var = 1 assay with a warning, matching gds_from_neurovols() behavior for raw maps without uncertainty images.gds_from_nifti_maps() workflows and the recommendation to use ols:voxelwise rather than fixed/random-effects meta-analysis when the variance assay is synthetic.gds_from_nifti_maps() contrast relabeling for one-contrast raw-map layouts.gds_from_scalar_maps() / as_scalar_map_gds(), group_ols(), one_sample(), two_sample(), and write_nifti_assays() with a per-file manifest.fmrigds CLI into a plan-oriented interface with probe, plan, preview, run, and list commands.--save-plan / --load-plan, plus repeatable passthrough flags for adapter, reducer, post-hoc, and writer options.cli-workflows vignette covering the main command patterns and advanced control flags.lmm:ri and lmm:ri_slope1.theta_mode = "pooled" and theta_mode = "voxelwise" for shared- vs sample-specific variance parameters.contrast_data_cols, so contrast metadata can be extracted directly from tabular sources.lme4 parity, pooled/voxelwise equivalence for identical responses, and sample-specific theta recovery.lme4-backed restricted LMM parity tests plus a scheduled benchmark workflow.fmrigds name.gds(<data.frame>) now works via the tabular adapter (no temporary files needed).roi_col alias in tabular ingestion to treat ROI/parcel labels as the sample axis.list(beta=..., se=...), directories, or vectors; classification by filename patterns for beta vs se.as_plan.gds() so verbs like reduce()/posthoc() accept realized GDS directly.plan(x) for as_plan(x).as_gds() to accept 2-D inputs and standardized all assays to 3-D [sample × subject × contrast] shapes.var from se (and vice versa when required) in reducer/posthoc preflight.fdr:spatial) posthoc with group-wise Simes + weighted BH; documented usage and options.cov:<term_i>:<term_j>) while retaining attachments for metadata.subjects.gds_plan() and contrasts.gds_plan().nifti_source(beta=NULL, se=NULL) for explicit NIfTI list sources.Guidance for integrators (fmrireg):
gds(df, format="tabular", roi_col="roi") or as_gds(df, mapping=list(roi="roi", ...)) over temporary CSV writes.plan(gds_obj) or as_plan(gds_obj) to chain reduce()/posthoc() on realized results.gds-h5) with probe and block-read support so plans can lazily stream assays from disk.inst/schemas/hdf5.md and wired helpers for writing simple gds stores.compute() now supports sink = "h5" and write_out() queues, emitting provenance-aware gds-h5 stores (with tests)./gds/alignments, rehydrated at load time, and available via align(plan, "<name>").