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4 Options for run_SSMSE

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Many inputs are possible for the run_SSMSE() option. Here, we will describe some of the options available. For detailed documentation, type ?SSMSE::run_SSMSE() into the R console.

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4.1 Scenarios in SSMSE

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Note that multiple scenarios can be called in run_SSMSE(), often through vector inputs to run_SSMSE(). Below, we will describe inputs needed to run 1 scenario.

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4.2 Operating model

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The operating model (OM) for SSMSE should be a Stock Synthesis model. This could be any fitted Stock Synthesis model. There is one built-in OM that comes with SSMSE, which is a cod-like model. To use the cod model in run_SSMSE, set OM_name_vec = "cod". Otherwise, the path to the OM model should be specified in OM_in_dir_vec.

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4.3 The Management Strategy/procedure and (if applicable) Estimation model (EM)

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The management strategy (and EM) can be specified in one of two ways:

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  1. Using an SS model
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In theory, any management strategy should work, as long as it can take the data file produced by the OM as output and provide back to SSMSE future catches by fleet.

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4.3.1 Specify the Management Strategy in a SS model

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An SS model can be set up as the EM for the MSE. To use this option, specify "EM" as part of MS_vec. As with the OM, the built-in cod model could be used; just specify "cod" in the EM_name_vec. To use any other SS model as the EM, specify the path in EM_in_dir_vec.

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Future catches will be determined from the forecasting file settings of the SS model. SSMSE will change the number of forecast years to match the number of years between assessments, but other specifications need to be made by the user.

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4.3.2 Using a custom management strategy/procedure

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Users can outline a custom managment strategy as an R function to use. As long as the correct inputs and outputs are used, any estimation method and management procedure can be used. For example, here is a simple function that just sets future catches as half the sampled catches in a specified year:

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Let’s assume this function is saved in a file within the working directory named constant_catch_MS.R.

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This function can then be used in a call to run_SSMSE():

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4.4 Sampling options

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Currently, the only available sampling option is to use the bootstrapping module within SS itself. This means specifying use_SS_boot_vec = TRUE. Details on how sampling is done using the bootstrapping module in SS is available in the “Bootstrap Data Files” section of the SS user manual.

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Users also need to specify how and which data types should be sampled for each future year in the simulation in sample_struct_list. sample_struct_list is a list of lists. The first level is a list for each scenario; then, for the scenario, there is a list of dataframes, each of which specifying which years and fleets of data should be sampled in the future as well as which standard errors or sample sizes should be used.

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The helper function create_sample_struct() can be used to help users generate the list of dataframes for a scenario. See an example of this function’s use in the simple example or by typing ?SSMSE::create_sample_struct() into the R console.

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Users can specify the sampling during the historical period of the model through the sample_struct_hist input to run_SSMSE. This is an optional input and has the same structure as sample_struct_list, but the years will be before the original end year of the OM.

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4.5 Future changes to the operating model (#future)

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By default, SSMSE will simply extend the structure of the OM into the future using the same parameter values as in the last year of the model. However, the user may want to allow changes in parameter values to occur as the OM is extended forward in time. Users can input values into the future_om_list to accomplish this. This input is a list of lists. For the first level of lists, each represents a separate change to make. Within the first level, there are 4 list components that outline the details of the change to make. There are 2 main choices: 1) to specify model changes by telling SSMSE how values should be sampled or 2) to input your own custom values for future values of a given parameter.

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4.5.1 The strucutre of future_om_list

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For example, here is an example list containing just one future change for the model. It shows that the model should be changed to 4.5 in year 103 and afterwards:

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-[1] "SizeSel_P_3_Fishery(1)"
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-  first_yr_averaging last_yr_averaging last_yr_orig_val first_yr_final_val ts_param
-1                 NA                NA              102                103     mean
-    method value
-1 absolute   4.5
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[1] 1
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[1] 4
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Note there is just one change specified here. For the change, there are four list items that are required for any specified change.

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  1. The first list item is named “pars”. It contains a vector of parameter name(s) to apply the change to. The names should be the same as the names in r4ss::SS_read_pars() or can include the following:
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In this case, we just want to apply the change to the SizeSel_P_3_Fishery(1) parameter of the model.

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  1. The second item is “scen”, which contains a vector of information about how to apply the changes within and across scenarios. The first value is an option to specify how the change should be applied among scenarios, either “randomize” (use a different value for each iteration of each scenario) or “replicate” (use the same set of values across scenarios for the same number iteration, but each value will be different across iterations within the same scenario). Note that the same values will be applied across iterations and scenarios if there isn’t any stochasticity in the values being drawn (e.g., standard deviation set at 0), regardless if “randomize” or “replicate” is chosen. In the example above, there is no stochasticity, so specifying randomize or replicate does not matter. Following the first value are the names of the scenarios to apply the change to. If the change should be applied to all of the scenarios, “all” can be used in place of naming every scenario. In this example, The change will only be applied to the scenario named “scen2”, so the input for “scen” is c("randomize", "scen2").

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  3. The “pattern” is a vector of character inputs. The first value should be either “model_change”, meaning that SSMSE will calculate the change values, or “custom” which allows the user to directly put in the values that they want in the model. In this case, “model_change” is used. A second input can also be added when the “model_change” pattern is used, which specifies the distribution to pull from. This could be “normal” or “lognormal”, but if no input is provided, a normal distribution will be used for sampling.

