NEWS

PhysioTwin 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.

PhysioTwin 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.

PhysioTwin 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/.