tmp commit: modifications to manual

inst/bookdown/01-intro.Rmd

@@ -2,36 +2,55 @@
 
 ## Purpose
 
-SSMSE was developed to increase the ease of using [Stock Synthesis](https://vlab.noaa.gov/web/stock-synthesis) (SS) directly as an operating model in an Management Strategy evaluation. The approach requires a conditioned Stock Synthesis model, which is treated as the Operating Model and a Stock Synthesis model to use as the Estimation Model (EM) and to specify the Management procedure through the Stock Synthesis forecasting model. 
+SSMSE was developed to increase the ease of using [Stock Synthesis](https://vlab.noaa.gov/web/stock-synthesis) (SS) directly as an operating model in an Management Strategy evaluation. The approach requires a conditioned Stock Synthesis model, which is treated as the Operating Model (OM).
 
 ## Functions in SSMSE {#funs}
 
-The functions users can call in SSMSE are:
+The main functions users can call in SSMSE are:
 
 Function               | Description
 ---------------------- | ------------------
 `run_SSMSE()`            | Run the MSE simulations
-`create_sample_struct()`  | Helper function to create a list for future sampling from a model to use as input in `run_SSMSE()`
-`develop_OMs()`          | Helper function to turn one OM into many
 `SSMSE_summary_all()`    | Summarize MSE output
 
+The helper functions to create inputs to `run_SSMSE` are:
+
+Helper Function           | Description
+------------------------- | ------------------
+`create_sample_struct()`  | Helper function to create a list for future sampling from a model to use as input in `run_SSMSE()`
+`create_future_om_list()` | Helper function that provides examples of the structure for the future_om_list input to `run_SSMSE()`.
+`develop_OMs()`           | Helper function to turn one OM into many
+
+More information about helper functions is available in the [helper functions section](#helper).
+
+Exported functions that can be used for writing custom management strategies are:
+Function    | Description
+-----------  | ------------
+`run_EM()`       |  Run an SS estimation model (uses run_ss_model)
+`run_ss_model()` | Run an SS model
+`get_bin()`      | Get location of the SS binary.
+`parse_MS()`     | Function that runs the management strategy and returns catch by fleet for the projections. A reference function for those setting up custom management strategies. 
+
+Finally, some plotting functions are available:
+Plotting Function | Description
+----------------- | -----------
+`plot_index_sampling()` | Plot to compare the sampled index values to the operating model expected values and original operating model conditioning index data.
+`plot_comp_sampling()`  | Plot to compare the sampled composition values to the operating model expected values and original operating model conditioning composition data.
 
 ## Brief description of the SSMSE MSE simulation procedure
 
 ### Conditioning the OM and sampling from the OM
 
-For each scenario, SSMSE starts with the user providing a fitted Stock Synthesis model (or selecting an model from the SSMSE package) to use as an OM. For each iteration of the scenario, SSMSE turns the SS fitted model into an OM and runs it once with no estimation with Stock Synthesis in order to get the "true" values and a bootstrapped data set from SS.
+For each scenario, SSMSE starts with the user providing a fitted Stock Synthesis model (or selecting an model from the SSMSE package) to use as an OM. For each iteration of the scenario, SSMSE turns the SS fitted model into an OM and runs it once with no estimation with Stock Synthesis in order to get the "true" values and a bootstrapped data set from SS. Note that any modifications to the OM's parameters specified by the users as it is extended forward in time are also applied.
 
 ### First run of the management strategy in the MSE simulation
 
-The bootstrapped dataset is then used in a Management strategy to forecast catch for the next *n* years.
+The bootstrapped dataset is then used in a Management strategy to forecast catch for the next $n$ years, where $n$ is the number of years between assessments.
 
-### Feedback from Managment Strategy into OM: extending model years
+### Feedback from managment strategy into OM: extending model years
 
-The catch for the next *n* years before the next assessment is then added to the OM, as well as any recruitment or time varying parameter deviations. The OM is then projected forward *n* years and run with no estimation where it can be used to produce sampled data for the next *n* years. These new data values are appended to the original dataset.
+The catch for the next $n$ years before the next assessment is then added to the OM, as well as any recruitment or time varying parameter deviations. The OM is again run with no estimation where it can be used to produce sampled data for the next $n$ years. These new data values are appended to the original dataset.
 
 ### Subsequent runs of the management strategy
 
-The appended data set is then used in the managment strategy again. New forecasts are produced, and are fed back to the OM.
-
-
+The appended data set is then used in the managment strategy again, and new catch by fleet is produced that can then be fed back to the OM.