Package index • fmrilss

Single-trial estimation

LSSBeta: LSSBeta object
coef(<glmsingle_fit>): Single-trial betas from a glmsingle fit
glmsingle(): GLMsingle single-trial response estimation
glmsingle_design(): fmridesign front end for glmsingle()
lsa(): Least Squares All (LSA) Analysis
lss(): Least Squares Separate (LSS) Analysis
lss_beta_cpp(): Vectorized LSS Beta Computation Using C++
lss_cpp_optimized(): A wrapper for the optimized C++ LSS implementation
lss_design(): LSS Analysis with fmridesign Objects
lss_fit_wrappers: Convenience wrappers for modern lss() usage
lss_fused_optim_cpp(): Fused Single-Pass LSS Solver (C++)
lss_naive(): Naive Least Squares Separate (LSS) Analysis
lss_naive_fit(): Naive LSS with modern signature
lss_optimized(): Optimized LSS Analysis (Pure R)
lss_optimized_fit(): Optimized LSS with modern signature
lss_rank1(): Rank-1 GLM: joint voxel-wise HRF and trial amplitude estimation

VoxelHRF: VoxelHRF object
calculate_recovery_metrics(): Calculate HRF Recovery Metrics
compare_hrf_recovery(): Compare HRF Recovery Methods
create_lwu_grid(): Create LWU HRF Grid for OASIS Search
estimate_voxel_hrf(): Estimate Voxel-wise HRF Basis Coefficients
generate_lwu_data(): Generate Synthetic fMRI Data with LWU HRF
generate_rapid_design(): OASIS HRF Recovery Testing Functions
glmsingle_hrf_library() glmsingle_hrf(): GLMsingle canonical HRF and HRF library
lss_with_hrf(): Perform LSS using Voxel-wise HRFs
lss_with_hrf_pure_r(): Least Squares Separate with voxel-wise HRF (basis-weighted)
plot_hrf_comparison(): Plot HRF Recovery Comparison

.sbhm_adaptive_ridge_gls(): Adaptive fractional ridge per voxel from conditioning
.sbhm_auto_gate_threshold(): Auto-set cond_gate threshold from observed diagnostics
.sbhm_build_trial_regs(): Build per-trial basis regressors (each T x r) from SBHM basis + design_spec
.sbhm_prewhiten(): Optionally prewhiten Y, regs (stacked), intercept and nuisance
.sbhm_resid(): Residualize columns of M against Z (FWL)
.sbhm_resolve_ridge(): Resolve ridge value from spec (absolute or fractional)
.sbhm_solve(): Stable linear solve for multiple RHS with tiny ridge
lss_sbhm(): End-to-End LSS with Shared-Basis HRF Matching (SBHM)
lss_sbhm_design(): SBHM Pipeline with fmridesign Models
sbhm_amplitude_ls(): Single-shape GLM amplitudes given matched coordinates (global LS)
sbhm_amplitude_lss1(): Single-shape LSS amplitudes (2x2 per trial) given matched coordinates
sbhm_amplitude_oasis_k1(): OASIS K=1 amplitudes per voxel with matched HRF columns. Returns list(beta, se)
sbhm_build(): Build a Shared-Basis HRF Library (SBHM)
sbhm_hrf(): Wrap a Learned Basis as an HRF (SBHM HRF)
sbhm_match(): Match Voxels to Library HRFs in Shared Basis (SBHM)
sbhm_prepass(): SBHM Prepass: Aggregate Fit in a Shared Basis
sbhm_project(): Project Trial-wise SBHM Coefficients to Scalar Amplitudes

.convert_legacy_whiten(): Convert old-style whitening options to new format
.mixed_solve_cpp(): Mixed Model Solver using C++
.needs_advanced_prewhitening(): Check if advanced prewhitening is needed
benchmark_mixed_solve(): Benchmark Mixed Model Implementations
fit_oasis_grid(): Fit OASIS with HRF Grid Search
fmrilss-package fmrilss: fmrilss: Least Squares Separate (LSS) Analysis for fMRI Data
fmrilss_options: Option constructors for nested interfaces
mixed_precompute(): Precompute Workspace for Optimized Mixed Model
mixed_solve() mixed_solve_cpp(): Mixed Model Solver
mixed_solve_optimized(): Optimized Mixed Model Solver
oasis_options(): Construct OASIS options
prewhiten_options(): Construct prewhitening options
project_confounds(): Project Out Confound Variables
project_confounds_cpp(): Project Out Confounds Using C++
stglmnet_options(): Construct stglmnet backend options