Changes in version 0.1.0 (2026-10-06)                  

First release.

Robust and sparse group-level DCM

  - rsdcm() fits a robust, sparse group-level model to subject-level DCM
    parameters: Student-t weighting of subjects for robustness to
    outliers, a nonlocal product-moment (pMOM) spike-and-slab prior for
    sparse selection of group effects, and ReML-estimated
    between-subject variance components. It returns the group effects,
    their posterior inclusion probabilities, the per-subject robustness
    weights, and the learned spike/slab scales. See arXiv:2609.06379.
  - rsdcm_fit() is a convenience wrapper that assembles the inputs from
    a list of dcm_estimate() fits (the posterior Ep/Cp restricted to a
    chosen parameter field) and a between-subject design.
  - narps_dcm: subject-level DCM summaries for 48 subjects (with
    group/gender/age covariates), derived from the openly shared NARPS
    dataset. It is the runnable real-data example for rsdcm().
  - Progress is reported via message() and is silenceable, matching the
    rest of the package.

Dynamic Causal Modelling for fMRI (R port of SPM25)

  - dcm_estimate(), dcm_nlsi_GN(), dcm_int() ported from SPM25 with
    optimizations: propagator caching in the bilinear integrator,
    identity-projection fast path in dcm_diff(), diagonal fast paths in
    dcm_inv() / dcm_logdet(). Roughly 20x faster end-to-end than a
    literal port on representative DCMs.
  - toy_dcm dataset (three regions, 482 scans) for documentation,
    smoke-testing, and examples.
  - Vignette introduction with a worked end-to-end example.
  - Help pages for all exported functions.

Parametric Empirical Bayes (PEB)

  - Hierarchical (group-level) modelling of DCM parameters across
    subjects, ported from SPM25's spm_dcm_peb:
      - dcm_peb_prepare() / dcm_peb_run(): prepare and fit a second- or
        third-level PEB by variational Laplace.
      - dcm_peb_of_pebs(): third-level PEB-of-PEBs over a directory of
        subject PEBs, with a between-subject design.
      - dcm_peb_design(): build the group-level design matrix.
      - dcm_peb_files() / dcm_peb_load(): locate and load subject PEBs
        from .rds or MATLAB .mat files (.mat needs the suggested
        R.matlab package).
  - dcm_peb_of_pebs() does not write to disk unless a save_path is
    supplied.

Naming

Ported SPM routines keep the dcm_ prefix (from spm_), to avoid colliding
with an installed SPM-derived package and to make it obvious which
functions come from here: spm_dcm_estimate becomes dcm_estimate,
spm_nlsi_GN becomes dcm_nlsi_GN, spm_int becomes dcm_int, and so on.

Supported model variants

  - This release implements only the deterministic, single-state fMRI
    DCM (non-linear DCM, via a non-empty d array, is also supported).
  - The two-state, stochastic, and spectral / cross-spectral-density
    (induced) variants are not yet implemented. dcm_estimate() stops
    with an informative error if any of these options is switched on,
    instead of silently running the deterministic model. They are
    planned for a future update.

Behavioural changes vs. the MATLAB original

  - Progress messages from dcm_nlsi_GN() use message() and can be
    silenced with suppressMessages().
  - dcm_estimate() does not write to disk by default. Pass save = TRUE
    together with a file path to recover the SPM25 behaviour.
  - The previous .spm_env global is replaced by a package-internal
    environment whose mutable settings are exposed through
    rsdcm_options().