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Changelog

Source: NEWS.md

fmrihrf 0.4.0

Improvements

  • Added feature_regressor() for continuously sampled features (for example RMS energy). Each sample is a zero-order-hold bin of width dt, with optional pre-convolution centering and scaling. This is the continuous analogue of an amplitude-modulated event train, not a list of trials.

  • Added fixed-scale HRF normalization with normalize_hrf() and the hrf_norm argument to gen_hrf() and getHRF(). Modes include Nilearn/SPM reference-grid scaling, unit peak, unit integral, and independent per-basis unit peaks. The existing normalise_hrf() and normalize = TRUE interfaces retain their per-basis unit-peak behavior.

  • Factored the duplicated single-basis / multi-basis branching in block_hrf(), evaluate.HRF(), and normalise_hrf() into three internal helpers (.weighted_combine, .normalise_result, .get_peaks). No user-visible behavior change.

Convolution engine consolidation

The package carried four evaluation engines. conv provided the best speed/accuracy tradeoff in development benchmarks, so there is now one compiled convolution engine.

  • method = "conv" remains the default and is the only compiled convolution engine. It avoids FFT zero-padding overhead and is substantially faster than loop on large designs; exact speedups depend on design size, basis count, and precision.

  • method = "fft" and method = "Rconv" are deprecated. They now evaluate via conv and warn. The FFT engine never repaid its zero-padding, because the HRF is short relative to the sampled design, and it was the only method that could fail outright (on an internal FFT size above ~1e7). Rconv was an R reimplementation of conv that silently fell back to loop whenever the grid was irregular or durations varied. Both implementations have been removed.

  • method = "loop" is retained as the reference implementation. It can be more accurate at a given precision because event onsets are evaluated directly, and it remains the automatic fallback for regressors built from a list of per-event HRFs.

  • Event onsets and block edges are no longer snapped to the internal grid. Each is placed at its exact sub-bin position, substantially reducing off-grid error at the default precision = 0.33. Blocks are projected onto the linear hat basis, preserving trapezoid accuracy while keeping the exact edge placement.

  • method = "loop" no longer truncates blocked events at hrf_span. It now extends to hrf_span + duration, removing a residual error that did not shrink as precision decreased. Its support calculation no longer assumes a multi-point regular grid, so one-point and irregular grids work as well. Blocks whose onset precedes the requested grid are retained when their duration carries the response into it.

Bug Fixes

Addresses the defects reported in issue #45.

  • Breaking: epoch (duration > 0) regressors are no longer scaled by 1/precision. evaluate() on a Reg object summed microtime samples of a unit-height boxcar without a step-size factor, so the amplitude of every epoch regressor grew as the precision argument shrank – at the default precision = 0.33 an epoch column was inflated roughly 3.2x relative to the integral it was meant to approximate. The compiled engines now apply the same trapezoid quadrature evaluate.HRF() uses, so a block response is amplitude * integral h(t - onset - u) du over the block and converges as precision decreases. Point events (duration = 0) are unaffected. Fitted betas from epoch designs will change scale; model fit and t-statistics will not.

  • Breaking: summate = FALSE now takes effect on every evaluation method. It was silently ignored by the conv (the default), fft, and Rconv engines, which never received the flag; only method = "loop" honoured it.

  • Breaking: evaluate.HRF() selects its impulse and block branches on duration alone rather than on duration < precision. A numerical setting could previously decide which model was evaluated: at precision = 0.2 a duration of 0.19 was treated as an impulse and 0.20 as a block, a five-fold amplitude jump. Durations smaller than precision are now integrated as blocks.

  • The accepted evaluation methods (conv, fft, Rconv, loop) now agree to within quadrature error on block regressors; the deprecated names fft and Rconv route to conv.

  • hrf_sine() and hrf_fourier() no longer error on a scalar t. vapply() dropped the result to a plain vector when length(t) == 1, so the support mask failed with “incorrect number of subscripts on matrix”. This was reachable from public API via lag_hrf(), block_hrf(), and getHRF("fourier").

  • HRF_BSPLINE and hrf_bspline_generator() now place interior knots from span, fixed when the object is constructed. splines::bs() was called without knots= and fell back to quantiles of whatever t was supplied, so the basis depended on the evaluation grid and disagreed with hrf_bspline(). Evaluating on a single time point produced a degenerate basis.

  • The internal Daguerre basis normalizes each column against a fixed reference grid rather than against the caller’s t. Including negative lags inflated the divisor and shrank every returned value.

  • hrf_gaussian(), hrf_mexhat(), hrf_inv_logit(), and hrf_lwu() return 0 for t < 0, as the other kernels already did. The regressor path masked negative lags itself, but block_hrf() and lag_hrf() sample the shape function directly and mixed in pre-onset values – up to 2% of peak for a blocked Mexican hat. Note that hrf_mexhat() is discontinuous at t = 0 as a result, since its formula is non-zero there.

fmrihrf 0.3.1

New Features

Improvements

  • Removed an unused suggested dependency and tightened build-ignore rules for local check artifacts.

fmrihrf 0.3.0

CRAN release: 2026-03-28

Improvements

  • Consolidated derivative method Rd aliases into parent help pages, reducing documentation redundancy.
  • Added explicit importFrom(utils, tail) to avoid R CMD check NOTEs.

fmrihrf 0.2.1

Bug Fixes

  • Fixed hrf_bspline() support handling so values for t > span (and t < 0) are zeroed instead of wrapping to onset-like values.
  • Fixed block_hrf() block integration to include quadrature step-size scaling, making amplitudes stable across precision.
  • Fixed hrf_sine() and hrf_fourier() to clamp support to [0, span] and return zero outside the modeled window.
  • Fixed normalise_hrf() to use fixed normalization constants computed on the HRF support, avoiding data-dependent scaling across evaluation grids.
  • Fixed evaluate.HRF() block-duration summation to use the same weighted integration scheme as block_hrf().
  • Fixed evaluate.Reg(normalize = TRUE) to normalize regressor outputs consistently across evaluation methods, including single-trial regressors with different durations.
  • Fixed block_hrf(summate = FALSE) to return normalized block integration (for both single- and multi-basis HRFs) instead of the legacy pointwise-maximum behavior.

fmrihrf 0.2.0

CRAN release: 2026-02-09

New Features

  • New hrf_boxcar() function for simple boxcar (step function) HRFs with optional normalization.
  • New hrf_weighted() function for arbitrary weighted-window HRFs with constant or linear interpolation.
  • regressor() now accepts a list of HRF objects for trial-varying HRF designs.
  • New plot.Reg() method for visualizing regressor objects.
  • New plot_regressors() for comparing multiple regressors on one plot (ggplot2 or base R).
  • New plot_hrfs() for comparing multiple HRF shapes.
  • New print.HRF() method for concise HRF summaries.

Improvements

  • Revised hemodynamic response and regressor vignettes.
  • Expanded test suite for new HRF types and trial-varying regressors.

Bug Fixes

  • Fixed critical bug in as_hrf() where parameters stored in the params attribute were never used at evaluation time. The fix creates a closure that properly captures and applies parameters during evaluation.

fmrihrf 0.1.0

CRAN release: 2025-09-16

  • Initial CRAN release