The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

rsDCM

rsDCM is an R package for Dynamic Causal Modelling (DCM) of functional MRI data. It is an R port of the corresponding routines in the MATLAB SPM25 toolbox, with optimizations for the integration and Gauss-Newton inner loops.

Installation

From GitHub (development version):

# install.packages("remotes")
remotes::install_github("Kay202/rsDCM")

From CRAN, once published:

install.packages("rsDCM")

Quick start

library(rsDCM)

# Three-region toy DCM shipped with the package (482 scans, one input)
data(toy_dcm)

# Invert. Takes roughly 75 seconds on this dataset.
fit <- dcm_estimate(toy_dcm)

# Posterior connectivity
round(fit$Ep$A, 3)

# Variational free energy (log-evidence proxy)
fit$F

The introductory vignette has more detail:

vignette("introduction", package = "rsDCM")

What’s exported

User-facing entry points:

• dcm_estimate(): full DCM inversion.
• dcm_fmri_priors(): build priors from adjacency arrays.
• dcm_nlsi_GN(): variational Laplace optimisation (lower-level).
• dcm_int(): bilinear-system integrator (lower-level).
• dcm_fx_fmri(), dcm_gx_fmri(): neural and BOLD equations.
• dcm_evidence(): AIC / BIC summary of a fit.
• rsdcm_options(): adjust runtime options (e.g. FD step).

Group-level (Parametric Empirical Bayes):

• dcm_peb_prepare(), dcm_peb_run(): fit a second- or third-level PEB.
• dcm_peb_of_pebs(): third-level PEB-of-PEBs over a directory of subject PEBs.
• dcm_peb_design(): build the between-subject design matrix.
• dcm_peb_files(), dcm_peb_load(): locate/load subject PEBs (.rds or MATLAB .mat).

Group-level (robust and sparse, Student-t + pMOM):

• rsdcm(): robust, sparse group DCM using Student-t subject weighting, a nonlocal product-moment (pMOM) spike-and-slab prior (inclusion probabilities), and ReML variance components.
• rsdcm_fit(): wrapper that assembles the group model from a list of dcm_estimate() fits.

The narps_dcm dataset (48 subjects, from the openly shared NARPS data) is the runnable real-data example for rsdcm().

Lower-level utilities (dcm_vec, dcm_inv, dcm_logdet, …) are also exported and have their own help pages, so you can call them directly as dcm_vec(). They are marked @keywords internal, which only keeps them out of the package index. Access is not restricted, and the ::: operator is not needed for them.

Acknowledgment

rsDCM is a derivative work of the MATLAB SPM25 toolbox, distributed under GPL-2 by the Wellcome Centre for Human Neuroimaging. See LICENSE.note for the list of ported routines and the references.

License

GPL-2.

References

• Arhin, G., Sanyal, N. (2026). Robust and sparse group dynamic causal modeling via Student-t parametric empirical Bayes and nonlocal priors. arXiv:2609.06379. https://doi.org/10.48550/arXiv.2609.06379
• Botvinik-Nezer, R., Holzmeister, F., Camerer, C.F., et al. (2020). Variability in the analysis of a single neuroimaging dataset by many teams. Nature, 582(7810), 84-88. https://doi.org/10.1038/s41586-020-2314-9
• Friston, K.J., Harrison, L., Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273-1302. https://doi.org/10.1016/S1053-8119(03)00202-7
• Friston, K.J., Mattout, J., Trujillo-Barreto, N., Ashburner, J., Penny, W. (2007). Variational free energy and the Laplace approximation. NeuroImage, 34(1), 220-234. https://doi.org/10.1016/j.neuroimage.2006.08.035
• Valério, D., Peres, A., Bergström, F., Seidel, P., Almeida, J. (2025). Neural and behavioral similarity-driven tuning curves for manipulable objects. Imaging Neuroscience, 3. https://doi.org/10.1162/imag_a_00482

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.