Package: SSMSE 0.2.8

Kathryn Doering

SSMSE: Management Strategy Evaluation (MSE) using Stock Synthesis (SS)

An R package for performing Management Strategy Evaluation (MSE) using Stock Synthesis (SS). SS is used as the Operating Model (OM) and, if the user desires, the Estimation model (EM). SSMSE allows existing SS models to be used as the basis for an OM. These OMs are used in the MSE framework provided by SSMSE to evaluate the implications of management actions on a population given uncertainty.

Authors:Kathryn Doering [aut, cre], Nathan Vaughan [aut]

SSMSE_0.2.8.tar.gz
SSMSE_0.2.8.zip(r-4.5)SSMSE_0.2.8.zip(r-4.4)SSMSE_0.2.8.zip(r-4.3)
SSMSE_0.2.8.tgz(r-4.5-any)SSMSE_0.2.8.tgz(r-4.4-any)SSMSE_0.2.8.tgz(r-4.3-any)
SSMSE_0.2.8.tar.gz(r-4.5-noble)SSMSE_0.2.8.tar.gz(r-4.4-noble)
SSMSE_0.2.8.tgz(r-4.4-emscripten)SSMSE_0.2.8.tgz(r-4.3-emscripten)
SSMSE.pdf |SSMSE.html✨
SSMSE/json (API)

# Install 'SSMSE' in R:
install.packages('SSMSE', repos = c('https://noaa-fisheries-integrated-toolbox.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/nmfs-fish-tools/ssmse/issues

On CRAN:

Conda:

stock-synthesis

4.38 score 20 stars 20 exports 79 dependencies

Last updated 10 months agofrom:1f12b27f65. Checks:1 OK, 8 ERROR. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 20 2025
R-4.5-winERRORMar 22 2025
R-4.5-macERRORMar 22 2025
R-4.5-linuxERRORMar 22 2025
R-4.4-winERRORMar 22 2025
R-4.4-macERRORMar 22 2025
R-4.4-linuxERRORMar 22 2025
R-4.3-winERRORMar 22 2025
R-4.3-macERRORMar 22 2025

Exports:check_convergencecreate_future_om_listcreate_sample_structcreate_scen_listdevelop_OMsget_avg_catchget_binget_catch_cvget_catch_sdget_rel_SSB_avgget_SSB_avgget_total_catchparse_MSplot_comp_samplingplot_index_samplingrun_EMrun_ss_modelrun_SSMSEset_MSE_seedsSSMSE_summary_all

Dependencies:assertive.baseassertive.propertiesassertive.typesbase64encbbmlebdsmatrixbslibcachemclicodacodetoolscolorspacecorpcorcpp11digestdoParalleldplyrevaluatefansifarverfastmapfontawesomeforcatsforeachfsgenericsggplot2gluegtablegtoolshighrhtmltoolsisobanditeratorsjquerylibjsonlitekableExtraknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellmvtnormnlmenumDerivpillarpkgconfigpurrrr4ssR6rappdirsRColorBrewerrlangrmarkdownrstudioapisassscalesss3simstringistringrsvglitesystemfontstibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunxml2yaml

