Overview | The toy model | Simulating a BOLD response | Inverting the model | Model comparison | Tuning | Group-level modelling | Acknowledgment | References
Package: rsDCM 0.1.0
rsDCM: Robust and Sparse Dynamic Causal Modelling for Functional MRI
Provides a robust and sparse method for group-level Dynamic Causal Modelling (DCM) of functional magnetic resonance imaging (fMRI) data: Student-t weighting of subjects for robustness, combined with a nonlocal product-moment (pMOM) spike-and-slab prior for sparse selection of group-level effects (<doi:10.48550/arXiv.2609.06379>). The package also provides an R implementation of single-subject DCM for fMRI using variational Laplace inversion (Friston et al., 2003 <doi:10.1016/S1053-8119(03)00202-7>), including the bilinear neural state equation and the Buxton-Friston hemodynamic response model, ported from the 'SPM25' (version 25.01.02) toolbox for 'MATLAB'.
Authors:
rsDCM_0.1.0.tar.gz
rsDCM_0.1.0.zip(r-4.7-any)rsDCM_0.1.0.zip(r-4.6-any)rsDCM_0.1.0.zip(r-4.5-any)
rsDCM_0.1.0.tgz(r-4.6-any)rsDCM_0.1.0.tgz(r-4.5-any)
rsDCM_0.1.0.tar.gz(r-4.7-any)rsDCM_0.1.0.tar.gz(r-4.6-any)
rsDCM_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
rsDCM/json (API)
| # Install 'rsDCM' in R: |
| install.packages('rsDCM', repos = c('https://kay202.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/kay202/rsdcm/issues
Datasets:
Last updated from:99e0052e90. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 144 | ||
| source / vignettes | OK | 166 | ||
| linux-release | OK | 119 | ||
| macos-release | OK | 116 | ||
| macos-oldrel | OK | 102 | ||
| windows-devel | OK | 106 | ||
| windows-release | OK | 77 | ||
| windows-oldrel | OK | 80 | ||
| wasm-release | OK | 98 |
Exports:dcm_bireducedcm_blocks_to_matdcm_catdcm_Cedcm_data_iddcm_detrenddcm_diagdcm_diffdcm_dxdcm_endcm_estimatedcm_evidencedcm_expmdcm_fieldindicesdcm_find_pCdcm_find_rCdcm_fmri_mode_gendcm_fmri_priorsdcm_funcheckdcm_fx_fmridcm_fx_fmri2dcm_gx_fmridcm_intdcm_invdcm_kernelsdcm_lengthdcm_log_evidencedcm_log_evidence_reducedcm_logdetdcm_Ncdfdcm_nlsi_GNdcm_peb_designdcm_peb_filesdcm_peb_loaddcm_peb_of_pebsdcm_peb_preparedcm_peb_rundcm_phidcm_pinvdcm_Qdcm_speyedcm_svddcm_tracedcm_unvecdcm_vecdcm_zerosexpvpadmrsdcmrsdcm_fitrsdcm_options
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| rsDCM: Robust and Sparse Dynamic Causal Modelling for Functional MRI | rsDCM-package rsDCM |
| Estimate a Dynamic Causal Model for fMRI | dcm_estimate |
| Approximate model evidence (AIC, BIC) | dcm_evidence |
| Construct fMRI DCM priors | dcm_fmri_priors |
| fMRI neural and hemodynamic state equation | dcm_fx_fmri |
| fMRI BOLD observation equation | dcm_gx_fmri |
| Integrate a bilinear DCM | dcm_int |
| Variational Laplace inversion of a non-linear system | dcm_nlsi_GN |
| Run a third-level PEB-of-PEBs over a directory of subject PEBs | dcm_peb_of_pebs |
| Prepare a PEB (second- or third-level) model | dcm_peb_prepare |
| Fit a prepared PEB model by variational Laplace | dcm_peb_run |
| Subject-level DCM summaries for a group analysis (NARPS) | narps_dcm |
| Robust and sparse group-level DCM (Student-t + pMOM) | rsdcm |
| Robust sparse group DCM from a list of fitted DCMs | rsdcm_fit |
| Get or set rsDCM runtime options | rsdcm_options |
| Example DCM for fMRI inversion | toy_dcm |