Package: fmrireg 0.3.0

Introduction | A complete fit first | Key Components and Workflow | 1. Data Representation | 2. Model Building | a) Hemodynamic Response Function (HRF) | b) Event Model (event_model) | c) Baseline Model (baseline_model) | d) Contrasts (pair_contrast, unit_contrast, etc.) | 3. Model Estimation (fmri_model, fmri_lm) | 4. Advanced Topics | Acceleration: Sketched / Nyström Analysis | Group-Level Meta-Analysis | Getting Started | Next Steps

Set up a synthetic dataset | Simulate data with AR(1) noise | Pack into an fmri dataset | Choose the approximation engine | Build parcels | Fit with Global AR + SRHT sketch | Fit with Parcel AR + SRHT sketch | Choosing a sketch | Compare sketching to an exact OLS baseline | Standard errors follow the estimator | Reduced-rank GLS with bootstrap standard errors | Landmark extension (full-voxel, global AR) | Choosing landmarks: practical guidance | What the comparisons establish | When to use which knob | Pros and cons | Practical defaults | Reproducibility tips | Next

Introduction to Contrasts | Example: A 2x2 Factorial Design | Basic Contrasts: pair_contrast | Defining Pair Contrasts | Computing Contrast Weights | Unit Contrasts: Comparing to Baseline | General Contrasts with contrast() | Contrasts for Main Effects and Interactions | Polynomial Contrasts for Ordered Factors | Helper Functions for Common Comparisons | Grouping Contrasts: contrast_set | Applying Contrasts in fmri_lm | Extracting Contrast Results | Execute an omnibus F-contrast | Visualizing Contrast Weights | Working with Contrast Weights | Conclusion | Next

Introduction to fMRI Linear Models | Simulating a Dataset for Analysis | Fitting a Linear Model | Accounting for Temporal Autocorrelation | Handling Outliers with Row-Wise Robust Fitting | Extracting Model Results | 1. Coefficient Estimates | 2. T-Statistics and P-Values | 3. Contrasts Between Conditions | Fitted HRF Curves | Estimating the HRF Shape from Data | Comparing Models with Different HRF Bases | Summary | From first‑level to group analysis (GDS) | Next

Introduction to fMRI Data Simulation | Simulating Clean BOLD Signals | Simulating Structured fMRI-like Noise | Power Spectrum Analysis | Creating a Complete Dataset with Signal and Noise | Simulating Matrix Time Series with Column-Specific Variation | Summary and Comparison | Next

Overview | Creating a Test Dataset with Known Connectivity | Modeling Scanner Drift | Connectivity Analysis Using fmrireg's GLM Framework | Validating the Results | Repeated-sampling calibration | Visualizing the Connectivity Map | Extending to Real Data | Summary

Introduction: Linking Data and Design | The sampling_frame | In-Memory Volumetric Data (neurovec_frame) | File-Based Volumetric Data (nifti_frame) | Matrix Data (matrix_frame) | Latent Data (latent_frame) | Reading a frame back | Next

Why use the plugin API? | How do you build a minimal dataset? | How do you register a custom basis? | How do you register a custom engine? | Which helper should you use? | How do capabilities affect the executed configuration? | How can you inspect engine specs? | What should extension authors remember?

Introduction | Available Benchmark Datasets | Loading and Exploring a Dataset | Examining the Data Structure | Visualizing the Data | Creating Design Matrices | Does the Benchmark Recover Its Own Ground Truth? | Comparing True vs Estimated Betas | Testing Different Datasets | HRF Variability Dataset | Trial Amplitude Variability | Summary | Next

Overview | Create a small ROI dataset | Fit group meta-analysis | Extract coefficients and a contrast | A quick visualization | Notes on voxelwise analysis | Minimal NIfTI Example (Reproducible) | Exact contrasts and stored covariance | Two-sample t-test (Welch and OLS) on NIfTI | Combining t-statistics only (Stouffer/Fisher/Lancaster) | Meta engine via fmri_ttest with weights | ROI t-only example (Stouffer and Lancaster) | A brief recap

The problem | A minimal, runnable example | Reducers: what each subject becomes | Discovering subjects from BIDS | Preflight before you fan out | Running: locally, in parallel, or on a cluster | Closing the loop: the group level | Summary | Next