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Changelog

Source: NEWS.md

dynR 0.1.5

New functions

  • leida_pipeline() / sw_pipeline(): high-level wrappers running the full phase-based or correlation-based pipeline in one call. Both accept an optional filter argument to skip bandpass filtering on pre-filtered data. Return S3 objects (dynR_leida / dynR_sw) with print() and plot() methods.

  • plot_fc(): FC matrix heatmap using the dynR diverging palette (deep indigo -> periwinkle -> brick red).

  • plot_synchrony(): Kuramoto R(t) time series with mean reference line.

  • plot_state_sequence(): brain state tile plot with dynR state palette.

  • plot.dynR_leida() / plot.dynR_sw(): S3 plot methods for pipeline outputs; dispatch to plot_synchrony() / plot_fc() or the RSS plot via type = c("synchrony"|"fc") and type = c("rss"|"fc").

  • batch_leida() / batch_sw(): apply the respective pipeline to multiple subjects at once. Accepts either a named list of [N x Tmax] matrices or a 3-D array [N x Tmax x subjects].

  • stack_leida(): combine LEiDA eigenvector matrices from a batch result into a single data frame or matrix for cross-subject K-means clustering.

  • stack_synchrony(): combine synchrony time series into a tidy long-format data frame with subject, timepoint, synchrony, and metastability columns.

Dependencies

  • ggplot2 moved from Suggests to Imports (required by plot functions).

dynR 0.1.4

Performance

Four additional compiled backends and one vectorisation, completing the C++ acceleration of all main compute paths:

  • get_leida(): C++ / LAPACK backend (get_leida_cpp()). Calls LAPACK dsyev directly via R_ext/Lapack.h; workspace allocated once and reused across all timepoints. dsyev returns eigenvalues in ascending order so the leading eigenvector is the last column (opposite of R’s eigen()). Parity vs eigen(): max diff < 2.5e-15.

  • kuramoto(): C++ backend (kuramoto_sync_cpp()). Replaces vapply + R complex exp(1i*x) with direct cos/sin accumulation and sqrt — no complex-number allocation. Parity vs R vapply reference: < 1e-14.

  • hilbert_phases(): vectorised with mvfft(). Replaces N per-parcel fft() calls in an R loop with two mvfft() calls on the full matrix. The Hilbert multiplier is broadcast across parcel columns in one step. (fft_factor/fft_work are not in R’s public API on macOS/Accelerate; mvfft achieves equivalent throughput without a bundled FFT.) Parity vs .hilbert_r() loop: < 1e-14.

  • corr_slide(): C++ backend (corr_slide_cpp()). Computes Pearson correlation directly on the input matrix without per-window submatrix allocation or transposition. Cross-products accumulated in the upper triangle with t-outer loop ordering (column-major sequential access). Tested for non-overlapping and overlapping (step = 10, 5) windows. Parity vs cor(): < 1e-10; removes stats::cor from Imports.

  • do_euclid(): vectorised with diff() + rowSums(). Loop over rows replaced by c(0, sqrt(rowSums(diff(x)^2))).

Tests

  • Added parity tests for all new backends: get_leida_cpp, kuramoto_sync_cpp, hilbert_phases (mvfft path), corr_slide_cpp, and dyn_phase_lock_cpp (bit-perfect, max diff == 0).
  • Added tests/testthat/test-leida.R covering dimensions, sign convention, and LAPACK/eigen parity.

dynR 0.1.3

Performance

  • dyn_phase_lock() now delegates the phase-locking loop to a compiled C++ backend (dyn_phase_lock_cpp()). The inner outer(cos(a-b)) R loop is replaced by a triple nested C++ loop that exploits symmetry (cos(a-b) = cos(b-a)): only the upper triangle is evaluated and mirrored, halving trigonometric operations. Adds Rcpp as a dependency.

Dependencies

  • Removed gsignal from Imports. Both remaining uses have been ported to base-R equivalents:
    • gsignal::butter() replaced by .butter_bandpass() — a direct port of scipy.signal.butter() (analog Butterworth LP prototype -> LP-to-BP transformation -> bilinear transform -> ZPK-to-TF). Coefficients validated against the [k, 0, -2k, 0, k] bandpass structure and existing scipy parity tests.
    • gsignal::hilbert() replaced by .hilbert_r() — FFT-based analytic signal using base-R fft(), equivalent to scipy.signal.hilbert(). Phase range confirmed [-pi, pi] on synthetic cosine.

dynR 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

dynR 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.

dynR 0.1.0