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  5. The fourth item, named “input”, is a dataframe, which contains different column name values depending on if the “model_change” pattern is used or if the “custom” pattern is used. For the model_change options, a dataframe in input specifies a change for the parameter of a distribution. Because we are using the model change option, we have the following columns:

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  • first_yr_averaging: The first year to average historical values from the model, if using for the change. This should be NA if historical averaging will not be used.
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  • last_yr_averaging: The last year to average historical values from the model, if using for the change. This should be NA if historical averaging will not be used.
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  • last_yr_orig_val: The last year of the future deviations with the original model value. This value will not be changed, but the following year’s value will be.
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  • first_yr_final_val: The first year where the final value as specified will be reached. If no future changes are made, the final value will continue forward in the OM through all projected years. For step changes, the first_yr_final_val is just 1 year after the last_yr_orig_val. However, if a more gradual adjustment toward the final value is desired, this could be any number of years after the original value.
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  • ts_param: The sampling parameter to modify. Options are “mean”, “ar_1_phi” (if using autocorrelation), “sd”, and “cv”. If not included in the dataframe, “mean” is assumed to be the same as in the previous model year, sd is assumed to be 0, and we assume no autocorrelation (as specified through an autoregressive AR1 process). Phi should be between -1 and 1 (this creates a stationary process) and if phi = 1, this creates a random walk (as random walk is a special case of an AR1 model). Note that both sd and cv cannot be specified within the same input dataframe.
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4.5.2 Example of future_om_list denoting a gradual change

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Suppose we wanted to change the value of “SizeSel_P_3_Fishery(1)” in scen2 over 4 years after year 102 to arrive at a value of 4.5 in year 106. We can do this by using my_future_om_list, but changing the value in the first row of first_yr_final_val to 106:

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-1                 NA                NA              102                106     mean
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-1 absolute   4.5
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4.5.3 Example of future_om_list with a random deviations

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Suppose we now wanted the value of “SizeSel_P_3_Fishery(1)” to change randomly according to a normal distribution with a standard deviation of 0.1 around a mean of 4.5 from 104 onwards. This can be done by adding a line specifying a change in standard deviation (which for now, has been assumed to be 0) to the data frame:

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-1                 NA                NA              102                106     mean
-2                 NA                NA              103                104       sd
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-1 absolute   4.5
-2 absolute   0.1
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Note that the last_yr_orig_val and first_yr_final_val are different than the line for the mean, which is allowed.

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4.5.4 Example of using historical values for determining parameter values

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This example applies random annual deviations to all parameters for scenarios scen2 and scen3.

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4.5.5 Example using “custom” pattern instead of “model_change”

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-1  impl_error scen1    1 101  1.05
-2  impl_error scen1    2 101  1.05
-3  impl_error scen1    3 101  1.05
-4  impl_error scen1    4 101  1.05
-5  impl_error scen1    5 101  1.05
-6  impl_error scen1    1 102  1.05
-7  impl_error scen1    2 102  1.05
-8  impl_error scen1    3 102  1.05
-9  impl_error scen1    4 102  1.05
-10 impl_error scen1    5 102  1.05
-11 impl_error scen1    1 103  1.05
-12 impl_error scen1    2 103  1.05
-13 impl_error scen1    3 103  1.05
-14 impl_error scen1    4 103  1.05
-15 impl_error scen1    5 103  1.05
-16 impl_error scen1    1 104  1.05
-17 impl_error scen1    2 104  1.05
-18 impl_error scen1    3 104  1.05
-19 impl_error scen1    4 104  1.05
-20 impl_error scen1    5 104  1.05
-21 impl_error scen1    1 105  1.05
-22 impl_error scen1    2 105  1.05
-23 impl_error scen1    3 105  1.05
-24 impl_error scen1    4 105  1.05
-25 impl_error scen1    5 105  1.05
-26 impl_error scen1    1 106  1.05
-27 impl_error scen1    2 106  1.05
-28 impl_error scen1    3 106  1.05
-29 impl_error scen1    4 106  1.05
-30 impl_error scen1    5 106  1.05
-31 impl_error scen2    1 101  1.10
-32 impl_error scen2    2 101  1.10
-33 impl_error scen2    3 101  1.10
-34 impl_error scen2    4 101  1.10
-35 impl_error scen2    5 101  1.10
-36 impl_error scen2    1 102  1.10
-37 impl_error scen2    2 102  1.10
-38 impl_error scen2    3 102  1.10
-39 impl_error scen2    4 102  1.10
-40 impl_error scen2    5 102  1.10
-41 impl_error scen2    1 103  1.10
-42 impl_error scen2    2 103  1.10
-43 impl_error scen2    3 103  1.10
-44 impl_error scen2    4 103  1.10
-45 impl_error scen2    5 103  1.10
-46 impl_error scen2    1 104  1.10
-47 impl_error scen2    2 104  1.10
-48 impl_error scen2    3 104  1.10
-49 impl_error scen2    4 104  1.10
-50 impl_error scen2    5 104  1.10
-51 impl_error scen2    1 105  1.10
-52 impl_error scen2    2 105  1.10
-53 impl_error scen2    3 105  1.10
-54 impl_error scen2    4 105  1.10
-55 impl_error scen2    5 105  1.10
-56 impl_error scen2    1 106  1.10
-57 impl_error scen2    2 106  1.10
-58 impl_error scen2    3 106  1.10
-59 impl_error scen2    4 106  1.10
-60 impl_error scen2    5 106  1.10
-61 impl_error scen3    1 101  1.10
-62 impl_error scen3    2 101  1.10
-63 impl_error scen3    3 101  1.10
-64 impl_error scen3    4 101  1.10
-65 impl_error scen3    5 101  1.10
-66 impl_error scen3    1 102  1.10
-67 impl_error scen3    2 102  1.10
-68 impl_error scen3    3 102  1.10
-69 impl_error scen3    4 102  1.10
-70 impl_error scen3    5 102  1.10
-71 impl_error scen3    1 103  1.10
-72 impl_error scen3    2 103  1.10
-73 impl_error scen3    3 103  1.10
-74 impl_error scen3    4 103  1.10
-75 impl_error scen3    5 103  1.10
-76 impl_error scen3    1 104  1.10
-77 impl_error scen3    2 104  1.10
-78 impl_error scen3    3 104  1.10
-79 impl_error scen3    4 104  1.10
-80 impl_error scen3    5 104  1.10
-81 impl_error scen3    1 105  1.10
-82 impl_error scen3    2 105  1.10
-83 impl_error scen3    3 105  1.10
-84 impl_error scen3    4 105  1.10
-85 impl_error scen3    5 105  1.10
-86 impl_error scen3    1 106  1.10
-87 impl_error scen3    2 106  1.10
-88 impl_error scen3    3 106  1.10
-89 impl_error scen3    4 106  1.10
-90 impl_error scen3    5 106  1.10
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The inputs required for pattern = custom are similar to using pattern = model_change, but there is no need to specify a distribution as the second vector input of “pattern” and the column names in input are different. Also that the change is “randomized”, which indicates that a value for each scenario and each iteration are necessary, whereas if the first value of “scen” was “replicate”, separate values for each scenario should not be specified, but rather a single value for “all” should be used. The columns in the dataframe are:

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4.5.6 How is the operating modified to accomodate changes as specified in the future_OM_list?

