autohrf

1.1.3

Automated Generation of Data-Informed GLM Models in Task-Based fMRI Data Analysis

0packages depend

3.7Kdownloads / year

89.5%test coverage

13/13checks pass

Overview

About

Maintained by Jure DemšarFirst published 2022-07-215 releasesCRAN page ↗GitHub ↗

Analysis of task-related functional magnetic resonance imaging (fMRI) activity at the level of individual participants is commonly based on general linear modelling (GLM) that allows us to estimate to what extent the blood oxygenation level dependent (BOLD) signal can be explained by task response predictors specified in the GLM model. The predictors are constructed by convolving the hypothesised timecourse of neural activity with an assumed hemodynamic response function (HRF). To get valid and precise estimates of task response, it is important to construct a model of neural activity that best matches actual neuronal activity. The construction of models is most often driven by predefined assumptions on the components of brain activity and their duration based on the task design and specific aims of the study. However, our assumptions about the onset and duration of component processes might be wrong and can also differ across brain regions. This can result in inappropriate or suboptimal models, bad fitting of the model to the actual data and invalid estimations of brain activity. Here we present an approach in which theoretically driven models of task response are used to define constraints based on which the final model is derived computationally using the actual data. Specifically, we developed 'autohrf' — a package for the 'R' programming language that allows for data-driven estimation of HRF models. The package uses genetic algorithms to efficiently search for models that fit the underlying data well. The package uses automated parameter search to find the onset and duration of task predictors which result in the highest fitness of the resulting GLM based on the fMRI signal under predefined restrictions. We evaluate the usefulness of the 'autohrf' package on publicly available datasets of task-related fMRI activity. Our results suggest that by using 'autohrf' users can find better task related brain activity models in a quick and efficient manner.

Install

R (CRAN)pak (dev)Ubuntu (r2u)Fedora (COPR)openSUSE (OBS)

install.packages("autohrf", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

if (!requireNamespace("pak", quietly = TRUE))
  install.packages("pak", repos = "https://cloud.r-project.org/")
pak::pak("demsarjure/autohrf")

Development version from demsarjure/autohrf. Installs pak first if it is not already available.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-autohrf

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-autohrf

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-autohrf

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed).

Health

CRAN checks

13OK

Slowest check: 3.7 min · r-release-macos-x86_64

Code health

Yes

Tests · ratio 0.15

89.5%

Coverage · measured lines

100%

Documentation · exports

Dependencies · direct

Check history

OK2026-06-09
13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
ERROR2026-06-08
12 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
OK2026-03-10
14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

READMEYes · 337 wordsVignettesYes · dynamicpkgdown siteNoNEWSYes · 67% structuredCode of conductNoContributing guideNo

Examples that run: 100%
Documented parameters: 100%
Return-value docs: 100%
References docs: 0%

Downloads

3.7K

CRAN downloads in the past year

Rank #12,576 · ~10/day · ~306/mo

Releases R versions

Daily download trend is not available in this view yet.

25330 days

85090 days

3.7K1 year

Compare downloads with other packages →

Also on119 r2u58 autocran

Repository

GitHub ↗ demsarjure/autohrf

2Stars

0Forks

0Open issues

0Open PRs

0Releases

116Commits

2Contributors

License GPL-3.0 · 116 commits · Last activity 2024-01-16

Stars over time

2025-01-18 · 22026-07-07 · 2

Repository practices

Upstream repositoryBeta

3 development-tooling and community-health practices detected across 2 families in the upstream repository

Checks run against github.com/demsarjure/autohrf on 2026-07-19.

CRAN release process (2)

cran-comments.mdCRAN-SUBMISSION

Lint, format, editor (1)

RStudio project

How this is detected·Detection ruleset v1 (2026-07-18)

Dependencies

Declared dependencies

11 external dependencies (excludes base and recommended)

Depends (1)

R >= 3.5.0

Imports (9)

cowplot doParallel dplyr foreach ggplot2 gtools lubridate magrittr RColorBrewer (>= 1.1)

LinkingTo (0)

none

Suggests (2)

knitr testthat

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

Code & Tests

Code Composition

R 1,659 (56%)Rd 819 (27%)Vignettes 502 (17%)

Code characteristics

Cyclomatic complexity: 4.0 median / 15 max
Test cases: 15 / 0.15 per code line

Test coverage

Line coverage

89%

Expression

89.2%

Tests / Examples

89.5% / 82% ex

Functions

18 18 exported

Complexity

4.5 avg / 15 max

Call network

18 nodes / 15 edges

Call graph

Open call graph →

Loading call graph…

Lowest coverage

18 functions

Function	Cyclo	Coverage
plot_fitness exp	2	0%
plot_best_models exp	6	75%
convolve_events exp	4	82%
downsample exp	4	83%
fit_to_constraints exp	15	85%
plot_model exp	9	85%

Datasets

Bundled datasets · 2

Name	Class	Rows × Cols	Also ships in
flanker	data.frame	192 × 3	–
swm	data.frame	11,520 × 3	–

People & History

People (3)

Maintainer (1)

Jure Demšar

Maintainer, Author

Authors (3)

Jure Demšar

Maintainer, Author

Nina Purg

Author

Grega Repovš

Author

Package Timeline

5 releases. Pick two to compare their code metrics. R releases are shown for context.

R
R 4.6.0 released · 2026-04-24
R
R 4.5.0 released · 2025-04-11
R
R 4.4.0 released · 2024-04-24
1.1.3Latest
2024-01-16 · current release · diff ↗
R
R 4.3.0 released · 2023-04-21
1.1.2
2023-02-15 · diff ↗
1.1.0
2022-11-19 · diff ↗
1.0.4
2022-09-30 · diff ↗
1.0.3
2022-07-21
R
R 4.2.0 released · 2022-04-22

Package metadata

First published: 2022-07-21
Total releases: 5 / 4 yrs
License: GPL (>= 3) OSI
Minimum R: ≥ 3.5.0
Bundled data: 100 KB / 6 files
Download size: 2.0 MB
Installed size: not tracked yet
With dependencies: not tracked yet

Appears in task views

Medical Image Analysis

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