Package: fmrihrf 0.3.1

Introduction | HRF Libraries: Systematic Parameter Exploration | Example 1: Library of Gamma HRFs | Example 2: Library of Lagged SPM HRFs | Reconstruction Matrices: From Coefficients to HRF Shapes | How Reconstruction Works | Interactive Visualization: Building an HRF Step by Step | Regressor Sets: Multi-Condition Experimental Designs | Regressor Design: Complex Block Designs

Introduction: What is a Regressor? | Basic Regressor from Event Onsets | Evaluating and Plotting a Regressor | Varying Event Durations | Duration and Summation | Varying Event Amplitudes (Parametric Modulation) | Combining Duration and Amplitude Modulation | Regressors with HRF Basis Sets | Shifting Regressors

Introduction to Hemodynamic Response Functions (HRFs) | Pre-defined HRF Objects | Modifying HRF Parameters with gen_hrf | Modeling Event Duration with block_hrf | Normalization | Modeling Saturation with summate | Modeling Temporal Shifts with lag_hrf | Combining Lag and Duration | Multivariate HRFs: Basis Sets | SPM Basis Sets | B-Spline Basis Set | Sine Basis Set | Half-Cosine Basis Set (FLOBS-like) | Other HRF Shapes | Gamma HRF | Mexican Hat Wavelet HRF | Inverse Logit Difference HRF | Boxcar and Weighted HRFs (No Hemodynamic Delay) | Simple Boxcar HRF (hrf_boxcar) | Normalized Boxcar: Estimating Mean Signal | Weighted HRF (hrf_weighted) | Using width for Evenly Spaced Weights | Using Explicit times for Custom Spacing | Smooth Weights (Linear Interpolation) | Sub-second Precision | Normalized Weighted HRF: Estimating Weighted Mean | Practical Example: Comparing Early vs. Late Response Windows | Using Boxcar/Weighted HRFs with Regressors | Creating Custom Basis Sets with gen_hrf_set | Creating Empirical HRFs | From a Single Measured Response (gen_empirical_hrf) | Empirical Basis Set via PCA

Why Generators? | Creating a Basis with a Generator | Visualising FIR Basis Functions | Summary