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All changes are made by converting the parameter(s) with changes to be time varing by using additive annual parameter deviations. Because this is an operating model, Stock Synthesis is not run with estimation, so to get the changes into the OM, values (drawn or calculated in model_changes or specified by the user using “custom”) are directly input as parameter deviations into the ss.par file.

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If an OM already contains time varying parameters, these parameters will also be converted to additive parameter deviations before applying the changes specified in the future_OM_list.

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4.5.7 Example of specifying recruitment deviations

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This shows some code for putting recruitment deviations with a mean of 0 and the same standard deviation as the historical recruitment deviations in years 1 to 100:

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5 Helper functions in SSMSE

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run_SSMSE() requires some detailed inputs that are time consuming to make. In the interest of automating routines as much as possible, helper functions are available in SSMSE.

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5.1 Creating multiple operating models with develop_OMs()

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This function allows users to change a parameter in a Stock Synthesis OM and, if desired, refit the operating model to data. Typically, users will want to create multiple operating models to use in different scenarios in order to account for uncertainties in parameter values, such as biological parameters relating to natural mortality or growth.

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5.2 Set up sampling for a scenario with create_sample_struct()

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run_SSMSE() requires the input sample_struct, which outlines the future sampling structure to use to generate data sets. This object could be difficult to create manually and users may want to just continue sampling as previously done in an existing SS model. create_sample_struct() will use the time patterns in sampling for different data types found in an SS data file and extend it forward in time. If no pattern is found, the function will return NAs and provide a warning to the user. If any NAs are returned (except to indicate that an entire data type is missing), the user will need to remove or fill in the NA value before usign the list as an input to run_SSMSE().

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See use of create_sample_struct() in simple example.

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5.3 Get examples of the future_om_list input with create_future_om_list()

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Users can modify the future structure of their operating model through the future_om_list input to run_SSMSE. The structure and names of this object matter, so examples are provided by calling create_future_om_list().

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See more details about the specification of future_om_list objects.

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+ Management Strategy Evaluation for SS +
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Preface

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Logo for the SSMSE package

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This is the user manual for SSMSE, an R package for Management Strategy Evaluation with Stock Synthesis Operating models. This document is still a work in progress! If you would like to suggest changes, the R markdown files used to generate the user manual are available in a bookdown folder within the SSMSE repository. Please feel free to open an issue or submit a pull request to suggest changes.

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R session information

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R version 4.0.2 Patched (2020-07-15 r78861)
-Platform: x86_64-w64-mingw32/x64 (64-bit)
-Running under: Windows 10 x64 (build 18363)
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-Matrix products: default
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-attached base packages:
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+ Management Strategy Evaluation for SS (SSMSE) + +
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+ + Type: associated tool + +
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+ Author: Doering, K., and N. Vaughan + +
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+ SSMSE is 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.
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1 Introduction

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1.1 Purpose

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SSMSE allows users to directly use Stock Synthesis (SS) as an operating model (OM) in an Management Strategy Evaluation (MSE). The approach requires a conditioned Stock Synthesis model, which is treated as the OM.

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1.2 Functions in SSMSE

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The main functions users can call in SSMSE are:

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FunctionDescription
run_SSMSE()Run the MSE simulations
SSMSE_summary_all()Summarize MSE output
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The helper functions to create inputs to run_SSMSE are:

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Helper FunctionDescription
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
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Exported functions that can be used for writing custom management strategies are:

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FunctionDescription
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.
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Finally, some plotting functions are available:

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Plotting FunctionDescription
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.
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1.3 Brief description of the SSMSE MSE simulation procedure

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1.3.1 Conditioning the OM and sampling from the OM

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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.

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1.3.2 First run of the management strategy in the MSE simulation

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The bootstrapped dataset is then used in a Management strategy to project catch by fleet to use for the next \(n\) years, where \(n\) is the number of years between assessments.

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1.3.3 Feedback from managment strategy into OM: extending model years

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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.

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1.3.4 Subsequent runs of the management strategy

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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.

