Introduction | Trend Composition | Quick Start: From trend to model | Defining a Trend | Available Kernel Types | Linear Trends | Exponential Trends | Power Trends | Polynomial Trends | Learning Rule Trends | Multiple Trends | Sharing Parameters | Phases | Parameter Inputs (par_input) | Apply a trend only at a factor level (at) | Multiple contributions to the same parameter | Phase per trend entry | Sharing kernel parameters (not only base weights) | Missing input values (NA handling) | Trial‑wise evaluation and conditional covariates | Vary trend parameters via the design formula | Custom C++ Trends | Tips for Choosing Trends | Interpreting Bases and Transforms

Introduction | 1. Ordered Response Models | 2. Fit the Ordered Model

Introduction | 1. Specify a DDM Design | 2. Inspect Parameters and Mapping | 3. Simulate Data | 4. Set Prior and Build EMC Object | 5. Fit | 6. Summarize and Check Model Fit

Introduction | 1. Specify a Race-Model Design | 2. Inspect Parameters and Mapping | 3. Simulate Data | 4. Set Prior and Build EMC Object | 5. Fit | 6. Summarize and Check Model Fit

Introduction | How to SBC | 1. Specify the Probabilistic Model | 2. Simulate Parameters and Data | 3. Compute Posterior Distributions | 4. Evaluate the Rank Statistics | 5. Check Uniformity | Non-hierarchical SBC in EMC2 | Compression | LBA | DDM | Hierarchical SBC | Prior structure | RDM | LNR