Installation and Usage Guidelines

We decided to test a few packages with a simulated data set – two packages in each of the data-limited and moderate model types that fell into our Supported and Recommended list. This is not to replace the developer’s tools, but as a complimentary resource using data that has not been produced for the specific package’s purpose. We hope this helps an assessor get further input into how to download and use the package.

We excluded the data-rich assessments such as Integrated assessments given there complexity and requirement for large amounts of guidance. In these cases, it is best to use the developer’s resources.

The main coding resources we have used is R markdown which is a specific file type from the freely available statistical package R (R Core Team. 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

R markdown is useful in that you can see our code and also find out in a step-by-step manner how to access the specific package and use our data to undertake an assessment. You can download the *.rmd file and data below to run the model and modify the text. We have not tried to write the neatest R code. This is partly a reflection of our skill and also that often very good R code is difficult for a novice R user to understand. We try also not add too many additional libraries beyond what is needed.

These packages are all run from R. R can be installed

The recommended way to run R is using RStudio. After you have installed R, then install RStudio. It can be found  Although RStudio is a registered commercial product, we are using the freely available, open-source version here.

We have assumed that you will save the package data in the same directory you used to save and open the *.rmd file. The data we have used plus the R markdown (*.rmd) script files are also provided.

The packages are:

Catch curve methods

  1. Catch curve with selectivity (from the datalowSA package) – catch-at-age data and R Markdown Script
  2. Catch Curve (from the TropfishR package) – catch-at-age, length composition data and R Markdown Script

Catch only methods

  1. cMSY (from the datalowSA package) – catch data and R Markdown Script
  2. DB-SRA (from the fishmethods package) – catch data and R Markdown Script

Length-based methods

  1. LBSPRlength composition data and R Markdown Script
  2. LIMElength composition data, and can also use catch-at-age data; R Markdown Script – NOTE: LIME as yet does not work on R4.0.4 but does on R3.9

Surplus production methods

  1. JABBAcatch and cpue data; R Markdown Script
  2. SPM (from the datalowSA package) – catch and cpue data; R Markdown Script