Package: fmrilss 0.2.0

fmrilss: Least Squares Separate (LSS) Analysis for fMRI Data

Implements efficient least squares separate (LSS) analysis for functional magnetic resonance imaging (fMRI) data, following Mumford et al. (2012) <doi:10.1016/j.neuroimage.2011.08.076>. LSS estimates trial-by-trial activation patterns in event-related designs. The package provides reference R implementations, optimized C++ backends, prewhitening, and methods for voxel-specific haemodynamic response functions, including a fast implementation of GLMsingle (Prince et al., 2022) <doi:10.7554/eLife.77599>.

Authors:Brad Buchsbaum [aut, cre]

fmrilss_0.2.0.tar.gz
fmrilss_0.2.0.zip(r-4.7-x86_64)fmrilss_0.2.0.zip(r-4.7-arm64)fmrilss_0.2.0.zip(r-4.6-x86_64)fmrilss_0.2.0.zip(r-4.6-arm64)fmrilss_0.2.0.zip(r-4.5-x86_64)
fmrilss_0.2.0.tgz(r-4.6-x86_64)fmrilss_0.2.0.tgz(r-4.6-arm64)fmrilss_0.2.0.tgz(r-4.5-x86_64)fmrilss_0.2.0.tgz(r-4.5-arm64)
fmrilss_0.2.0.tar.gz(r-4.7-arm64)fmrilss_0.2.0.tar.gz(r-4.7-x86_64)fmrilss_0.2.0.tar.gz(r-4.6-arm64)fmrilss_0.2.0.tar.gz(r-4.6-x86_64)
fmrilss_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
fmrilss/json (API)

# Install 'fmrilss' in R:
install.packages('fmrilss', repos = c('https://bbuchsbaum.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/bbuchsbaum/fmrilss/issues

Pkgdown/docs site:https://bbuchsbaum.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

openblascppopenmp

3.97 score 1 stars 2 packages 31 scripts 47 exports 45 dependencies

Last updated from:188d0dd908. Checks:13 WARNING, 1 ERROR, 1 OK. Indexed: yes.

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macos-release-arm64WARNING200
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windows-devel-arm64WARNING371
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windows-release-x86_64WARNING328
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wasm-releaseOK151

Exports:benchmark_mixed_solvecalculate_recovery_metricscompare_hrf_recoverycreate_lwu_gridestimate_voxel_hrffit_oasis_gridgenerate_lwu_datagenerate_rapid_designglmsingleglmsingle_designglmsingle_hrfglmsingle_hrf_libraryitem_build_designitem_compute_uitem_cvitem_fititem_from_lsaitem_predictitem_slice_foldlsalsslss_beta_cpplss_cpp_optimizedlss_designlss_naivelss_naive_fitlss_optimizedlss_optimized_fitlss_rank1lss_sbhmlss_sbhm_designlss_with_hrfmixed_precomputemixed_solvemixed_solve_cppmixed_solve_optimizedoasis_optionsplot_hrf_comparisonprewhiten_optionsproject_confoundsproject_confounds_cppsbhm_buildsbhm_hrfsbhm_matchsbhm_prepasssbhm_projectstglmnet_options

Dependencies:assertthatBHbigmemorybigmemory.sricachemclicodetoolscpp11farverfastmapfmriARfmrihrfforeachggplot2glmnetgluegtableisobanditeratorsjsonlitelabelinglatticelifecyclemagrittrMASSMatrixmemoisenumDerivpracmapurrrR6RColorBrewerRcppRcppArmadilloRcppEigenrlangroptimS7scalesshapesurvivaluuidvctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Benchmark Mixed Model Implementationsbenchmark_mixed_solve
Calculate HRF Recovery Metricscalculate_recovery_metrics
Single-trial betas from a glmsingle fitcoef.glmsingle_fit
Compare HRF Recovery Methodscompare_hrf_recovery
Create LWU HRF Grid for OASIS Searchcreate_lwu_grid
Estimate Voxel-wise HRF Basis Coefficientsestimate_voxel_hrf
Fit OASIS with HRF Grid Searchfit_oasis_grid
Option constructors for nested interfacesfmrilss_options
Generate Synthetic fMRI Data with LWU HRFgenerate_lwu_data
OASIS HRF Recovery Testing Functionsgenerate_rapid_design
GLMsingle single-trial response estimationglmsingle
fmridesign front end for glmsingle()glmsingle_design
GLMsingle canonical HRF and HRF libraryglmsingle_hrf glmsingle_hrf_library
Build ITEM design metadataitem_build_design
Compute ITEM trial covariance matrixitem_compute_u
Crossvalidated ITEM decodingitem_cv
Fit ITEM decoder weightsitem_fit
Build an ITEM bundle from LS-A estimatesitem_from_lsa
Predict targets from ITEM weightsitem_predict
Slice an ITEM bundle into train/test fold objectsitem_slice_fold
Least Squares All (LSA) Analysislsa
Least Squares Separate (LSS) Analysislss
Vectorized LSS Beta Computation Using C++lss_beta_cpp
A wrapper for the optimized C++ LSS implementationlss_cpp_optimized
LSS Analysis with fmridesign Objectslss_design
Convenience wrappers for modern 'lss()' usagelss_fit_wrappers
Naive Least Squares Separate (LSS) Analysislss_naive
Naive LSS with modern signaturelss_naive_fit
Optimized LSS Analysis (Pure R)lss_optimized
Optimized LSS with modern signaturelss_optimized_fit
Rank-1 GLM: joint voxel-wise HRF and trial amplitude estimationlss_rank1
End-to-End LSS with Shared-Basis HRF Matching (SBHM)lss_sbhm
SBHM Pipeline with fmridesign Modelslss_sbhm_design
Perform LSS using Voxel-wise HRFslss_with_hrf
LSSBeta objectLSSBeta
Precompute Workspace for Optimized Mixed Modelmixed_precompute
Mixed Model Solvermixed_solve mixed_solve_cpp
Optimized Mixed Model Solvermixed_solve_optimized
Construct OASIS optionsoasis_options
Plot HRF Recovery Comparisonplot_hrf_comparison
Construct prewhitening optionsprewhiten_options
Project Out Confound Variablesproject_confounds
Project Out Confounds Using C++project_confounds_cpp
Build a Shared-Basis HRF Library (SBHM)sbhm_build
Wrap a Learned Basis as an HRF (SBHM HRF)sbhm_hrf
Match Voxels to Library HRFs in Shared Basis (SBHM)sbhm_match
SBHM Prepass: Aggregate Fit in a Shared Basissbhm_prepass
Project Trial-wise SBHM Coefficients to Scalar Amplitudessbhm_project
Construct stglmnet backend optionsstglmnet_options
VoxelHRF objectVoxelHRF