MultimodalSRM 0.1.0

The first package release of MultimodalSRM, providing shared response models for observations across participants, modalities, and native sampling times.

Included

• R-MSRM (MultimodalSRM): regularized fitting, latent inference, held-out prediction, and participant calibration.
• GP-MSRM (BayesianMultimodalSRM): MAP estimation and supported Bayesian posterior workflows, with optional JAX/NumPyro dependencies.
• Explicit response filters, native timestamps, masks, missing streams, and target-excluded prediction.
• Fitted GP archive persistence, focused synthetic regression suites, and runnable R/GP MAP examples.
• Documentation, MIT licensing, and CI-gated PyPI publishing.

Installation

Python 3.12 or later:

python -m pip install "multimodalsrm==0.1.0"
python -m pip install "multimodalsrm[bayesian]==0.1.0"

Enable JAX_ENABLE_X64=true before Bayesian fitting. The examples live in the source repository at tag v0.1.0.

Scope

See the capability matrix for supported response families and inference backends. Learned FIR responses remain deferred. This software release does not establish general convergence, uncertainty calibration, physiological timing, or empirical recovery. Preserve original research environments for historical R pickle/joblib models and source-bound warmup checkpoints; see migration guidance.

Published on PyPI. The release workflow passed the complete Linux/macOS test matrix and all wheel/sdist installation checks before upload. Both published distribution hashes match the tested artifacts. Fresh PyPI installations passed the R fitting/prediction/calibration example and GP MAP prediction/archive replay, with dependency consistency checked.