1. Authentication | 2. The One-Stop Solution (formr_api_results) | What does formr_api_results do by default? | Customizing the Fetch | 3. Advanced: The Manual Pipeline | B. Reverse Coding (formr_api_reverse) | C. Aggregation (formr_api_aggregate) | 4. Full Workflow Example | 5. Troubleshooting | "My scale isn't calculating!"

Installation | The "Two Ways of Connecting" | Authentication | 1. Local Setup (One-time) | 1.1 Save your API Credentials | 1.2 Save your regular User Credentials (Classic) | 2. Authenticating in Scripts | 2.1 Thru the API | 2.2 With your User Credentials (Classic) | 3. Authenticating Inside formr Runs | Workflow Examples | Project Management (Push & Pull) | Fetching Results | Token Management & Security

Listing Files | Uploading Files | Single File | Multiple Files or Directories | Deleting Files | Cleaning Up (Delete All)

Prerequisites | Backing up a Study | The Local Development Workflow | 1. Initialize (Pull) | 2. Edit | 3. Push | Watch Mode | Managing Run Settings | Advanced: Run Structure (JSON)

Listing Your Runs | Creating a New Run | Configuring Run Settings | Managing Run Structure (JSON) | Exporting Structure | Importing Structure | Deleting a Run

Listing Sessions | Filter by Status | Pagination | Find Specific Participants | Creating Sessions | Controlling Sessions (Actions) | Use Case 1: Unsticking a User (Move Position) | Use Case 2: Cleaning Data (Toggle Testing) | Use Case 3: Ending Sessions

Listing Your Surveys | Inspecting and Downloading Surveys | Inspect Items in R | Download Survey Source (.xlsx) | Uploading or Updating a Survey | Google Sheets | Deleting a Survey

1. Why Run R Inside Your Study? | 2. Where the API Code Goes | A. Calculate Items (API Entry Point) | B. Inline R in Labels (Display + Fetch) | C. Inline R in Choices (Display) | 3. Your Toolkit | Authentication | Run Context | Fetching Data from Other Surveys | The current() Shorthand | 4. Walkthrough: Participant Counter | Run Structure | Calculate: participant_count | Survey: welcome | 5. Walkthrough: Real-Time Group Norms | Survey: feedback (label) | 6. Walkthrough: Dynamic Group Balancing | Calculate: pick_condition | Showif conditions | 7. Walkthrough: Synchronising with a Waiting Room | Participant experience | 8. Patterns for Robust Code | Next Steps

Introduction | Installing CmdStan | Compiling a model | Running MCMC | Posterior summary statistics | Summaries from the posterior package | CmdStan's stansummary utility | Posterior draws | Extracting draws | Plotting draws | Sampler diagnostics | Extracting diagnostic values for each iteration and chain | Sampler diagnostic warnings and summaries | CmdStan's diagnose utility | Running optimization and variational inference | Optimization | Laplace Approximation | Variational (ADVI) | Variational (Pathfinder) | Saving fitted model objects | Comparison with RStan | Additional resources

Introduction | Compilation | Immediate compilation | Delayed compilation | Pedantic check | Stan model variables | Executable location | Processing data | Named list of R objects | JSON file | R dump file | Writing CmdStan output to CSV | Default temporary files | Non-temporary files | Reading CmdStan output into R | Lazy CSV reading | read_cmdstan_csv() | as_cmdstan_fit() | Saving and accessing advanced algorithm info (latent dynamics) | Developing using CmdStanR | Pre-compiled Stan models in R packages | Troubleshooting and debugging

Introduction | Adding profiling statements to a Stan program | Accessing the profiling information from R | Comparing to a faster version of the model | Per-gradient timings, and memory usage | Accessing and saving the profile files | References

Loading data | Adding and changing metadata | Variable labels | Value labels | Adding scales | Metadata about the entire dataset | Exporting and sharing the data with metadata | Releasing the codebook publicly | The Codebook

Summary statistics | Extracting posterior draws/samples | Structured draws similar to rstan::extract()

The XOR operator | Stream ciphers | Symmetric encryption | Public-key encryption and Diffie-Hellman

Introduction | Graphical relationship | Regression | Write a partial on the fly | Debug

Summary | Tidyverse package | Components | Design principles | Acknowledgments | References

Reuse existing data structures | Compose simple functions with the pipe | Embrace functional programming | Design for humans

Using the StanHeaders Package from Other R Packages | Calling functions in the StanHeaders Package from R | Using Higher-Order Functions in the StanHeaders Package | Derivatives and Minimization | Integrals and Ordinary Differential Equations | Parellelization | map_rect Function | reduce_sum Function | Defining a Stan Model in C++

Option 1: Using RStan for all chunks | Option 2: Using CmdStanR for all chunks | Example | Option 3: Using both RStan and CmdStanR in the same R Markdown document | Caching chunks | Running interactively

Introduction | Prerequisites | Typical Workflow | Example | Write a Stan Program | Preparing the Data | Sample from the Posterior Distribution | Arguments to the stan Function | Data Preprocessing and Passing | Methods for the "stanfit" Class | Sampling Difficulties | Additional Topics | User-defined Stan Functions | The Log-Posterior (function and gradient) | Optimization in Stan | Model Compilation | Running Multiple Chains in Parallel | Working with CmdStan | See Also

Using Sodium | Random data generator | Hash functions | Secret key encryption | Secret key authentication | Public key encryption | Public key authentication (signatures) | Public key authenticated encryption

References

Posterior draws | extract() | as.matrix(), as.data.frame(), as.array() | Posterior summary statistics and convergence diagnostics | Sampler diagnostics | Model code | Initial values | (P)RNG seed | Warmup and sampling times

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