An R package for stock-assessment simulation with Stock Synthesis
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ss3sim is an R package that simplifies the steps needed to generate beautiful simulation output from the widely-used Stock Synthesis (SS3), a statistical age-structured stock assessment framework. To learn more, read on or check out the vignettes.

Table of contents


Below are instructions for installing ss3sim from GitHub, which is the preferred approach. Users can use either {remotes} or {pak} to do this, though the example below is for the former. "main" is the default branch with the latest code, all features in development will be in feature branches.

Install the GitHub version via {pak}:

# install.packages("pak")

The CRAN version of ss3sim will be updated one last time in September prior to being archived, thus it is not recommended to install from CRAN. We suggest using the GitHub version because it comes with the SS3 executable/binary. If you are using the CRAN version, you will need to install the binary and place it in your system path. See the Introduction vignette for more details on how to get the latest version of SS3 and place it in your path.

Once ss3sim is installed, you can read the help files and access the vignettes for reproducible examples of ss3sim simulations with

vignette("introduction", "ss3sim")

Simulation setup

An ss3sim simulation requires three types of input:

  1. a base model of the underlying truth (an SS3 operating model; OM),
  2. a base model of how you will assess that truth (an SS3 estimation model; EM), and
  3. a data frame specifying how you want to manipulate (1) and (2) from their status-quo configurations.

You can find examples of an OM and EM on GitHub or locally on your machine if you have installed ss3sim. To find the location of these files locally, run system.file("extdata", "models", package = "ss3sim"). Users often modify these files to create new life histories or modify their own files from a production stock assessment to work within ss3sim. See the vignettes on modifying models and making models for more information.

An example data frame for (3) is also available within the package via ss3sim::setup_scenarios_defaults(). This example is sufficient to run a single scenario using the OM and EM supplied in the package. Many more options (i.e., columns) are possible and users should take note that this example provided in the package represents a minimum viable setup. Users can either create their own data frame in R or augment this existing data frame to run a set of custom scenarios. Specifically, adding columns will enable the manipulation of additional components of the OM, sampling procedure, and the EM. Adding will lead to more scenarios, where a scenario is the result of the combination of specifications in that row, i.e., how you manipulate the OM and the EM.

ss3sim stores each scenario in its own directory. Inside the scenario directory will be one directory per iteration. Iterations within a scenario differ only by the seed used within R to define the randomness of that iteration. See the figure below for an example directory structure from a simulation with two scenarios and 3 iterations.

 scenario 1
 scenario 2

How ss3sim works

ss3sim works by converting simulation arguments (e.g., a given natural mortality trajectory) into manipulations of SS3 configuration files. It takes care of running the operating and estimation models as well as making these manipulations at the appropriate stage in the simulation.

ss3sim functions are divided into three types:

  1. change and sample functions that manipulate SS3 configuration files. These manipulations generate an underlying "truth" (OMs) and control our assessment of those models (EMs).

  2. run functions that conduct simulations. These functions generate a folder structure, call manipulation functions, run SS3 as needed, and save the output.

  3. get functions for synthesizing the output.

Example output from a simulation

data("scalar_dat", package = "ss3sim")
p <- scalar_dat %>%
    M = ifelse(NatM_p_1_Fem_GP_1 == 0.2, "M = 0.2", "M = Estimated")
  ) %>%
  dplyr::filter(model_run == "em") %>%
  ggplot2::ggplot(ggplot2::aes(x = LnQ_base_Survey_2, y = depletion)) +
  ggplot2::geom_point() +
  ggplot2::facet_grid("M") +
  ggplot2::xlab("Survey scalar (q)")

Use the previous code chunk to visualize the results of a simulation that investigated scenarios that fixed natural mortality (M) at its true value from the OM (0.2) or estimated M. Upper panel shows how the estimates depletion change as the estimate of q changes for when M is fixed at the truth and the lower panel shows the same relationship when M is estimated.

Citing ss3sim

If you use ss3sim in a publication, please cite it as shown by


and add your publication to the list of publications in the wiki.

Contributing to ss3sim

Interested in contributing to ss3sim? We recognize contributions come in many forms, including but not limited to code, reporting issues, creating examples and/or documentation.

We strive to follow the NMFS Fisheries Toolbox Contribution Guide. We also have included ss3sim-specific code contribution information in the Developers page of the ss3sim wiki. Note that these are guidelines, not rules, and we are open to collaborations in other ways that may work better for you. Please feel free to reach out to us by opening an issue in this repository or by emailing the maintainer (call maintainer("ss3sim") in R to view the current maintainer's name and email address).

Note that contributors are expected to uphold the code of conduct.

Code of conduct

This project and everyone participating in it is governed by the NMFS Fisheries Toolbox Code of Conduct. By participating, you are expected to uphold this code. Please report unacceptable behavior to [email protected]. Note that the maintainers of ss3sim do not have access to this email account, so unacceptable behavior of maintainers can also be reported here.

The NFMS Fisheries Toolbox Code of Conduct is adapted from the Contributor Covenant, version 1.4, available at

For answers to common questions about this code of conduct, see


"The United States Department of Commerce (DOC) GitHub project code is provided on an 'as is' basis and the user assumes responsibility for its use. DOC has relinquished control of the information and no longer has responsibility to protect the integrity, confidentiality, or availability of the information. Any claims against the Department of Commerce stemming from the use of its GitHub project will be governed by all applicable Federal law. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by the Department of Commerce. The Department of Commerce seal and logo, or the seal and logo of a DOC bureau, shall not be used in any manner to imply endorsement of any commercial product or activity by DOC or the United States Government."

NOAA Fisheries

U.S. Department of Commerce | National Oceanographic and Atmospheric Administration | NOAA Fisheries

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