Changes in version 0.1.2                        

New functions

  - dyn_transitions(): first-order Markov transition probabilities
    between brain states. Takes a long-format timepoint-level data
    frame, computes source → target transitions within each group
    (subject × session), and returns a nested tibble of per-transition
    probabilities ready for grouped_perm_glmm(). Ported from
    clusters_markov() in the neonatal_dfc analysis pipeline (França et
    al., Nat Commun). remIntra = TRUE strips self-transitions before
    normalising.

Dependencies

  - Added dplyr, tidyr, and rlang to Imports (required by
    dyn_transitions()).

                        Changes in version 0.1.1                        

Bug fixes

  - bandpass_filter(): replaced gsignal::filtfilt() (zero initial
    conditions) with a scipy-compatible implementation that achieves
    bit-perfect parity with scipy.signal.filtfilt and
    scipy.signal.sosfiltfilt (max |diff| < 4e-12). The old
    implementation produced edge transients of up to ~0.24 signal units
    on real fMRI data due to zero initial state; this propagated into
    hilbert_phases() and dyn_phase_lock(), causing LEiDA eigenvectors to
    diverge from the Python dynfc reference pipeline.
    
    The fix introduces three internal helpers:
    
      - .lfilter_zi(): steady-state initial conditions via
        companion-matrix solve (equivalent to scipy.signal.lfilter_zi).
      - .lfilter(): Direct Form II Transposed IIR filter with explicit
        initial state (equivalent to scipy.signal.lfilter).
      - .odd_ext(): odd-reflection signal extension matching
        scipy.signal._arraytools.odd_ext exactly — right extension uses
        x[N-2 : N-n-2 : -1], padlen = 3 × max(length(b), length(a)).
    
    With these corrections, per-timepoint correlations between R and
    Python LEiDA eigenvectors are identically 1.0 across all sessions.

Tests

  - Added tests/testthat/test-bandpass_filter.R with six tests adapted
    from the scipy.signal.filtfilt documentation examples: output-length
    preservation, DC blocking, in-band sine-wave recovery, stopband
    attenuation (>40 dB), zero-phase property, and machine-precision
    agreement with scipy.signal.filtfilt on fMRI parameters (guarded by
    skip_if_not_installed("reticulate")).
  - Added reticulate to Suggests for the scipy cross-validation test.

                        Changes in version 0.1.0                        

  - Initial scaffold.
  - Ported core dynamic functional connectivity functions from the
    Python dynfc package.
  - Implements: bandpass_filter(), hilbert_phases(), corr_slide(),
    cofluct(), corr_corr(), dyn_phase_lock(), get_leida(), kuramoto(),
    shannon_entropy(), do_euclid().