1. Model dimensions and operating mode | msmMode — single-species vs. multi-species | initMode — initial age structure | estimateMode — what does fit_mod() actually do? | Random-effects toggles | 2. Predation: suitMode (per predator) | 3. Selectivity (per fleet) | 4. Catchability (per fleet) | 5. Composition likelihoods | 6. Recruitment / stock-recruit (recFun = build_srr()) | 7. Natural mortality (M1Fun = build_M1()) | M1_model — fixed-effects structure of M1 | M1_re — random-effects structure on M1 | 8. Growth and weight-at-age (growthFun = build_growth()) | growth_model — fixed-effects structure of growth | growth_re — random effects on growth | 9. Harvest control rules (HCR = build_hcr()) | 10. Projection and MSE | 11. Diagnostics and inference | 12. Plotting | References

Overview | 1. Installation | 2. Data | 3. Fit models | 4. Compare models | 5. Model plots and diagnostics

Fitting OMs | Fitting EMs (Tier 3 HCR) | Running MSEs | Reproducibility | Evaluating MSEs via plots | Summary statistics | Alternative scenarios | Different BRPs | Triannual BTS | 4-year cycle and 50% sampling | Climate MSE | Climate linked OM | Run MSE | Overall summary

Setup and plotting data | S3 Methods | Convergence diagnostics | Fit plots | Composition data | Survey indices | Catch | One-step-ahead (OSA) residuals | Process residuals | Retrospective analysis | Jitter testing | Self-test (simulation–estimation) | Likelihood profile | Comparing single- and multi-species trajectories | Model average

Setup | Fixed-F projections | Recruitment in the projection period | Manual recruitment deviations | Stochastic recruitment via sample_rec() | Comparing projection scenarios | Random recruitment effects | Available harvest control rules

When to use each mode | Bering Sea example | Gulf of Alaska example | Predator-prey suitability (suitMode) | Comparing outputs

Required vs. optional fields | Setup | 1. Species / model controls | 2. Fleet control table | 3. Survey index data | 4. Catch data | 5. Composition data (optional) | 6. Conditional age-at-length (CAAL) data (optional) | 7. Biological inputs | Age-to-length transition matrix | Ageing error matrix | Weight-at-age | Maturity-at-age | Sex ratio at age | Fixed selectivity (optional) | Fixed numbers-at-age (optional) | Natural mortality (M1 base) | Environmental covariate data (optional) | 8. Multi-species bioenergetics parameters (optional in single-species mode) | 9. Validate and fit | 10. Export back to Excel (optional)

Model controls | Fleets | Catch data | Survey indices | Composition data | Ageing error | Weight-at-age | Other inputs | Verifying the conversion against SS | See also

In progress! | Population dynamics | Predation sub-model | Suitability | MSVPA Type 2 based suitability | Predation mortality | Single-species mode | MSVPA Type 2 based predation mortality | MSVPA Type 3 based predation mortality | Consumption | Bioenergetics based ration | Input ration-at-age | Fishery and survey observation model | Selectivity parameterizations | Fixed selectivity | Non-parametric (Type 1) | Logistic | Time-varying logistic | Double logistic | Time-varying double logistic 1 (ascending and descending time-varying parameters) | Time-varying double logistic 2 (random walk ascending-time varying parameters) | Descending logistic | Time-varying descending logistic | Non-parametric (Type 2) | Time-varying non-parametric (Type 2 for hake) | Catchability parameterizations | Linear time-invariant catchability formulations | Time-varying catchability formulations

Overview | A first example: temperature on K | The linkage_spec() API | Priors | Putting a prior on the parameter itself | Growth SD endpoints | Species- and (species, sex)-specific priors | Per-species formulas | Per-sex formulas | Polynomial and basis-expansion formulas | How the pieces fit together | Natural mortality | Recruitment | A multi-process example | Inspecting and debugging | Behind the scenes: automatic base-parameter handling | init and the base parameter | Stratification | Single-sex models and by = ~ ... + sex | On the roadmap | See also

Overview | Load data and inspect the growth dataset | Build a growth model with linkages | What this linkage does | Fit the model | Inspect the linkage table and prior contributions | Compare with an unlinked baseline model and Richards model | Notes on priors through linkages

Captions and alternative text (alt text) | How are captions and alt text constructed? | How can I alter the captions and/or alt text? | If I recreate a figure or table, will its caption and/or alt text be updated/overwritten? | How can I change or update captions and alt text? | Individual figures/tables (e.g., plot_biomass()) | All available figures and tables (e.g., save_all_plots())

