Package: fmrigds 0.1.0

Why fmrigds? | What is a GDS? | Which path fits the summaries you have? | Plan first, compute later | Quick example | Building and inspecting a plan | Exploring a realised GDS | Tidy export | Deriving statistics | Group-level analysis with reduce() | Effects without first-level variance | z, t, or p summary statistics | Fixed-effects | Random-effects | Meta-regression | Repeated-measures mixed models | Post-hoc corrections | Writing results to disk | Working with NIfTI files | The pipeline at a glance | Next steps

What does the input look like? | What model is fitted? | What is the fastest end-to-end path? | How are the random effects estimated? | How do first-level variance estimates enter the model? | Should variance components be pooled or fitted voxelwise? | What if your repeated-measure metadata is already separate? | What is supported, and what is intentionally out of scope? | Where do you go next?

Why use the CLI? | What are we driving? | How do you get a result quickly? | How do you inspect a source before you commit to a model? | How do you separate planning from execution? | How do you preview a saved plan? | How do you reach more complex workflows? | How do you discover available methods? | Where should you go next?

What data do you need? | How do you run the examination? | How do you read the action plane? | What does the residual geometry add? | How do you inspect one retained subject? | What happens to the post-hoc conclusion? | How do you share the examination?

When do you need this? | Sample data | Chaining operations on realized objects | Standard FDR correction | Spatial FDR | Requirements for spatial FDR | Next steps

When do you need this? | Two paths for reading data | Native /gds layout (Path B) | Labeled volumes (LabeledVolumeSet) | Parcellated data (H5ParcellatedScan) | Temporal policies | Space type | Latent bases (LatentNeuroVec) | Reading subsets efficiently | Provenance tracking | Quick reference | Next steps

When do you need this? | Building a GDS from in-memory data | Masking: dealing with missing data | Why mask? | Setting up the example | Intersection mask (the default) | Threshold mask | Other mask rules | Masking in a pipeline | Alignment: bringing subjects into a common space | Why align? | Creating test data in a latent space | Providing per-subject rotation matrices | Bundling rotations into a MapFamily | Running the alignment | Alignment followed by group analysis | Other alignment types | Space transformations with map_to() | Why transform spaces? | Setting up a mapping | Applying the transformation | How uncertainty propagation works | Combining test statistics without effect estimates | Putting it all together | Space types at a glance | Next steps