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Changelog

Source: NEWS.md

fmrihrf 0.4.0.9000

Changes that affect results

  • regressor_design() now convolves each run (block of the sampling frame) separately. Previously it mapped all onsets to session time and convolved once over the whole session, so the response to an event near the end of one run (including its undershoot) spilled into the first scans of the next run. Each run’s rows now depend only on that run’s events and are identical to evaluating that run on its own. Design matrices with events within roughly one HRF span of a run’s end change; rebuild them and refit affected analyses (#62).

  • hrf_mexhat() now uses the standard Mexican-hat (Ricker) normalising constant 2 / (sqrt(3 * sd) * pi^(1/4)), so the wavelet has unit L2 norm. The previous constant, sqrt(2 / (3 * sd * pi^(1/4))), was a mis-transcription that gave neither unit norm nor unit peak. Every value is now sqrt(2) * pi^(-1/8) (about 1.2257) times larger, so coefficients for Mexican-hat regressors shrink by that factor.

  • hrf_spmg1() now scales its undershoot as t^P2 * exp(-t) / (6 * P2!). The normaliser was hard-coded as 6 * 15!, so any P2 other than the default 15 gave a wrongly scaled undershoot. The analytic derivatives behind deriv() are fixed the same way. Results with the default P2 are bit-identical.

  • neural_input() now returns the stimulus function that evaluate()’s default "conv" method actually convolves, as a density on seq(start, end, by = resolution). Zero-duration events, which were missing, now appear as spikes of area amplitude; block edges carry fractional weights; blocks shorter than resolution keep their area amplitude * duration; and blocks of a summate = FALSE regressor have unit area. The return structure and defaults are unchanged.

  • evaluate() with the "conv" method no longer over-weights a block that starts within one precision step of one HRF span before the first grid point. Such a block gained up to half a step of extra mass, so its response depended on which grid points were requested. The effect is negligible for canonical HRFs and noticeable for boxcar- or FIR-like kernels that are non-zero at their span.

  • make_hrf() (and therefore regressor(hrf = "<name>")) now gives basis sets their default size when nbasis is omitted; its nbasis default is now NULL instead of 1. Previously make_hrf("bspline", lag) and regressor(hrf = "bspline") failed, make_hrf("fir", lag) built a single 24 s bin, and a plain function returning several columns was labelled as one basis function. An explicit nbasis behaves as before.

Deprecated

  • gen_hrf_blocked() and its alias hrf_blocked() are formally deprecated. They already warned, but their help page did not say so and other help pages listed them alongside the current generators. The warning and help now point to block_hrf(hrf, width = ) for an existing HRF object and to gen_hrf(hrf, width = ), which they call.

Bug fixes

  • regressor_design() and evaluate() on a RegSet now return named columns: the factor levels, or <level>_basis<k> when the HRF has more than one basis function (the same names the design CLI command writes) (#63).

  • getHRF("lwu", ...) now works. It returns a single-basis LWU HRF that takes tau, sigma and rho through ... and supports the usual lag, width and normalisation options; previously it always failed with ‘argument “t” is missing’. list_available_hrfs() reports its nbasis_default as “1”.

  • gen_hrf_lagged() / hrf_lagged() with a vector of lags now return an HRF basis set with one block of columns per lag, as documented. They previously emitted a deprecation warning and failed, and dropped normalize. With normalize = TRUE, an all-zero response now stays zero instead of NaN.

  • hrf_basis_lwu() now works for every theta0 that passes its own validation, including rho = 0, rho = 1.5 and sigma just above 0.05, using one-sided numerical derivatives at those bounds. It is also about 300 times faster (one Jacobian instead of one gradient per time point).

  • HRF_SPMG1 now has class c("SPMG1_HRF", "HRF", "function"), like HRF_SPMG2 and HRF_SPMG3, so deriv(HRF_SPMG1, t) uses the analytic derivative as documented instead of numerical differentiation (values change by at most 3e-13).

  • as_hrf() and gen_hrf() accept function literals that deparse to several lines; they failed with “length(name) not equal to 1”.

  • B-spline and tent HRFs can be evaluated on an empty time grid (tent bases errored, B-spline bases warned). hrf_bspline_generator(nbasis = 1) and getHRF("bspline", nbasis = 1) now stop with “nbasis must be at least 2 for a cubic B-spline basis” instead of building an all-zero basis that warned on every evaluation.

  • penalty_matrix() for a one-function Daguerre basis returns a 1 x 1 zero matrix instead of a 0 x 0 one.

  • list_available_hrfs() marks "gam" as an alias of "gamma".

  • print() of an HRF shows vector-valued parameters (e.g. from hrf_weighted()) as [a, b, ...] instead of c("0.0000", ...).

  • shift() keeps the zero-amplitude events of a regressor built with drop_zero_amplitude = FALSE.