Installing the r4ss R package | Basic installation | Loading the package and reading help pages | Alternative versions | Reading model output and making default plots | Creating select plots | Scripting Stock Synthesis workflows with r4ss | Read in Stock Synthesis files | Investigate the model | Modify the model | Write out the modified models | Download the Stock Synthesis executable from GitHub | Run the modified model | Investigate the model run | Should I script my whole Stock Synthesis workflow? | Functions for common stock assessment tasks | Running retrospectives | Jittering | Tuning composition data

Overview | Installing mizer | Setting the model parameters | Running a simulation | Exploring the results | Size-spectrum models | Single-species model | Community model | Trait-based model | Multispecies model | Which model to use | References

Introduction | R package | Input files | Data file | Separate Data Sets | Combined Data Set | Other Considerations | Specifications file | Bias options | Sigma options | Example Specifications file | Examples | Blue Grenadier | Bight Redfish | School Whiting | Sablefish | Trouble Shooting | Acknowledgements | References

Helper functions in AgeingError | Writing the data file | Writing the specifications file | Convenience wrapper: write_files() | Running the model

Load data and packages | Setup model | Setup Recruitment Dynamics | Setup biological dynamics | Setup Movement and Tagging | Setup Catch and Fishing Mortality | Setup Fishery Indices and Compositions | Setup Survey Indices and Compositions | Setup Fishery Selectivity and Catchability | Setup Survey Selectivity and Catchability | Setup Model Weighting | Fit model | Fit model (Without Francis) | Fit model (With Francis) | Check Model Results | Convergence | Time Series Results | Catch and Index Fits | Composition Fits | Fishery Ages | Fishery Lengths | Survey Ages | Retrospectives | Retrospectives (Without Francis) | Retrospectives (With Francis) | Likelihood Profiles | Jitter Analysis | MCMC | Reference Points and Projections | Reference Points | Projections | Spawning Biomass Projections | Catch Projections | Catch Advice under $F_{40%}$ | Fishing Mortality Projections

Unstructured Markov | Age Blocks | Year Blocks | Sex Blocks | Blocks Across all Dimensions | Continuous-Time Markov Chain (CTMC) | Constant Movement | Age-Varying | Linear | Spline | Movement Across all Dimensions | Process Error

Background | Set up and format | Naming Conventions | Final Formatting | Helpful Functions to Manipulate Data

Overview | Creating plots | Example: Creating plots from simple and adding a custom tab | read model output | make the default plots and associated HTML files | Make custom plots and write CSV file with info about them | Add custom plots to the .csv | Getting the plots onto github

Summary | Sampling for ADMB models | Setting up the model | Sampling with sample_nuts and sample_rwm | mceval phase and posterior outputs | Bounds & Priors | Initializing chains | Parallel sampling | Diagnostics and plotting results | Diagnosing MCMC chains | Plotting output | Mass matrix adaptation | Sampling for TMB models | The no-U-turn sampler implementation | Brief review of Hamiltonian Monte Carlo | Algorithm implementation details | User intervention | Algorithm validity | References

Templates | Calculating and exporting captions and alternative text | Connecting the tables & figures with the captions & alternative text | Expected outcomes | Single plot functions | Multi-plot functions

Data Inputs for Defining Model Dimensions | Data Inputs for Defining Recruitment Processes | Data Inputs for Defining Biological Processes | Data Inputs for Defining Movement Processes | Data Inputs for Defining Tagging Processes | Data Inputs for Defining Catch and Fishing Mortality Processes | Data Inputs for Defining Fishery Indices and Compositions | Data Inputs for Defining Survey Indices and Compositions | Data Inputs for Defining Fishery Selectivity and Catchability | Data Inputs for Defining Survey Selectivity and Catchability | Data Inputs for Defining Model Weighting

Fishery Removals | Fishery Selectivity | Fishery Catchability | Fishery Composition Likelihoods | Survey Selectivity | Survey Catchability | Survey Composition Likelihoods | Natural Mortality | Recruitment | Movement | Tagging

Process Equations | Population Initialization | Recruitment Processes | Population Projection | Movement Processes | Observation Equations | Fishery Observation Model | Survey Observation Model | Tagging Observation Model | Fishery and Survey Selectivity | Likelihoods | Observation Likelihoods | Fishery Catches | Fishery and Survey Indices | Fishery and Survey Compositions | Structuring Compositions and Ageing Error | Tagging | Parameter Priors and Process Error Penalties | Parameter Priors | Natural Mortality | Fishery and Survey Catchability | Steepness | Recruitment Proportions | Movement | Tag Reporting Rates | Process Error Penalties | Initial Age Deviations | Recruitment Deviations | Fishing Mortality Deviations | Joint Likelihood | References