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div.proposition, div.conjecture { - font-style: italic; -} -span.theorem, span.lemma, span.corollary, span.proposition, span.conjecture { - font-style: normal; -} -div.proof>*:last-child:after { - content: "\25a2"; - float: right; -} -.header-section-number { - padding-right: .5em; -} diff --git a/docs/libs/gitbook-2.6.7/css/plugin-clipboard.css b/docs/libs/gitbook-2.6.7/css/plugin-clipboard.css deleted file mode 100644 index 6844a70a..00000000 --- a/docs/libs/gitbook-2.6.7/css/plugin-clipboard.css +++ /dev/null @@ -1,18 +0,0 @@ -div.sourceCode { - position: relative; -} - -.copy-to-clipboard-button { - position: absolute; - right: 0; - top: 0; - visibility: hidden; -} - -.copy-to-clipboard-button:focus { - outline: 0; -} - -div.sourceCode:hover > .copy-to-clipboard-button { - visibility: visible; -} diff --git a/docs/libs/gitbook-2.6.7/css/plugin-fontsettings.css b/docs/libs/gitbook-2.6.7/css/plugin-fontsettings.css deleted file mode 100644 index 87236b4c..00000000 --- a/docs/libs/gitbook-2.6.7/css/plugin-fontsettings.css +++ /dev/null @@ -1,292 +0,0 @@ -/* - * Theme 1 - */ -.color-theme-1 .dropdown-menu { - background-color: #111111; - border-color: #7e888b; -} -.color-theme-1 .dropdown-menu .dropdown-caret .caret-inner { - border-bottom: 9px solid #111111; -} -.color-theme-1 .dropdown-menu .buttons { - border-color: #7e888b; -} -.color-theme-1 .dropdown-menu .button { - color: #afa790; -} -.color-theme-1 .dropdown-menu .button:hover { - color: #73553c; -} -/* - * Theme 2 - */ -.color-theme-2 .dropdown-menu { - background-color: #2d3143; - border-color: #272a3a; -} -.color-theme-2 .dropdown-menu .dropdown-caret .caret-inner { - border-bottom: 9px solid #2d3143; -} -.color-theme-2 .dropdown-menu .buttons { - border-color: #272a3a; -} -.color-theme-2 .dropdown-menu .button { - color: #62677f; -} -.color-theme-2 .dropdown-menu .button:hover { - color: #f4f4f5; -} -.book .book-header .font-settings .font-enlarge { - line-height: 30px; - font-size: 1.4em; -} -.book .book-header .font-settings .font-reduce { - line-height: 30px; - font-size: 1em; -} -.book.color-theme-1 .book-body { - color: #704214; - background: #f3eacb; -} -.book.color-theme-1 .book-body .page-wrapper .page-inner section { - background: #f3eacb; -} -.book.color-theme-2 .book-body { - color: #bdcadb; - background: #1c1f2b; -} -.book.color-theme-2 .book-body .page-wrapper .page-inner section { - background: #1c1f2b; -} -.book.font-size-0 .book-body .page-inner section { - font-size: 1.2rem; -} -.book.font-size-1 .book-body .page-inner section { - font-size: 1.4rem; -} -.book.font-size-2 .book-body .page-inner section { - font-size: 1.6rem; -} -.book.font-size-3 .book-body .page-inner section { - font-size: 2.2rem; -} -.book.font-size-4 .book-body .page-inner section { - font-size: 4rem; -} -.book.font-family-0 { - font-family: Georgia, serif; -} -.book.font-family-1 { - font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; -} -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal { - color: #704214; -} -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal a { - color: inherit; -} -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h1, -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h2, -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h3, -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h4, -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h5, -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h6 { - color: inherit; -} -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h1, -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h2 { - border-color: inherit; -} -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h6 { - color: inherit; -} -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal hr { - background-color: inherit; -} -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal blockquote { - border-color: #c4b29f; - opacity: 0.9; -} -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal pre, -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal code { - background: #fdf6e3; - color: #657b83; - border-color: #f8df9c; -} -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal .highlight { - background-color: inherit; -} -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal table th, -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal table td { - border-color: #f5d06c; -} -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal table tr { - color: inherit; - background-color: #fdf6e3; - border-color: #444444; -} -.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal table tr:nth-child(2n) { - background-color: #fbeecb; -} -.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal { - color: #bdcadb; -} -.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal a { - color: #3eb1d0; -} -.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h1, -.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h2, -.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h3, -.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h4, -.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h5, -.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h6 { - color: #fffffa; -} -.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h1, -.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h2 { - border-color: #373b4e; -} -.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h6 { - color: #373b4e; -} -.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal hr { - background-color: #373b4e; -} -.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal blockquote { - border-color: #373b4e; -} -.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal pre, -.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal code { - color: #9dbed8; - background: #2d3143; - border-color: #2d3143; -} -.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal .highlight { - background-color: #282a39; -} -.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal table th, -.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal table td { - border-color: #3b3f54; -} -.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal table tr { - color: #b6c2d2; - background-color: #2d3143; - border-color: #3b3f54; -} -.