Fit Meta-Analysis and return packed covariance per voxel

Usage

fmri_meta_fit_cov(
  Y,
  V,
  X,
  method = c("pm", "dl", "fe", "reml"),
  robust = c("none", "huber"),
  huber_c = 1.345,
  robust_iter = 2,
  n_threads = getOption("fmrireg.num_threads", 0)
)

Arguments

Y: Numeric matrix of effect sizes (subjects x features)
V: Numeric matrix of variances (subjects x features)
X: Numeric matrix; design matrix (subjects x predictors), including intercept
method: Character scalar; meta-analysis method: "pm" (Paule-Mandel), "dl" (DerSimonian-Laird), "fe" (fixed-effects), or "reml" (REML, uses PM solver)
robust: Character scalar; robust estimation method: "none" or "huber"
huber_c: Numeric scalar; tuning constant for Huber M-estimator (default: 1.345). Smaller values provide more robust estimates but may reduce efficiency.
robust_iter: Integer scalar; number of IRLS iterations for robust estimation (default: 2)
n_threads: Integer scalar; number of OpenMP threads (0 = use all available)

Value

List with base outputs and cov_tri (tsize x P) where tsize = K*(K+1)/2