Biological Processes | Fishery Processes | Survey Processes | Tagging Processes | Likelihoods | Parameter Deviations | Derived Quantities (also reported in ADREPORT)

Setup Model Dimensions | Setup Recruitment Dynamics | Setup Biological Dynamics | Setup Movement and Tagging Dynamics | Setup Catch and Fishing Mortality | Setup Index and Composition Data (Fishery and Survey) | Setup Selectivity and Catchability (Fishery and Survey) | Setup Model Weighting | Fit Model and Plot

Define Operating Model | Define Management Options | Define Estimation Model | Define and Run Closed-Loop Simulation

Reference Points | Single Region | Spawning Potential Ratio | Maximum Sustainable Yield (Beverton-Holt) | Multi-Region | Independent | Global | Local | Deriving Catch Advice and Projections | Code Demonstration | Getting Reference Points | Multi Region | Conducting Catch Projections to Derive Catch Advice (Deterministic Recruitment) | Conducting Stochastic Population Projections | References

Starting Values | Mapping | Fixing Parameters | Sharing Parameters

Steepness | Natural Mortality | Catchability | Selectivity | Movement | Tag Reporting Rates | Recruitment Proportions

Overview | Estimation Model | Projection Options | Functions to Determine Catch | Management Procedures | Simulation Loop (Single Replicate) | Simulation Loop (Parrallelized) | Condition Closed Loop Simulation | Run Simulations | Process Results | Visualize

Load data | Simple model with constant bycatch, no covariates | Make table of expanded bycatch estimates | Make table of total bycatch estimates | Derived quantities | Simulating future values

Load data | Simple model with constant bycatch, no covariates

Load data | Simple model with constant bycatch, no covariates | Make plots | Extracting model selection information (LOOIC) | Negative binomial example | Zero - inflated example | Continuous models | Example with covariates | Fit model with time-varying effects | Fit model with no bycatch events

Overview | Simple two-stream example | Fitting a multi-stream model | Plotting results | Stream-specific summaries | Three-stream example | Comparing to pooled data | Different distributions | Adding covariates | Time-varying effects

Overview | Other families | Fixed effect covariates | Time varying parameters | Expanding estimates

Setup Model Dimensions | Setup Recruitment Dynamics | Setup Biological Dynamics | Setup Movement and Tagging | Setup Catch and Fishing Mortality | Setup Fishery Indices and Compositions | Setup Survey Indices and Compositions | Setting up Fishery Selectivity and Catchability | Setting up Survey Selectivity and Catchability | Setup Model Weighting | Fit Model and Plot

Simulation Settings | Population, Recruitment, and Biological Processes | Fishery Processes | Survey Processes | Tagging

Introduction | Packages | Load Data | QAQC | Create Centroids | Alternative Choice | Expected Catch | Model Design | Run Models

Simulation Cross-Testing in SPoRC | Define Model Dimensions | Fishing Processes | Survey Processes | Biological Dynamics | Tagging and Movement | Recruitment | Run the Operating Model | Define Estimation Model | Run Cross-Test Analysis | Self Testing

Setup Model Dimensions | Setup Recruitment Dynamics | Setup Biological Dynamics | Setup Movement and Tagging Dynamics | Setup Catch and Fishing Mortality | Setup Index and Composition Data (Fishery and Survey) | Setup Selectivity and Catchability (Fishery and Survey) | Setup Model Weighting | Fit Model and Plot

Model parameters | Differences between Rpath and EwE balances

Setting up a food web model | Parameter file generation | Model parameters | Stanza Parameters | Diet Parameters | Pedigree parameters

Setting up pkgdown | Setting up code coverage

Introduction | Development guidelines | Create a likelihood function | Input arguments | Function body | Integrating your function with FishSET | Review code and test function | Create a pull request

Running Rpath

Ecosense | Setting up Ecosense | Generating an ensemble of Ecosim parameter sets | Run the kept systems with a perturbation

Installation | References

Unit Tests | Details | Regenerating Baseline Files | List of Tests | AB and RK4 Sim Run Tests: | AB and RK4 Sim Run Tests with Jitter on ForcedBiomass: | AB and RK4 Sim Run Tests with Jitter on ForcedMigrate: | AB and RK4 Sim Run Tests with Stepped Noise on ForcedBiomass: | AB and RK4 Sim Run Tests with Stepped Noise on ForcedMigrate: | AB and RK4 Sim Run Tests with Jitter on ForcedEffort: | AB and RK4 Sim Run Tests with Jitter on ForcedFRate: | AB and RK4 Sim Run Tests with Jitter on ForcedCatch: | AB and RK4 Sim Run Tests with Stepped Noise on ForcedEffort: | AB and RK4 Sim Run Tests with Stepped Noise on ForcedFRate: | AB and RK4 Sim Run Tests with Stepped Noise on ForcedCatch: | Figures | Example plots:

Background | Install packages | Load libraries | Create a function that shows perceptions of palettes | Test main palettes | Test alternative regional color palette | Create alternative palettes | Show alternative palettes | Show alternative palettes in figures

Theming presentations | Color theming | Font theming | Background images | Extra css

Dependency graph

Installation | Configuration | Create a token | Back up repositories | Upload the zip file to a Google Drive folder | Schedule the run

Overview | Installation | Using the dir_size function | Output

Load packages | Create a spatial grid | Add zone ID variable to the spatial object | Plot | Save spatial grid | Reassign zone ID in primary data

Introduction | Packages | Project Setup | Data Import | Fleet assignment | Bin Gears | Operating Profit | Summary Table | QAQC | NA Check | NaN Check | Unique Rows | Empty Variables | Lon/Lat Format | Spatial QAQC | Data Creation | Landing Year | Scale LANDED_OBSCURED to thousands of pounds | Catch per Unit Effort (CPUE) | CPUE Percent Rank | Value Per Unit Effort | VPUE Percent Rank | Fleet Tabulation | Zone Assignment | Closure Area Assignment | Zone Summary | Frequency | Catch | Trip Length | CPUE | VPUE | Other Spatial Units | Outliers | POUNDS | Temporal Plots | Scatter Plots | Correlation Matrix | Active Vessels | Distributions | LANDED_OBSCURED

Introduction | FishSET GUI | Opening the FishSET GUI | Using the FishSET GUI | Upload Data | Data Quality Evaluation | Explore the Data | Data Exploration | Simple Analyses | Map Viewer | Compute New Variables | Fleet Assignment and Summary | Fleet assignment | Fleet Summary | Define Alternative Fishing Choices | Expected Catch/Revenue | Models | Policy | Zone Closure | Run Policy | Bookmark Choices | Generating reports

Reproducibility

Comparison with phylolm | Generalized linear models | Poisson-distributed response | Binomial regression | Summary of PGLM results | Compare with phylopath | Comparison with sem | Comparison with Rphylopars

Estimability for multiple covariance transformations | Scaling of runtime with increased tree size

Model comparison: some important differences | Model fitting: sometimes different

Preparing the Then et al. mortality database | Fitting and selecting among phylogenetic structural equation models | Visualizing output | Sensitivity to using taxonomic tree

Introduction to the DFA model | DFA model with no extreme events | DFA model with extreme events | Fitting DFA models with non-Gaussian families | Alternative loadings for DFA models | Including autoregressive (AR) or moving-average (MA) components on trends | Applying Hidden Markov Models to identify latent regimes | DFA model with weights

Documentation | Tutorials | For Statisticians | CITATION | PUBLICATIONS | NOAA Disclaimer

tldr; | The MARSS model | Model specification | Data and fitting | Data | Fit call | Different fitting methods | Defaults for model list | form="marxss" | form="dfa" | Showing the model fits and getting the parameters | Tips and Troubleshooting | Tips | Troubleshooting | More information and tutorials | Shortcuts and all allowed model structures | Z | B | U and x0 | A | Q, R and V0 | D and C | d and c | G and H | Covariates, Linear constraints and time-varying parameters | Covariates | Linear constraints | Time-varying parameters

Example

Case 1: unequal trend variability | Candidate models | Recovering loadings | Recovering trends | Summary

Data simulation | Sampling argument | Posterior optimization | Posterior approximation

Data simulation | Estimating trends as B-splines | Estimating trends as P-splines | Estimating trends as Gaussian processes | Comparing approaches

Notation review for DFA models | Observation covariates | Process covariates | Examples -- observation covariates | Examples -- process covariates

2 - trend model | 3 - trend model

simulate_population_harvest IBM to simulate a fish population and catches. | LH_sample Bootstrap sampling routine to estimate life history parameters | LH_plot Produce plots from an LH_sample object

Common metrics for ESUs | Dynamic linear modeling for time-varying trend estimation | Multivariate DLMs for analysis of multiple time series from one ESU | Treatment of NAs and 0s | Model selection | Code to fit a multivariate DLM | Wild spawner and fraction wild estimates | Summary statistics

Instructions to run a demo | Instructions to run your own data | Modifying the tables

Notes | Example data file

Overview | Import SDM predictions | Overlay predictions | Calculate evaluation metrics | Create and evaluate ensemble predictions | References