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal table tr:nth-child(2n) { - background-color: #35394b; -} -.book.color-theme-1 .book-header { - color: #afa790; - background: transparent; -} -.book.color-theme-1 .book-header .btn { - color: #afa790; -} -.book.color-theme-1 .book-header .btn:hover { - color: #73553c; - background: none; -} -.book.color-theme-1 .book-header h1 { - color: #704214; -} -.book.color-theme-2 .book-header { - color: #7e888b; - background: transparent; -} -.book.color-theme-2 .book-header .btn { - color: #3b3f54; -} -.book.color-theme-2 .book-header .btn:hover { - color: #fffff5; - background: none; -} -.book.color-theme-2 .book-header h1 { - color: #bdcadb; -} -.book.color-theme-1 .book-body .navigation { - color: #afa790; -} -.book.color-theme-1 .book-body .navigation:hover { - color: #73553c; -} -.book.color-theme-2 .book-body .navigation { - color: #383f52; -} -.book.color-theme-2 .book-body .navigation:hover { - color: #fffff5; -} -/* - * Theme 1 - */ -.book.color-theme-1 .book-summary { - color: #afa790; - background: #111111; - border-right: 1px solid rgba(0, 0, 0, 0.07); -} -.book.color-theme-1 .book-summary .book-search { - background: transparent; -} -.book.color-theme-1 .book-summary .book-search input, -.book.color-theme-1 .book-summary .book-search input:focus { - border: 1px solid transparent; -} -.book.color-theme-1 .book-summary ul.summary li.divider { - background: #7e888b; - box-shadow: none; -} -.book.color-theme-1 .book-summary ul.summary li i.fa-check { - color: #33cc33; -} -.book.color-theme-1 .book-summary ul.summary li.done > a { - color: #877f6a; -} -.book.color-theme-1 .book-summary ul.summary li a, -.book.color-theme-1 .book-summary ul.summary li span { - color: #877f6a; - background: transparent; - font-weight: normal; -} -.book.color-theme-1 .book-summary ul.summary li.active > a, -.book.color-theme-1 .book-summary ul.summary li a:hover { - color: #704214; - background: transparent; - font-weight: normal; -} -/* - * Theme 2 - */ -.book.color-theme-2 .book-summary { - color: #bcc1d2; - background: #2d3143; - border-right: none; -} -.book.color-theme-2 .book-summary .book-search { - background: transparent; -} -.book.color-theme-2 .book-summary .book-search input, -.book.color-theme-2 .book-summary .book-search input:focus { - border: 1px solid transparent; -} -.book.color-theme-2 .book-summary ul.summary li.divider { - background: #272a3a; - box-shadow: none; -} -.book.color-theme-2 .book-summary ul.summary li i.fa-check { - color: #33cc33; -} -.book.color-theme-2 .book-summary ul.summary li.done > a { - color: #62687f; -} -.book.color-theme-2 .book-summary ul.summary li a, -.book.color-theme-2 .book-summary ul.summary li span { - color: #c1c6d7; - background: transparent; - font-weight: 600; -} -.book.color-theme-2 .book-summary ul.summary li.active > a, -.book.color-theme-2 .book-summary ul.summary li a:hover { - color: #f4f4f5; - background: #252737; - font-weight: 600; -} diff --git a/docs/libs/gitbook-2.6.7/css/plugin-highlight.css 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isObj=isObject(source),props=isObj?keys(source):undefined,methodNames=props&&props.length?baseFunctions(source,props):undefined;if(!(methodNames?methodNames.length:isObj)){methodNames=false;options=source;source=object;object=this}}if(!methodNames){methodNames=baseFunctions(source,keys(source))}var chain=true,index=-1,isFunc=isFunction(object),length=methodNames.length;if(options===false){chain=false}else if(isObject(options)&&"chain"in options){chain=options.chain}while(++index0||end<0)){return new LazyWrapper(result)}if(start<0){result=result.takeRight(-start)}else if(start){result=result.drop(start)}if(end!==undefined){end=+end||0;result=end<0?result.dropRight(-end):result.take(end-start)}return result};LazyWrapper.prototype.takeRightWhile=function(predicate,thisArg){return this.reverse().takeWhile(predicate,thisArg).reverse()};LazyWrapper.prototype.toArray=function(){return this.take(POSITIVE_INFINITY)};baseForOwn(LazyWrapper.prototype,function(func,methodName){var 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if(freeExports&&freeModule){if(moduleExports){(freeModule.exports=_)._=_}else{freeExports._=_}}else{root._=_}}).call(this)}).call(this,typeof global!=="undefined"?global:typeof self!=="undefined"?self:typeof window!=="undefined"?window:{})},{}],3:[function(require,module,exports){(function(window,document,undefined){var _MAP={8:"backspace",9:"tab",13:"enter",16:"shift",17:"ctrl",18:"alt",20:"capslock",27:"esc",32:"space",33:"pageup",34:"pagedown",35:"end",36:"home",37:"left",38:"up",39:"right",40:"down",45:"ins",46:"del",91:"meta",93:"meta",224:"meta"};var _KEYCODE_MAP={106:"*",107:"+",109:"-",110:".",111:"/",186:";",187:"=",188:",",189:"-",190:".",191:"/",192:"`",219:"[",220:"\\",221:"]",222:"'"};var _SHIFT_MAP={"~":"`","!":"1","@":"2","#":"3",$:"4","%":"5","^":"6","&":"7","*":"8","(":"9",")":"0",_:"-","+":"=",":":";",'"':"'","<":",",">":".","?":"/","|":"\\"};var _SPECIAL_ALIASES={option:"alt",command:"meta",return:"enter",escape:"esc",plus:"+",mod:/Mac|iPod|iPhone|iPad/.test(navigator.platform)?"meta":"ctrl"};var _REVERSE_MAP;for(var i=1;i<20;++i){_MAP[111+i]="f"+i}for(i=0;i<=9;++i){_MAP[i+96]=i}function _addEvent(object,type,callback){if(object.addEventListener){object.addEventListener(type,callback,false);return}object.attachEvent("on"+type,callback)}function _characterFromEvent(e){if(e.type=="keypress"){var character=String.fromCharCode(e.which);if(!e.shiftKey){character=character.toLowerCase()}return character}if(_MAP[e.which]){return _MAP[e.which]}if(_KEYCODE_MAP[e.which]){return _KEYCODE_MAP[e.which]}return String.fromCharCode(e.which).toLowerCase()}function _modifiersMatch(modifiers1,modifiers2){return modifiers1.sort().join(",")===modifiers2.sort().join(",")}function _eventModifiers(e){var modifiers=[];if(e.shiftKey){modifiers.push("shift")}if(e.altKey){modifiers.push("alt")}if(e.ctrlKey){modifiers.push("ctrl")}if(e.metaKey){modifiers.push("meta")}return modifiers}function _preventDefault(e){if(e.preventDefault){e.preventDefault();return}e.returnValue=false}function _stopPropagation(e){if(e.stopPropagation){e.stopPropagation();return}e.cancelBubble=true}function _isModifier(key){return key=="shift"||key=="ctrl"||key=="alt"||key=="meta"}function _getReverseMap(){if(!