Background | Workflow Overview | How to run extract_sis_data() | Familiarize yourself with the two templates | sis_assmt_template.csv | sis_ts_template.csv | Export at least one relevant figure/table as an .rda file | Provide the path to the key_quantities.csv file | Time series | Assessment data | Provide assessment data before or after running extract_sis_data() to populate additional fields | Provide additional time series data

Overview | Setup and data loading | Quality assurance and quality control | Prepare and format data | Model design and fit | Model diagnostics and validation | Reproducibility

Introduction | Development guidelines | Step 1: Extract Formatted Data | Step 2: Mock the Design Object | Step 3: Write the RTMB Objective Function | Template: Custom Objective Function | Step 4: Integrate and Submit

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

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())

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

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

Extracting parameters manually | Helper functions | Annual summaries

Overview | Manipulating data for estimation | Fitting the model | Getting coefficients | Getting predicted values | Plotting results | Additional examples | Diagnosing lack of convergence

Covariates

Using initial values | Specifying limits

Overview | Model 1: time varying intercept and slope | Model 2: time varying intercept and constant slope | Model 3: constant intercept and time varying slope | Comparing models

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

Fish stomach contents example | Overdispersion or not? | Model selection | Summarizing estimates | Random effects

Construct a MAR model | Fit state-space model | Compare to best fit model | Use a known observation error

Set up the data | Create the restriction matrix | Fit the model | Show output | Model with restrictions | re-run the analysis | run with a different search method | construct a MAR model using windows

Example data | Fit models without covariates | Log likelihoods | Compare parameter estimates | Add example with covariates | Fit model | Compare time and log likelihoods | More MARSS models | Run some time comparisons

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

Include a comparison with covariates | Look at a bigger data set

Example | Parameter estimates | Estimated states | Fitted values | Diagnostics | Predictions and forecasts | Output to LaTeX | Important | Tips and Tricks | Linear constraints | Time-varying parameters | Need more information?

Example data | Fit models without covariates | Log likelihoods | Compare parameter estimates | Add example with covariates | Fit model | Parameter estimates | Plot estimates

Optimization | nlminb | optim | MARSSkem | Likelihood Calculation | Koopman and Durbin Kalman filter and smoother | Classic Kalman filter and smoother | TMB

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

Overview | Simulating data from a VAR model | Fitting a model (and optional arguments) | Default priors | Shrinkage priors

Data | View raw data — Download raw data | Common Metrics

Data | View raw data — Download raw data | Common Metrics

Data | View raw data — Download raw data | Common Metrics

Data | View raw data — Download raw data | Common Metrics

Data | View raw data — Download raw data | Common Metrics

Data | View raw data — Download raw data | Common Metrics

Data | View raw data — Download raw data | Common Metrics

Data | View raw data — Download raw data | Common Metrics

Data | View raw data — Download raw data | Common Metrics

Data | View raw data — Download raw data | Common Metrics

Data | View raw data — Download raw data | Common Metrics

Data | View raw data — Download raw data | Common Metrics

Data | View raw data — Download raw data | Common Metrics

Data | View raw data — Download raw data | Common Metrics

Data | View raw data — Download raw data | Common Metrics

Data | View raw data — Download raw data | Common Metrics

Data | View raw data — Download raw data | Common Metrics

Data | View raw data — Download raw data | Common Metrics

Data | View raw data — Download raw data | Common Metrics

Data | View raw data — Download raw data | Common Metrics

Data | View raw data — Download raw data | Common Metrics

Data | View raw data — Download raw data | Common Metrics

Fish stomach contents example | Does scale of the data impact precision of the posterior estimates?

Dirichlet priors

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

Requirements | Simulating data | Ex 1: Linear food chain | Ex 2: Grazers & plants

Introduction | Model | ER calibration | Reimplementation of VRAP in R | Compared to results from the VRAP package | Faster with Rcpp and C++ | log normal residuals with autocorrelation | Next steps | simFish C++ function