics": [
        "jansenRit"
      ]
    },
    {
      "page": "kuramoto",
      "title": "Kuramoto coupled-oscillator network",
      "topics": [
        "kuramoto"
      ]
    },
    {
      "page": "limbTwin",
      "title": "Build a parameterised movement twin (single-joint limb)",
      "topics": [
        "limbTwin"
      ]
    },
    {
      "page": "markerMeasure",
      "title": "Measure a marker trajectory with an optical mocap system",
      "topics": [
        "markerMeasure"
      ]
    },
    {
      "page": "markerSensor",
      "title": "Specify motion-capture marker characteristics",
      "topics": [
        "markerSensor"
      ]
    },
    {
      "page": "metropolis",
      "title": "Adaptive random-walk Metropolis-Hastings sampler",
      "topics": [
        "metropolis"
      ]
    },
    {
      "page": "multimodalTwin",
      "title": "Build a multi-modal (movement + cardiorespiratory) twin",
      "topics": [
        "multimodalTwin"
      ]
    },
    {
      "page": "optimalDesign",
      "title": "Bayesian optimal experimental design for twin identification",
      "topics": [
        "optimalDesign"
      ]
    },
    {
      "page": "particleFilter",
      "title": "Bootstrap particle filter (sequential Monte-Carlo state estimator)",
      "topics": [
        "particleFilter"
      ]
    },
    {
      "page": "particleGibbs",
      "title": "Particle Gibbs with ancestor sampling",
      "topics": [
        "particleGibbs"
      ]
    },
    {
      "page": "particleMCMC",
      "title": "Particle marginal Metropolis-Hastings (particle MCMC)",
      "topics": [
        "particleMCMC"
      ]
    },
    {
      "page": "personalizeCardio",
      "title": "Personalise the closed-loop CV-respiratory twin to real HRV/BPV summaries",
      "topics": [
        "personalizeCardio"
      ]
    },
    {
      "page": "personalizeCardioWaveform",
      "title": "Personalise the closed-loop cardiovascular twin from the whole RR record",
      "topics": [
        "personalizeCardioWaveform"
      ]
    },
    {
      "page": "personalizeTwin",
      "title": "Personalise a movement twin to recorded IMU data",
      "topics": [
        "personalizeTwin"
      ]
    },
    {
      "page": "populationIntervention",
      "title": "In-silico intervention trial over a population of twins",
      "topics": [
        "populationIntervention"
      ]
    },
    {
      "page": "predict.gpEmulator",
      "title": "Predict from a Gaussian-process emulator",
      "topics": [
        "predict.gpEmulator"
      ]
    },
    {
      "page": "profileLikelihood",
      "title": "Profile-likelihood practical identifiability",
      "topics": [
        "profileLikelihood"
      ]
    },
    {
      "page": "quantizationNoise",
      "title": "Quantisation noise of an analogue-to-digital conversion",
      "topics": [
        "quantizationNoise"
      ]
    },
    {
      "page": "reconcileEvidence",
      "title": "Reconcile a mechanistic prediction with external evidence",
      "topics": [
        "reconcileEvidence"
      ]
    },
    {
      "page": "rehabEvaluate",
      "title": "Evaluate a twin's intervention prediction against a real single-case series",
      "topics": [
        "rehabEvaluate"
      ]
    },
    {
      "page": "respiration",
      "title": "Respiratory waveform generator",
      "topics": [
        "respiration"
      ]
    },
    {
      "page": "rsaTachogram",
      "title": "Respiratory sinus arrhythmia tachogram (heart-rate variability generator)",
      "topics": [
        "rsaTachogram"
      ]
    },
    {
      "page": "scoreInterventions",
      "title": "Score and rank candidate interventions with predictive uncertainty",
      "topics": [
        "scoreInterventions"
      ]
    },
    {
      "page": "simulateMultimodal",
      "title": "Simulate a multi-modal twin (movement + cardiorespiratory summary)",
      "topics": [
        "simulateMultimodal"
      ]
    },
    {
      "page": "simulateTwin",
      "title": "Simulate a movement twin (forward dynamics)",
      "topics": [
        "simulateTwin"
      ]
    },
    {
      "page": "sobolIndices",
      "title": "Variance-based global sensitivity (Sobol indices)",
      "topics": [
        "sobolIndices"
      ]
    },
    {
      "page": "twinReadiness",
      "title": "Certify a personalised twin before it is allowed to advise (validation gate)",
      "topics": [
        "twinReadiness"
      ]
    },
    {
      "page": "unscentedKalmanFilter",
      "title": "Unscented Kalman filter (general nonlinear state estimator)",
      "topics": [
        "unscentedKalmanFilter"
      ]
    },
    {
      "page": "validateTwin",
      "title": "Validity assessment of a movement twin (verification & validation)",
      "topics": [
        "validateTwin"
      ]
    },
    {
      "page": "windkessel",
      "title": "Windkessel arterial blood-pressure generator",
      "topics": [
        "windkessel"
      ]
    }
  ],
  "_readme": "https://github.com/x-biosignal/PhysioTwin/raw/HEAD/README.md",
  "_rundeps": [],
  "_vignettes": [
    {
      "source": "PhysioTwin.Rmd",
      "filename": "PhysioTwin.html",
      "title": "PhysioTwin: the movement digital-twin pipeline",
      "engine": "knitr::rmarkdown",
      "headings": [
        "1--2. A \"real\" subject, recorded through a noisy IMU",
        "3. Personalise the twin from the recording",
        "4a. In-silico intervention",
        "4b. A labelled synthetic training set",
        "Validity and verification",
        "Where next"
      ],
      "created": "2026-10-03 13:11:50",
      "modified": "2026-10-03 13:11:50",
      "commits": 1
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  "_score": 2.6989700043360187,
  "_indexed": true,
  "_nocasepkg": "physiotwin",
  "_universes": [
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    "ymatts"
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  "_previous": "0.1.1",
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      "version": "0.1.2",
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