_REVERSE_MAP){_REVERSE_MAP={};for(var key in _MAP){if(key>95&&key<112){continue}if(_MAP.hasOwnProperty(key)){_REVERSE_MAP[_MAP[key]]=key}}}return _REVERSE_MAP}function _pickBestAction(key,modifiers,action){if(!action){action=_getReverseMap()[key]?"keydown":"keypress"}if(action=="keypress"&&modifiers.length){action="keydown"}return action}function _keysFromString(combination){if(combination==="+"){return["+"]}combination=combination.replace(/\+{2}/g,"+plus");return combination.split("+")}function _getKeyInfo(combination,action){var keys;var key;var i;var modifiers=[];keys=_keysFromString(combination);for(i=0;i1){_bindSequence(combination,sequence,callback,action);return}info=_getKeyInfo(combination,action);self._callbacks[info.key]=self._callbacks[info.key]||[];_getMatches(info.key,info.modifiers,{type:info.action},sequenceName,combination,level);self._callbacks[info.key][sequenceName?"unshift":"push"]({callback:callback,modifiers:info.modifiers,action:info.action,seq:sequenceName,level:level,combo:combination})}self._bindMultiple=function(combinations,callback,action){for(var i=0;i-1){return false}if(_belongsTo(element,self.target)){return false}return element.tagName=="INPUT"||element.tagName=="SELECT"||element.tagName=="TEXTAREA"||element.isContentEditable};Mousetrap.prototype.handleKey=function(){var self=this;return self._handleKey.apply(self,arguments)};Mousetrap.init=function(){var documentMousetrap=Mousetrap(document);for(var method in documentMousetrap){if(method.charAt(0)!=="_"){Mousetrap[method]=function(method){return function(){return documentMousetrap[method].apply(documentMousetrap,arguments)}}(method)}}};Mousetrap.init();window.Mousetrap=Mousetrap;if(typeof module!=="undefined"&&module.exports){module.exports=Mousetrap}if(typeof define==="function"&&define.amd){define(function(){return Mousetrap})}})(window,document)},{}],4:[function(require,module,exports){(function(process){function normalizeArray(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last==="."){parts.splice(i,1)}else if(last===".."){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up--;up){parts.unshift("..")}}return parts}var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;var splitPath=function(filename){return splitPathRe.exec(filename).slice(1)};exports.resolve=function(){var resolvedPath="",resolvedAbsolute=false;for(var i=arguments.length-1;i>=-1&&!resolvedAbsolute;i--){var path=i>=0?arguments[i]:process.cwd();if(typeof path!=="string"){throw new TypeError("Arguments to path.resolve must be strings")}else if(!path){continue}resolvedPath=path+"/"+resolvedPath;resolvedAbsolute=path.charAt(0)==="/"}resolvedPath=normalizeArray(filter(resolvedPath.split("/"),function(p){return!!p}),!resolvedAbsolute).join("/");return(resolvedAbsolute?"/":"")+resolvedPath||"."};exports.normalize=function(path){var isAbsolute=exports.isAbsolute(path),trailingSlash=substr(path,-1)==="/";path=normalizeArray(filter(path.split("/"),function(p){return!!p}),!isAbsolute).join("/");if(!path&&!isAbsolute){path="."}if(path&&trailingSlash){path+="/"}return(isAbsolute?"/":"")+path};exports.isAbsolute=function(path){return path.charAt(0)==="/"};exports.join=function(){var paths=Array.prototype.slice.call(arguments,0);return exports.normalize(filter(paths,function(p,index){if(typeof p!=="string"){throw new TypeError("Arguments to path.join must be strings")}return p}).join("/"))};exports.relative=function(from,to){from=exports.resolve(from).substr(1);to=exports.resolve(to).substr(1);function trim(arr){var start=0;for(;start=0;end--){if(arr[end]!=="")break}if(start>end)return[];return arr.slice(start,end-start+1)}var fromParts=trim(from.split("/"));var toParts=trim(to.split("/"));var length=Math.min(fromParts.length,toParts.length);var samePartsLength=length;for(var i=0;i1){for(var i=1;i= 0x80 (not a basic code point)","invalid-input":"Invalid input"},baseMinusTMin=base-tMin,floor=Math.floor,stringFromCharCode=String.fromCharCode,key;function error(type){throw RangeError(errors[type])}function map(array,fn){var length=array.length;var result=[];while(length--){result[length]=fn(array[length])}return result}function mapDomain(string,fn){var parts=string.split("@");var result="";if(parts.length>1){result=parts[0]+"@";string=parts[1]}string=string.replace(regexSeparators,".");var labels=string.split(".");var encoded=map(labels,fn).join(".");return result+encoded}function ucs2decode(string){var output=[],counter=0,length=string.length,value,extra;while(counter=55296&&value<=56319&&counter65535){value-=65536;output+=stringFromCharCode(value>>>10&1023|55296);value=56320|value&1023}output+=stringFromCharCode(value);return output}).join("")}function basicToDigit(codePoint){if(codePoint-48<10){return codePoint-22}if(codePoint-65<26){return codePoint-65}if(codePoint-97<26){return codePoint-97}return base}function digitToBasic(digit,flag){return digit+22+75*(digit<26)-((flag!=0)<<5)}function adapt(delta,numPoints,firstTime){var k=0;delta=firstTime?floor(delta/damp):delta>>1;delta+=floor(delta/numPoints);for(;delta>baseMinusTMin*tMax>>1;k+=base){delta=floor(delta/baseMinusTMin)}return floor(k+(baseMinusTMin+1)*delta/(delta+skew))}function decode(input){var output=[],inputLength=input.length,out,i=0,n=initialN,bias=initialBias,basic,j,index,oldi,w,k,digit,t,baseMinusT;basic=input.lastIndexOf(delimiter);if(basic<0){basic=0}for(j=0;j=128){error("not-basic")}output.push(input.charCodeAt(j))}for(index=basic>0?basic+1:0;index=inputLength){error("invalid-input")}digit=basicToDigit(input.charCodeAt(index++));if(digit>=base||digit>floor((maxInt-i)/w)){error("overflow")}i+=digit*w;t=k<=bias?tMin:k>=bias+tMax?tMax:k-bias;if(digitfloor(maxInt/baseMinusT)){error("overflow")}w*=baseMinusT}out=output.length+1;bias=adapt(i-oldi,out,oldi==0);if(floor(i/out)>maxInt-n){error("overflow")}n+=floor(i/out);i%=out;output.splice(i++,0,n)}return ucs2encode(output)}function encode(input){var n,delta,handledCPCount,basicLength,bias,j,m,q,k,t,currentValue,output=[],inputLength,handledCPCountPlusOne,baseMinusT,qMinusT;input=ucs2decode(input);inputLength=input.length;n=initialN;delta=0;bias=initialBias;for(j=0;j=n&¤tValuefloor((maxInt-delta)/handledCPCountPlusOne)){error("overflow")}delta+=(m-n)*handledCPCountPlusOne;n=m;for(j=0;jmaxInt){error("overflow")}if(currentValue==n){for(q=delta,k=base;;k+=base){t=k<=bias?tMin:k>=bias+tMax?tMax:k-bias;if(q0&&len>maxKeys){len=maxKeys}for(var