Vectorized LSS Beta Computation Using C++

Fast C++ implementation of least squares separate (LSS) beta estimation using vectorized matrix operations. Computes all trial betas in a single pass without loops.

Usage

lss_beta_cpp(C_projected, Y_projected)

Arguments

C_projected

Projected trial regressors (n x T) from project_confounds_cpp

Y_projected

Projected data (n x V) from project_confounds_cpp

Value

Beta matrix (T x V) with LSS estimates for each trial and voxel

Details

This vectorized implementation computes all LSS betas simultaneously using matrix algebra. It's significantly faster than per-trial loops and automatically benefits from BLAS multithreading. The algorithm handles numerical edge cases by setting problematic denominators to NaN.

For best performance on large datasets, ensure your R installation uses optimized BLAS (like OpenBLAS or Intel MKL).

Examples

if (FALSE) { # \dontrun{
# After projecting out confounds
result <- project_confounds_cpp(X_confounds, Y_data, C_trials)
betas <- lss_beta_cpp(result$Q_dmat_ran, result$residual_data)
} # }