Readme and manuals

Help Manual

Help pageTopics
Add the deviation changes from the list obj to an existing dfadd_dev_changes
Add new data to an existing EM datasetadd_new_dat
Add in future parameter valuesadd_OM_devs
Add in years of sampling data neededadd_sample_struct
Calculate uncertainty and biases in historic composition datacalc_comp_var
Calculate the parameter trendcalc_par_trend
Change dataset from OM into format for EMchange_dat
Change the years in the forecast filechange_yrs_fcast
check all index years/fleets in EM available in OM. (but not vice versa) a general function that can be usedcheck_avail_dat
Check the catch dataframecheck_catch_df
Flag potential convergence issues in SS3 model runscheck_convergence
Check that the directory for an OM is validcheck_dir
Check structure of forecast is suitable to use in the EMcheck_EM_forecast
Check future catch smaller than the last year's population size.check_future_catch
Check the general structure of a future OM list and standardize valuescheck_future_om_list_str
Check structure of a future OM list against the scen_list and standardize outputcheck_future_om_list_vals
check that an OM data set has at least the same data as an estimation modelcheck_OM_dat
Check sample_struct_listcheck_sample_struct
Check structure of the object scen_listcheck_scen_list
clean the initial model filesclean_init_mod_files
function that creates a combined column to the list_item of interestcombine_cols
Create the devs dataframe for a scenario and iteration from user inputconvert_future_om_list_to_devs_df
Convert user input to r4ss data namesconvert_to_r4ss_names
Copy OM and EM model filescopy_model_files
Helper function to create future om list objectscreate_future_om_list
Create the OMcreate_OM
create the OM directorycreate_out_dirs
Create the sample_struct listcreate_sample_struct
Create scen_list object to use in run_SSMSE function.create_scen_list
Develop different operating modelsdevelop_OMs
Use EM as the management strategy option.EM
Example Performance Metric: Calculate average catch over a range of yearsget_avg_catch
Get SS3 binary/executable location in packageget_bin
Example Performance Metric: Calculate the coefficient of variation of catchget_catch_cv
Example Performance Metric: Calculate Standard Deviation of Catchget_catch_sd
Get dead catch from the timeseries Report.sso tableget_dead_catch
Get the EM catch data frameget_EM_catch_df
Change the OM data to match the format of the original EM dataget_EM_dat
Get the Fishing mortality from the timeseries Report.sso tableget_F
Get the full sample structure from user inputget_full_sample_struct
Put implementation error of 0 into a matrixget_impl_error_matrix
Get the sampling scheme in a data file.get_init_samp_scheme
return a value from a data frameget_input_value
Get the data frame of catch for the next iterations when not using an estimation model.get_no_EM_catch_df
get basic data to calculate performance metricsget_performance_metrics
Example Performance Metric: Calculate the avg relative SSB (SSB/SSB unfished) over a range of years for each iterationget_rel_SSB_avg
Get retained catch from the timeseries Report.sso tableget_retained_catch
Example Performance Metric: calculate the average SSB over a range of years for each iterationget_SSB_avg
Example Performance Metric: Calculate total catch over a range of yearsget_total_catch
Interim assessment management strategyInterim
Last year catch used in the future for management strategylast_yr_catch
Locate the OM model fileslocate_in_dirs
Match parameter name to parameter names in the par filematch_parname
No Catch in the future management strategyno_catch
Parse management strategy optionsparse_MS
Plot comp data, expected values, and sampled data for 1 scenarioplot_comp_sampling
Plot index data, expected values, and sampled data for 1 scenarioplot_index_sampling
Error if object is not an r4ss objectr4ss_obj_err
Remove the historical sampling structurerm_sample_struct_hist
remove vals in 2 list components with the same namerm_vals
Run the estimation modelrun_EM
Initial run of the OMrun_OM
Run an operating or estimation modelrun_ss_model
run an MSE using SS OMsrun_SSMSE
Run one iteration of an MSE using SS OMrun_SSMSE_iter
Run an MSE scenario using SS OMrun_SSMSE_scen
Sample vals from normal random, lognormal random, or modified AR-1 process.sample_vals
Set the initial global, scenario, and iteration seedsset_MSE_seeds
Calculate uncertainty and biases in historic composition dataSim_comp
SSMSE: A package for Management Strategy Evaluation (MSE) using Stock Synthesis (SS)SSMSE-package SSMSE
Get results in a list for 1 scenarioSSMSE_summary_all
Get results in a list for 1 iterationSSMSE_summary_iter
Get results in a list for 1 scenarioSSMSE_summary_scen
Change a model from running with par to running without partest_no_par
Update a sequence of base parameter annual values to account for a time varying block effectsupdate_basevals_blocks
Update a sequence of base parameter annual values to account for a time varying deviation effectsupdate_basevals_dev
Update a sequence of base parameter annual values to account for a time varying environmental effectsupdate_basevals_env
Extend the OM forward using next years' catchupdate_OM