i=0;i=0){kstr=x.substr(0,idx);vstr=x.substr(idx+1)}else{kstr=x;vstr=""}k=decodeURIComponent(kstr);v=decodeURIComponent(vstr);if(!hasOwnProperty(obj,k)){obj[k]=v}else if(isArray(obj[k])){obj[k].push(v)}else{obj[k]=[obj[k],v]}}return obj};var isArray=Array.isArray||function(xs){return Object.prototype.toString.call(xs)==="[object Array]"}},{}],8:[function(require,module,exports){"use strict";var stringifyPrimitive=function(v){switch(typeof v){case"string":return v;case"boolean":return v?"true":"false";case"number":return isFinite(v)?v:"";default:return""}};module.exports=function(obj,sep,eq,name){sep=sep||"&";eq=eq||"=";if(obj===null){obj=undefined}if(typeof obj==="object"){return map(objectKeys(obj),function(k){var ks=encodeURIComponent(stringifyPrimitive(k))+eq;if(isArray(obj[k])){return map(obj[k],function(v){return ks+encodeURIComponent(stringifyPrimitive(v))}).join(sep)}else{return ks+encodeURIComponent(stringifyPrimitive(obj[k]))}}).join(sep)}if(!name)return"";return encodeURIComponent(stringifyPrimitive(name))+eq+encodeURIComponent(stringifyPrimitive(obj))};var isArray=Array.isArray||function(xs){return Object.prototype.toString.call(xs)==="[object Array]"};function map(xs,f){if(xs.map)return xs.map(f);var res=[];for(var i=0;i",'"',"`"," ","\r","\n","\t"],unwise=["{","}","|","\\","^","`"].concat(delims),autoEscape=["'"].concat(unwise),nonHostChars=["%","/","?",";","#"].concat(autoEscape),hostEndingChars=["/","?","#"],hostnameMaxLen=255,hostnamePartPattern=/^[a-z0-9A-Z_-]{0,63}$/,hostnamePartStart=/^([a-z0-9A-Z_-]{0,63})(.*)$/,unsafeProtocol={javascript:true,"javascript:":true},hostlessProtocol={javascript:true,"javascript:":true},slashedProtocol={http:true,https:true,ftp:true,gopher:true,file:true,"http:":true,"https:":true,"ftp:":true,"gopher:":true,"file:":true},querystring=require("querystring");function urlParse(url,parseQueryString,slashesDenoteHost){if(url&&isObject(url)&&url instanceof Url)return url;var u=new Url;u.parse(url,parseQueryString,slashesDenoteHost);return u}Url.prototype.parse=function(url,parseQueryString,slashesDenoteHost){if(!isString(url)){throw new TypeError("Parameter 'url' must be a string, not "+typeof url)}var rest=url;rest=rest.trim();var proto=protocolPattern.exec(rest);if(proto){proto=proto[0];var lowerProto=proto.toLowerCase();this.protocol=lowerProto;rest=rest.substr(proto.length)}if(slashesDenoteHost||proto||rest.match(/^\/\/[^@\/]+@[^@\/]+/)){var slashes=rest.substr(0,2)==="//";if(slashes&&!(proto&&hostlessProtocol[proto])){rest=rest.substr(2);this.slashes=true}}if(!hostlessProtocol[proto]&&(slashes||proto&&!slashedProtocol[proto])){var hostEnd=-1;for(var i=0;i127){newpart+="x"}else{newpart+=part[j]}}if(!newpart.match(hostnamePartPattern)){var validParts=hostparts.slice(0,i);var notHost=hostparts.slice(i+1);var bit=part.match(hostnamePartStart);if(bit){validParts.push(bit[1]);notHost.unshift(bit[2])}if(notHost.length){rest="/"+notHost.join(".")+rest}this.hostname=validParts.join(".");break}}}}if(this.hostname.length>hostnameMaxLen){this.hostname=""}else{this.hostname=this.hostname.toLowerCase()}if(!ipv6Hostname){var domainArray=this.hostname.split(".");var newOut=[];for(var i=0;i0?result.host.split("@"):false;if(authInHost){result.auth=authInHost.shift();result.host=result.hostname=authInHost.shift()}}result.search=relative.search;result.query=relative.query;if(!isNull(result.pathname)||!isNull(result.search)){result.path=(result.pathname?result.pathname:"")+(result.search?result.search:"")}result.href=result.format();return result}if(!srcPath.length){result.pathname=null;if(result.search){result.path="/"+result.search}else{result.path=null}result.href=result.format();return result}var last=srcPath.slice(-1)[0];var hasTrailingSlash=(result.host||relative.host)&&(last==="."||last==="..")||last==="";var up=0;for(var i=srcPath.length;i>=0;i--){last=srcPath[i];if(last=="."){srcPath.splice(i,1)}else if(last===".."){srcPath.splice(i,1);up++}else if(up){srcPath.splice(i,1);up--}}if(!mustEndAbs&&!removeAllDots){for(;up--;up){srcPath.unshift("..")}}if(mustEndAbs&&srcPath[0]!==""&&(!srcPath[0]||srcPath[0].charAt(0)!=="/")){srcPath.unshift("")}if(hasTrailingSlash&&srcPath.join("/").substr(-1)!=="/"){srcPath.push("")}var isAbsolute=srcPath[0]===""||srcPath[0]&&srcPath[0].charAt(0)==="/";if(psychotic){result.hostname=result.host=isAbsolute?"":srcPath.length?srcPath.shift():"";var authInHost=result.host&&result.host.indexOf("@")>0?result.host.split("@"):false;if(authInHost){result.auth=authInHost.shift();result.host=result.hostname=authInHost.shift()}}mustEndAbs=mustEndAbs||result.host&&srcPath.length;if(mustEndAbs&&!isAbsolute){srcPath.unshift("")}if(!srcPath.length){result.pathname=null;result.path=null}else{result.pathname=srcPath.join("/")}if(!isNull(result.pathname)||!isNull(result.search)){result.path=(result.pathname?result.pathname:"")+(result.search?result.search:"")}result.auth=relative.auth||result.auth;result.slashes=result.slashes||relative.slashes;result.href=result.format();return result};Url.prototype.parseHost=function(){var host=this.host;var port=portPattern.exec(host);if(port){port=port[0];if(port!==":"){this.port=port.substr(1)}host=host.substr(0,host.length-port.length)}if(host)this.hostname=host};function isString(arg){return typeof arg==="string"}function isObject(arg){return typeof arg==="object"&&arg!==null}function isNull(arg){return arg===null}function isNullOrUndefined(arg){return arg==null}},{punycode:6,querystring:9}],11:[function(require,module,exports){var $=require("jquery");function toggleDropdown(e){var $dropdown=$(e.currentTarget).parent().find(".dropdown-menu");$dropdown.toggleClass("open");e.stopPropagation();e.preventDefault()}function closeDropdown(e){$(".dropdown-menu").removeClass("open")}function init(){$(document).on("click",".toggle-dropdown",toggleDropdown);$(document).on("click",".dropdown-menu",function(e){e.stopPropagation()});$(document).on("click",closeDropdown)}module.exports={init:init}},{jquery:1}],12:[function(require,module,exports){var $=require("jquery");module.exports=$({})},{jquery:1}],13:[function(require,module,exports){var $=require("jquery");var _=require("lodash");var storage=require("./storage");var dropdown=require("./dropdown");var events=require("./events");var state=require("./state");var keyboard=require("./keyboard");var navigation=require("./navigation");var sidebar=require("./sidebar");var toolbar=require("./toolbar");function start(config){sidebar.init();keyboard.init();dropdown.init();navigation.init();toolbar.createButton({index:0,icon:"fa fa-align-justify",label:"Toggle Sidebar",onClick:function(e){e.preventDefault();sidebar.toggle()}});events.trigger("start",config);navigation.notify()}var gitbook={start:start,events:events,state:state,toolbar:toolbar,sidebar:sidebar,storage:storage,keyboard:keyboard};var MODULES={gitbook:gitbook,jquery:$,lodash:_};window.gitbook=gitbook;window.