FIMS | Loading the package | Getting help | Memory | Data | data_big | FIMSFrame() | Parameters | setup_default_parameters() | Update parameters | Fit | initialize_fims() | fit_fims() | Example | Logging system | Sensitivities | Initial values | Age-specific selectivity | Age only | Length

Main Example: Estimable VonBertalanffySchnute With Generated Data | Build a Compact Generated Data Set | Configure the VonBertalanffySchnute Model | Fit the Model | Plot the Observed and Predicted Growth Curve | Plot Residuals | One Light Downstream Check of the Shared Size Path | Architecture Path | Next Steps

Other sources of information | Data format | data_big | FIMSFrame() | Catch (type == "catch") | Indices (type == "index") | Age compositions (type == "age_comp") | Length compositions (type == "length_comp") | Length bins (type == "length_bin") | Weight-at-age data (type == "weight_at_age") | Age-to-length conversion (type == "age_to_length_conversion")

Overview | Who Should Use This Guide | Prerequisites | FIMS Code Structure | Directory Structure | Naming Conventions | C++ Naming Conventions | R Naming Conventions | Module-Specific Conventions | Step-by-Step Guide | Related documentation | Documentation expectations | Step 1: Create the C++ implementation | Class roles in the C++ layer | 1.1 Create or update the base class | 1.2 Create or update the implementation class | 1.3 Create or update the umbrella header | Step 2: Build the Rcpp interface | 2.1 Understand the parameter objects first | 2.2 Create or update the interface class | 2.3 Register parameters with TMB in add_to_fims_tmb_internal() | 2.4 Extract results in finalize() | Step 3: Register the module with the R API | 3.1 Update src/fims_modules.hpp | 3.2 Update R/FIMS-package.R | Step 4: Connect the module to the current R workflow | 4.1 Update the configuration and default-parameter path | 4.2 Update initialize_modules.R | 4.3 Keep the full run path in mind | Step 5: Add targeted tests | 5.1 C++ Google tests | 5.2 R testthat tests | Step 6: Finalize the documentation and build checks | 6.1 Module-specific documentation | Files to modify: complete checklist | Required or commonly required files | Validation checklist | Common patterns and best practices | Parameter handling | ID management and lifecycle | TMB integration | Namespace organization | Troubleshooting | Common issues | "undefined symbol" errors when loading the package | Parameters are not being estimated | Values are not appearing back in R after a run | The module initializes in R but is missing expected fields | Getting help | Examples and references | Code examples | Documentation resources | Summary

Modules in R | Rcpp Interface | fims namespace | What is a namespace? | Maturity example | fims_popdy::LogisticMaturity class | Population class | Overview | Thinking in R

What is C++ | Definition | Why use C++ | Writing C++ | Basics | Types | Hello World example | add example | Vectors | Constants | Templates | Syntax in FIMS | Compiling directions | Classes | MyClass example | Instantiate and initialize | Inheritance | Polymorphism | Pointers and References | Pointers | References | Modifying Pointers | Memory Management | Shared pointers

What is Rcpp | Definition | Why use Rcpp | Writing C++ functions | Inline C++ code in R | Calling .cpp files from R | Benchmarking against R | C++ in FIMS | Rcpp Types | Rcpp scalar classes | Rcpp vector classes | SEXP in R | Type conversion | Rcpp methods | Rcpp modules | RCPP_MODULE | RCPP_EXPOSED_CLASS | Modules in FIMS

Introduction | Methods | Time-series model | Catch-at-age model | Parameters | Code | Results

Set up FIMS model | Likelihood profile | Visualize results | Clean up | Retrospective analysis | Mohn's Rho | Visualize the results

Projections in FIMS | Without projections | Projections | Data | Model | Model fit | Model summaries

Overview | Stock Synthesis | Installation | Installing | Installing Stock Synthesis | Installing dependent R packages | Scenarios | File structure | Input file structure | Simulation file structure | Example simulations | Setting up the example | Fishing mortality | Composition data | Survey index of abundance | Estimating parameters | Visualizing the results | Self test to check for bias | Setup self test | Run self-test | Checking output | Beautiful output | .csv files | Post-processing of results | Calculate the relative error (RE) | Merge scalar and time series | Separate the deterministic from the stochastic runs | Boxplots | Stochasticity | Generating observation error | calculate_data_units() | Distributional assumptions for observation error | Indices of abundance | Effective sample sizes | Age and length compositions | Sampling with overdispersion | Structure of data bins | Mean length at age | Conditional age at length | Generating process error | Reproducibility | Detailed features | Time-varying parameters in the OM | Parallel computing | References

Overall file structure | Setting up a new operating model | Forecast file modifications | Starter file modifications | Control file modifications | Data file modifications | Setting up a new estimation model | Testing the new estimation model

Overview | ☁️ Google Cloud Workstations | 🌐 GitHub Codespaces | 🐧 WSL2 | 🧑‍💻 Local | 🙋 Need Help?

Modifying the OM and EM models included with ss3sim