  • regressor() rejects summate = NA; the "conv" method silently treated it as FALSE and "loop" errored.

  • Regressors without events behave consistently: a regressor built from a list of per-event HRFs keeps the first HRF (no span = -Inf warning), neural_input() no longer errors when end is not given, evaluate() on a regressor_set() with no levels returns a length(grid) x 0 matrix instead of NULL, and regressor_design() with no events returns a zero-column matrix instead of erroring.

  • sampling_frame() rejects block lengths that are not whole numbers. They were silently truncated by samples() and blockids(), and print() failed on them.

  • scale_colour_hrf() and scale_fill_hrf() work with ggplot2 < 3.5.0 (they failed with ‘argument “scale_name” is missing’).

  • plot() on HRF, regressor and feature-regressor objects lets arguments in ... (main, ylab, labels, type, and in stacked layouts xlab and xaxt) override the method’s defaults instead of failing with “matched by multiple actual arguments”. plot() on a regressor without events draws an empty plot instead of failing. print() on a plot_hrfs() / plot_regressors() result without an attached ggplot returns the object unchanged.

  • plot_hrfs() and plot_regressors() with use_ggplot = FALSE now honour layout = "stack" (one panel per curve on a shared time axis), scales, subtitle and, for plot_hrfs(), the reference caption. The base-graphics fallback used to draw every curve in one panel and ignore these arguments. It also colours event marks by regressor for show_onsets = TRUE, as documented, and draws sample points in each panel. plot() on regressors shares the same code and is unchanged.

  • The fmrihrf command-line tool:

    • validates every option value it is given, so --nbasis x, --span 2,3 or an empty --output= (which discarded the output) are usage errors (exit status 2);
    • keeps every = after the first in --key=value;
    • writes --json output at full precision (values were rounded to 4 decimals, so anything below 5e-5 became 0);
    • lists every accepted option and default in its help;
    • makes fmrihrf design fail with “Missing required column(s)” when a column named with --duration or --amplitude is absent, instead of silently using zero durations or unit amplitudes.

  • The fmrihrf.hrf_cache_max_size option is now read when the package is loaded; the HRF evaluation cache used to be created when the package was built.

Performance

  • Regressor evaluation is much faster. The compiled engine is single-threaded by design, but src/Makevars still enabled OpenMP, which made Armadillo start threads for every convolution with 64 or more taps (including the defaults). It also now computes only the convolution samples the output grid needs. Results are unchanged (differences at most 1.3e-15). Building fmridesign event models is 5-40 times faster end to end.

  • FIR basis evaluation is vectorised (about 5 times faster).

Internal changes

  • gen_hrf() and getHRF() share one decorator pipeline, the basis generators share one constructor, and the duplicated deriv() and penalty_matrix() branches are merged. The CLI uses one table of options per command, and the plotting functions share their data-building helpers.

  • Removed unused internals (makeDeriv(), soft_threshold(), hrf_ident(), an old copy of hrf_half_cosine(), neural_input_rcpp(), …) and unused imports. cachem is now declared in Imports, and LazyData is no longer set (there is no data/ directory).

  • The reconstruction_matrix() help page no longer has a wrong title and description, and references to the non-existent HRF_DAGUERRE and HRF_TENT are gone.

fmrihrf 0.4.0

CRAN release: 2026-10-04

Breaking changes

  • block_hrf(normalize = TRUE) now computes each basis’s absolute-peak normalization factor once, over the full blocked span, using the fixed reference-grid policy of normalize_hrf(..., "unit_peak_per_basis"). Previously each call normalized against its requested times: a scalar evaluation could become 1 even far from the peak, and the convolution and loop methods could disagree. Scalar, vector, and chunked queries now retain the same scale. Negative responses keep their sign and zero bases stay zero. Rebuild designs and refit analyses that used normalized blocked HRFs.

  • Every positive block_hrf() width is now integrated, including widths below precision. Previously a short block returned the unintegrated impulse response, creating a discontinuity when width crossed the numerical step. width = 0 retains the impulse convention. With summate = FALSE, the integral is divided by width; with finite half_life, decay attenuates the integrand but does not renormalize the averaging weights.

  • hrf_bspline(), hrf_bspline_generator() and HRF_BSPLINE now return a basis that is zero at both ends of the span. Previously the last basis function equalled 1 at span and was cut to 0 beyond it, so any weight on it produced a step at the end of every response. The N functions are now the interior functions of a clamped basis with N + 2 functions (the same construction as fmrireg::estimate_hrf()). Fitted coefficients and design matrices built from these bases change; N must be at least degree - 1. The tent basis (hrf_tent_generator(), getHRF("tent"), and hrf_bspline(degree = 1)) changes the same way: its tents now peak at interior knots and the basis is zero at both ends (with N = 5 over 24 s, peaks at 4, 8, …, 20 s instead of 4.8, …, 24 s).

