Changes in version 0.1.2                        

Documentation

  - ?PhysioTwin now answers: a package help page gives one paragraph on
    what the package is for, the main entry points grouped by task, and
    where to go next.
  - A vignette carries one task end to end on synthetic or bundled data,
    offline, and is built and run by R CMD check.
  - Runnable @examples added or corrected across 1 help pages. Each runs
    offline in seconds, writes nothing outside tempdir(), and is
    executed by R CMD check; anything needing a device, a download or an
    optional backend is fenced with the reason stated.
  - The README's quick start runs as written: it attaches the package,
    builds its own inputs, and uses only hard dependencies.

                        Changes in version 0.1.1                        

  - EEG 1/f aperiodic background. aperiodicNoise()
    generates 1/f^exponent background noise, and jansenRit(aperiodic=,
    aperiodic_exp=) mixes it into the neural-mass column, turning its
    over-concentrated single-rhythm spectrum into a realistic broadband
    one: the relative alpha power drops from 0.99 to about 0.66,
    matching a real resting EEG, while the alpha peak survives (the
    dominant-rhythm search now runs above 3 Hz, over the 1/f floor). The
    Jansen-Rit validation case fits the 1/f amplitude so the model's
    relative alpha matches the recording.
  - Broadband low-frequency HRV model. cardioStateSpace(lf_model =
    "ar1") and personalizeCardioWaveform(lf_model = "ar1") add a
    first-order-autoregressive (broadband, peakless) low-frequency
    component, so a record whose low-frequency variability is not a
    sharp Mayer rhythm is fitted without positing an arbitrary resonance
    frequency.

                        Changes in version 0.1.0                        

First release: a digital-twin engine coupling mechanistic simulation of
movement and physiology with realistic sensor emulation and data
assimilation.

  - Mechanistic generators. A parameterised single-joint limb (forward
    dynamics) and mechanistic physiological/network generators: a
    Jansen-Rit neural-mass EEG column (jansenRit()), a McSharry
    limit-cycle electrocardiogram (ecgsyn()), a Windkessel arterial
    pressure model with a closed-loop baroreflex (windkessel(),
    baroreflex()), a respiration and respiratory-sinus-arrhythmia
    generator (respiration(), rsaTachogram()), Kuramoto and Matsuoka
    rhythms (kuramoto(), cpgMatsuoka()), and a closed-loop
    cardiovascular-respiratory model with emergent Mayer waves
    (cardioRespiratory()).
  - Sensor emulation. Forward inertial-measurement-unit, optical-marker,
    surface-electromyography and electrocardiogram models, with richer
    chains for multi-channel EMG crosstalk (emgArray()), multi-lead ECG
    (ecgMeasureLeads()) and analogue-to-digital conversion
    (adcQuantize()).
  - Data assimilation. Unscented, extended, particle and ensemble Kalman
    filters; rejection and sequential Monte-Carlo approximate Bayesian
    computation (abcCalibration(), abcSMC()); Markov-chain Monte-Carlo
    (metropolis()); particle MCMC and particle Gibbs for whole-waveform
    fitting (particleMCMC(), particleGibbs()); a Gaussian-process
    surrogate (gpEmulator()) and GP-accelerated calibration
    (gpCalibrate()); and Fisher-information optimal experimental design
    (optimalDesign()).
  - Personalisation and applications. Fit a movement twin to recorded
    IMU data (personalizeTwin()) or the closed-loop cardiovascular twin
    to heart-rate/blood- pressure variability (personalizeCardio(),
    personalizeCardioWaveform()); single- and population-level in-silico
    intervention (insilicoIntervention(), populationIntervention()); a
    clinical decision loop with a validation gate,
    uncertainty-propagated scoring and evidence reconciliation
    (clinicalDecision()); and labelled synthetic training-data
    generation (generateTrainingData()).
  - Validity. A verification-and-validation harness (validateTwin(),
    profileLikelihood(), sobolIndices()) and real-data validation cases
    (ECGSYN vs MIT-BIH, Jansen-Rit vs a resting EEG, and the closed loop
    vs Fantasia heart-rate variability) under inst/validation/.