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VAST

Installation Instructions

Build Status [DOI](https://zenodo.org/badge/55002718.

This function depends on R version >=3.1.1 and a variety of other tools.

First, install the “devtools” package from CRAN

# Install and load devtools package
install.packages("devtools")
library("devtools")

Next, please install the VAST package from this GitHub repository using a function in the “devtools” package. This may require using the INSTALL_opts option depending upon your version of R:

# Install package
install_github("james-thorson/VAST@main", INSTALL_opts="--no-staged-install")
# Load package
library(VAST)

If you are having problems with installation, please consider installing dependencies individually, e.g. using:

# Install TMB from CRAN
install.packages("TMB")
# Install INLA using currently recommended method
install.packages("INLA", repos=c(getOption("repos"), INLA="https://inla.r-inla-download.org/R/stable"), dep=TRUE)
# Install FishStatsUtils from CRAN
install_github("james-thorson/FishStatsUtils@main", INSTALL_opts="--no-staged-install")

Finally, please confirm that VAST is installed by running a model, e.g., following the simple example here.

Known installation/usage issues

  1. If using a NOAA laptop, sometimes the PATH for Rtools is not correctly specified during installation. In those cases, please follow instructions here

  2. Some versions of R are having problems downloading dependencies from GitHub, see details here

  3. People using R version 3.6.0 or MRAN 3.5.3 are having a problem with changing standards for package namespaces, see details here, which appears to be particularly a problem with loading INLA due to install issues with that package.

  4. MacOS users have specific install issues and a discussion of potential fixes is here

  5. MacOS users should be aware that significant speed-ups in model fitting can be accomplished by switching the library used for Basic Linear Algebra Subprograms (BLAS) from the default. There are a few BLAS alternatives available, though, the simplest seems to be using the vecLib library, part of Apple’s Accelerate Framework and included in most recent R binaries. To switch the BLAS library, run the following lines in the terminal and then confirm the switch with a call to sessionInfo() in R.

    # Terminal commands to switch R BLAS library to increase speed
    cd /Library/Frameworks/R.framework/Resources/lib
    ln -sf /System/Library/Frameworks/Accelerate.framework/Frameworks/vecLib.framework/Versions/Current/libBLAS.dylib libRblas.dylib
  1. Windows has a speed-limit on the rate that users can access the GitHub API. You can get around this by installing each package locally from a ZIP file. You’ll need to first download a ZIP file for GitHub repositories TMBhelper (here), then ThorsonUtilities (here), then FishStatsUtils (here), then VAST (here) to your harddrive in a local directory while recording the directory name (which I will reference as download_dir), and then install these packages from each ZIP file in the same order. To install each package, please click “clone or download” -> “Download ZIP” -> devtools::install_local(path=download_dir, dependencies=FALSE)