  • hrf_weighted(method = "constant") now uses every weight: n weights fill n bins. With width, the window is split into n equal bins; with times, weight i covers [times[i], times[i + 1]) and the last bin is as wide as the one before it. Previously the last weight only closed the window (and was returned at the single time times[n]). normalize = TRUE now makes all n weights sum to 1. The method = "linear" form is unchanged. Weight vectors that ended with a 0 as an end marker give the same values as before, with span one bin longer.

  • Corrected the documentation of normalize in hrf_boxcar() and hrf_weighted(). A unit-area boxcar makes the GLM coefficient the integrated signal in the window, not its mean, and a weighted HRF’s coefficient is a least-squares amplitude, not a weighted mean. Behaviour is unchanged.

Plotting

  • New hrf_palette() and matching ggplot2 scales scale_colour_hrf(), scale_color_hrf() and scale_fill_hrf(). The categorical palette has at least 3:1 contrast on white and near-black backgrounds and stays distinguishable under common colour-vision deficiencies; the ordered palette is for basis functions and parameter sweeps. All package plots use them.

  • plot_hrfs() and plot_regressors() now plot every column of a basis set (basis = "first" restores the old behaviour), accept layout = "stack" for one panel per curve, and choose the palette with palette. plot_hrfs() gains reference for a dashed grey comparison curve. plot_regressors() accepts a regressor_set(), draws events as bars whose width is the event duration, and shows scan-time values with samples.

  • plot_regressors(), plot.Reg() and plot.FeatureReg() evaluate with a precision matched to the plotting grid by default (precision = NULL), so sharp HRF edges are drawn where they occur.

  • Inside knitr documents, plot_hrfs() and plot_regressors() return their result visibly and let knitr print the plot, like a ggplot object. This lets document themes add dark-mode figure versions. Assigning the result inside a chunk no longer draws it; print it explicitly.

  • The base-graphics plot() methods use the same palette, mark onsets with ticks on the time axis, and draw multi-basis regressors one panel per basis function (layout = "overlay" for the old style).

  • All vignette figures were redrawn with these helpers; the reconstruction section of the advanced vignette now fits basis weights to target HRFs.

Improvements

  • Improved vignette plots for narrow screens: compact legends, shared-axis panels for gamma libraries and reconstruction, and visible light/dark series colors. Comparison helpers respect the active ggplot2 theme and accept draw = FALSE for customization or vignette auto-printing.

  • Fixed clipped peak annotations and respected onset transparency in event and feature-regressor plots. Clarified the spline example’s 24-second support without changing its evaluated values.

  • Updated the website and vignettes to albersdown 2.1.0. Vignettes now use its self-contained output format with light, dark, and phone-sized figures, replacing the copied theme assets. Figure resolution is limited to keep the source package compact.

  • 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

  • Let the generated Rcpp wrapper translate native exceptions after C++ stack unwinding, removing the remaining direct Rf_error() call (issue #42).

  • Clarified that summate = FALSE computes a temporal average, whose peak can decrease for longer blocks; normalize = TRUE controls unit-peak scaling (issue #49).

  • Preserved parameter metadata in closed HRF constructors and decorators without incorrectly warning that the captured parameters would be ignored.

  • Fixed loop-based block regressors integrating the kernel beyond its declared span, where the convolution engine already truncated it. The restored SPMG undershoot exposed this existing tail discrepancy. Support is now applied before block integration on both paths.

  • Breaking: corrected the SPMG canonical positive coefficient from 0.0833 to 1/120 and used the exact undershoot coefficient 1/(6*15!). HRF_SPMG1, HRF_SPMG2, and HRF_SPMG3 now have the SPM double-gamma shape (about 8.9% undershoot relative to peak, previously 0.6%). This changes both raw scale and shape; existing analyses should be refitted.

  • Breaking: the third column of HRF_SPMG3 is now a genuine response dispersion derivative, holding positive-component mean and mass fixed and using SPM’s (h(d) - h(d + 0.01))/0.01 sign/step convention. Previously it was a second time derivative. deriv(HRF_SPMG3, ...) follows the corrected basis. Temporal derivatives remain analytic. These are raw, unorthogonalized continuous kernels; exact sampled SPM/Nilearn design compatibility is not implied. Existing normalize, normalise_hrf(), normalize_hrf(), and hrf_norm scaling policies are unchanged. Coefficients and derivative-based amplitude summaries must respect the selected scaling and basis geometry.

  • Fixed issue #50: feature_regressor() now rejects matrix and array inputs instead of silently flattening them into one long feature. Pass one feature column at a time.

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. Because this release also corrects response shapes and event timing, model fits and t-statistics cannot be assumed unchanged; rebuild design matrices and refit existing analyses.

  • 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