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R package for working with the Fishery Regulation Assessment Model (FRAM)

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FRAMverse/framr

framr

Lifecycle: experimental CRAN status

The goal of framr is to make it easier and faster to perform common tasks associated with the FRAM model.

The package currently consists of high-level convenience functions that typically require both updated R/RStudio installations and various FRAM-related files (e.g., Access project databases, Excel files).

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("FRAMverse/framr")

Several functions also require the Windows/Office low-level tools in the RDCOMClient package, which (as of 2021-07-19) can be installed with:

#worked with 4.1.0 to get to ver 0.95-0
devtools::install_github("BSchamberger/RDCOMClient")

## compiled but fatal errors when running 4.1.0
#devtools::install_github("omegahat/RDCOMClient")

##no longer working?
#install.packages("RDCOMClient", repos = "http://www.omegahat.net/R")

Examples

The function aeq_mort quickly generates AEQ’d mortality values for Chinook.

library(framr)

m_hcff <- aeq_mort(
  db = "path/to/ChinookFRAM.mdb",
  runs = 201, #defaults to all RunIDs
  stocks = 31:32, #defaults to all StockIDS 
  drop_t1 = T, #defaults to TRUE for "fishing year" t2:t4
  sum_ages = T #defaults to FALSE for disaggregated returned object
  )

The functions ayock_split and sps perform Chinook post-processing tasks, altering TAMM file values.

The functions rep_ocean_opt and update_2s perform Chinook database manipulation tasks.

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R package for working with the Fishery Regulation Assessment Model (FRAM)

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MIT, MIT licenses found

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MIT
LICENSE.md

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