master:
development:
ss3sim is an R package that facilitates flexible, rapid, andreproducible fisheries stock assessment simulation testing with thewidely-usedStockSynthesis (SS) statistical age-structured stock assessmentframework.
Install theCRANversion of ss3sim with:
install.packages("ss3sim")Or, install the development version from GitHub:
# install.packages("devtools")devtools::install_github("ss3sim/ss3sim",ref ="development",build_vignettes =TRUE,dependencies =TRUE)library(ss3sim)We suggest using the GitHub version because it comes with the SSexecutable/binary. If you are using the CRAN version, you’ll need toinstall the binary and place it in your system path. See theIntroduction vignette withvignette("introduction", "ss3sim") for more details on howto get the latest version of SS and place it in your path.
You can read the help files and access the vignettes for reproducibleexamples of ss3sim simulations with
?ss3simbrowseVignettes("ss3sim")An ss3sim simulation requires three types of input:
You can find examples of these SS operating and estimation modelswithinthe package data. Plain-text case files for some current simulationprojects run by the developers of the package arealsoavailable along with thecasefiles for the examples used in the paper and vignette.
An illustration of the input and output file and folderstructure.
ss3sim works by converting simulation arguments (e.g., a givennatural mortality trajectory) into manipulations of SS configurationfiles. It takes care of running the operating and estimation models aswell as making these manipulations at the appropriate stage in thesimulation.
ss3sim functions are divided into three types:
change andsample functions thatmanipulate SS configuration files. These manipulations generate anunderlying “truth” (operating models) and control our assessment ofthose models (estimation models).
run functions that conduct simulations. Thesefunctions generate a folder structure, call manipulation functions, runSS3 as needed, and save the output.
get functions for synthesizing the output.
Example output from an ss3sim simulation. This example shows acrossed simulation in which we considered (1) the effect of fixingnatural mortality (M) at its true value (0.2; case E0) orestimatingM (case E1) and (2) the effect of high survey effort(sigma_survey = 0.1; case D0) or low survey effort (sigma_survey = 0.4;case D1). Upper panels (blue) show time series of relative error inspawning stock biomass (SSB). Lower panels (grey) show the distributionof relative error across four scalar variables: depletion,M,SSB at maximum sustainable yield (SSB_MSY), and fishing mortality(F) in the terminal year. We show the values across simulationiterations with dots and the distributions with beanplots (kerneldensity smoothers).
If you use ss3sim in a publication, please cite ss3sim as shownby
citation("ss3sim")toBibtex(citation("ss3sim"))“The United States Department of Commerce (DOC) GitHub project codeis provided on an ‘as is’ basis and the user assumes responsibility forits use. DOC has relinquished control of the information and no longerhas responsibility to protect the integrity, confidentiality, oravailability of the information. Any claims against the Department ofCommerce stemming from the use of its GitHub project will be governed byall applicable Federal law. Any reference to specific commercialproducts, processes, or services by service mark, trademark,manufacturer, or otherwise, does not constitute or imply theirendorsement, recommendation or favoring by the Department of Commerce.The Department of Commerce seal and logo, or the seal and logo of a DOCbureau, shall not be used in any manner to imply endorsement of anycommercial product or activity by DOC or the United StatesGovernment.”