$=$;window.jQuery=$;gitbook.require=function(mods,fn){mods=_.map(mods,function(mod){mod=mod.toLowerCase();if(!MODULES[mod]){throw new Error("GitBook module "+mod+" doesn't exist")}return MODULES[mod]});fn.apply(null,mods)};module.exports={}},{"./dropdown":11,"./events":12,"./keyboard":14,"./navigation":16,"./sidebar":18,"./state":19,"./storage":20,"./toolbar":21,jquery:1,lodash:2}],14:[function(require,module,exports){var Mousetrap=require("mousetrap");var navigation=require("./navigation");var sidebar=require("./sidebar");function bindShortcut(keys,fn){Mousetrap.bind(keys,function(e){fn();return false})}function init(){bindShortcut(["right"],function(e){navigation.goNext()});bindShortcut(["left"],function(e){navigation.goPrev()});bindShortcut(["s"],function(e){sidebar.toggle()})}module.exports={init:init,bind:bindShortcut}},{"./navigation":16,"./sidebar":18,mousetrap:3}],15:[function(require,module,exports){var state=require("./state");function showLoading(p){state.$book.addClass("is-loading");p.always(function(){state.$book.removeClass("is-loading")});return p}module.exports={show:showLoading}},{"./state":19}],16:[function(require,module,exports){var $=require("jquery");var url=require("url");var events=require("./events");var state=require("./state");var loading=require("./loading");var usePushState=typeof history.pushState!=="undefined";function handleNavigation(relativeUrl,push){var uri=url.resolve(window.location.pathname,relativeUrl);notifyPageChange();location.href=relativeUrl;return}function updateNavigationPosition(){var bodyInnerWidth,pageWrapperWidth;bodyInnerWidth=parseInt($(".body-inner").css("width"),10);pageWrapperWidth=parseInt($(".page-wrapper").css("width"),10);$(".navigation-next").css("margin-right",bodyInnerWidth-pageWrapperWidth+"px")}function notifyPageChange(){events.trigger("page.change")}function preparePage(notify){var $bookBody=$(".book-body");var $bookInner=$bookBody.find(".body-inner");var $pageWrapper=$bookInner.find(".page-wrapper");updateNavigationPosition();$bookInner.scrollTop(0);$bookBody.scrollTop(0);if(notify!==false)notifyPageChange()}function isLeftClickEvent(e){return e.button===0}function isModifiedEvent(e){return!!(e.metaKey||e.altKey||e.ctrlKey||e.shiftKey)}function handlePagination(e){if(isModifiedEvent(e)||!isLeftClickEvent(e)){return}e.stopPropagation();e.preventDefault();var url=$(this).attr("href");if(url)handleNavigation(url,true)}function goNext(){var url=$(".navigation-next").attr("href");if(url)handleNavigation(url,true)}function goPrev(){var url=$(".navigation-prev").attr("href");if(url)handleNavigation(url,true)}function init(){$.ajaxSetup({});if(location.protocol!=="file:"){history.replaceState({path:window.location.href},"")}window.onpopstate=function(event){if(event.state===null){return}return handleNavigation(event.state.path,false)};$(document).on("click",".navigation-prev",handlePagination);$(document).on("click",".navigation-next",handlePagination);$(document).on("click",".summary [data-path] a",handlePagination);$(window).resize(updateNavigationPosition);preparePage(false)}module.exports={init:init,goNext:goNext,goPrev:goPrev,notify:notifyPageChange}},{"./events":12,"./loading":15,"./state":19,jquery:1,url:10}],17:[function(require,module,exports){module.exports={isMobile:function(){return document.body.clientWidth<=600}}},{}],18:[function(require,module,exports){var $=require("jquery");var _=require("lodash");var storage=require("./storage");var platform=require("./platform");var state=require("./state");function toggleSidebar(_state,animation){if(state!=null&&isOpen()==_state)return;if(animation==null)animation=true;state.$book.toggleClass("without-animation",!animation);state.$book.toggleClass("with-summary",_state);storage.set("sidebar",isOpen())}function isOpen(){return state.$book.hasClass("with-summary")}function init(){if(platform.isMobile()){toggleSidebar(false,false)}else{toggleSidebar(storage.get("sidebar",true),false)}$(document).on("click",".book-summary li.chapter a",function(e){if(platform.isMobile())toggleSidebar(false,false)})}function filterSummary(paths){var $summary=$(".book-summary");$summary.find("li").each(function(){var path=$(this).data("path");var st=paths==null||_.contains(paths,path);$(this).toggle(st);if(st)$(this).parents("li").show()})}module.exports={init:init,isOpen:isOpen,toggle:toggleSidebar,filter:filterSummary}},{"./platform":17,"./state":19,"./storage":20,jquery:1,lodash:2}],19:[function(require,module,exports){var $=require("jquery");var url=require("url");var path=require("path");var state={};state.update=function(dom){var $book=$(dom.find(".book"));state.$book=$book;state.level=$book.data("level");state.basePath=$book.data("basepath");state.innerLanguage=$book.data("innerlanguage");state.revision=$book.data("revision");state.filepath=$book.data("filepath");state.chapterTitle=$book.data("chapter-title");state.root=url.resolve(location.protocol+"//"+location.host,path.dirname(path.resolve(location.pathname.replace(/\/$/,"/index.html"),state.basePath))).replace(/\/?$/,"/");state.bookRoot=state.innerLanguage?url.resolve(state.root,".."):state.root};state.update($);module.exports=state},{jquery:1,path:4,url:10}],20:[function(require,module,exports){var baseKey="";module.exports={setBaseKey:function(key){baseKey=key},set:function(key,value){key=baseKey+":"+key;try{sessionStorage[key]=JSON.stringify(value)}catch(e){}},get:function(key,def){key=baseKey+":"+key;if(sessionStorage[key]===undefined)return def;try{var v=JSON.parse(sessionStorage[key]);return v==null?def:v}catch(err){return 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