NEWS

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PhysioClinStats 0.3.1

Documentation

• 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 3 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.

PhysioClinStats 0.3.0

• doseResponse(): therapy dose-response model relating a continuous/ordinal dose (hours/repetitions/sessions) to an outcome or change-from-baseline, with confounder adjustment, optional subject random intercepts (via fitMixedModel()), and linear / log / natural-spline / Emax dose forms. Returns an AnalysisResult with the dose effect, a predicted dose-response curve and the coefficient table. Honest scope: every coefficient is estimated from the data (no hard-coded slope), associational unless dose was randomised.

PhysioClinStats 0.2.1

• Relaxed the two LAPACK-derived medsens sensitivity-curve comparisons (acme_control/d0, ade_control/z0) in the pinned mediation fixture test from 1e-12 to 1e-8. These curves are RNG-invariant analytic functions computed through matrix/QR operations and drift by up to ~2.1e-10 relative across BLAS/LAPACK implementations (measured OpenBLAS vs reference LAPACK); the fixture was pinned under reference LAPACK. RNG-reproduced quantities keep 1e-12. Fixes the r-universe binary-check failure.

PhysioClinStats 0.2.0

Cross-tool validation

• Added strict offline parity fixtures for uncertainty, SCED, mixed/MMRM, survival, recovery, imputation, and causal inference. The gate rejects missing or mutated fixtures, stale surface inventory, direction drift, unexpected warnings, and unexecuted required rows.
• Added deterministic publication CSV, JSON, and Markdown tables plus an independent 100-row and 100-mutation audit.

Causal-inference workflows

• Added causalMediation(), a guarded mediation::mediate() workflow that verifies retained model rows, preserves the caller's RNG state, reports control, treated, and average ACME/ADE estimates, and records supported medsens() diagnostics.
• Added targetTrialProtocol() for explicit seven-component target-trial declarations with a separate time-zero rule and provenance representations for static and dynamic strategies.
• Added targetTrialEmulate() for intention-to-treat and per-protocol clone-censor-weight analyses of long person-period data. Raw and truncated weights, censor reasons, positivity and balance diagnostics, and clone-clustered robust uncertainty are retained in the result.
• Added guarded WeightIt and ipw parity paths for binary baseline IPTW.
• Added pinned offline backend fixtures and a 100-seed independent numerical validation with deliberate future-information, cumulative-product-grouping, and treatment-coding mutations.

These workflows estimate contrasts only under their stated consistency, exchangeability, positivity, time-zero, and model assumptions. They do not turn observational data into a randomized trial or prove a biological mechanism.

PhysioClinStats 0.1.0

Initial release of PhysioClinStats as a standalone package in the x-biosignal ecosystem. This package provides the clinical inference layer for rehabilitation and physiological signal studies, with heavy modelling backends kept as optional, guarded dependencies.

New Features

• Optional-backend detection and guarding for the clinical inference engine. Modelling packages are loaded only when present, so the package installs and loads without pulling in heavy dependencies.
  • clinStatsBackends() reports availability of the optional modelling backends (mmrm, emmeans, lme4, and SingleCaseES) as a named logical vector, letting callers check support before running an analysis.
  • requireBackend() asserts that a named optional backend is installed and otherwise stops early with an actionable install message.

Documentation

• Package-level documentation describing the intended scope: mixed-effects / MMRM longitudinal models, single-case (N-of-1) designs, estimands with multiple imputation, and per-estimate uncertainty.