R package for fitting distributions to run timing data via maximum likelihood
pkgdown site: https://nwfsc-cb.github.io/phenomix/
The DOI for this repository is
You can install phenomix with:
remotes::install_github("nwfsc-cb/phenomix",build_vignettes = TRUE)
#> Downloading GitHub repo nwfsc-cb/phenomix@HEAD
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#> ── R CMD build ─────────────────────────────────────────────────────────────────
#> checking for file ‘/private/var/folders/ts/4x6hzmfx7d52vbhjqmrs_3pw0000gp/T/RtmpjBCVFK/remotes161f77702fa38/nwfsc-cb-phenomix-a46f4fc/DESCRIPTION’ ... ✔ checking for file ‘/private/var/folders/ts/4x6hzmfx7d52vbhjqmrs_3pw0000gp/T/RtmpjBCVFK/remotes161f77702fa38/nwfsc-cb-phenomix-a46f4fc/DESCRIPTION’
#> ─ preparing ‘phenomix’:
#> checking DESCRIPTION meta-information ... ✔ checking DESCRIPTION meta-information
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#> ─ installing the package to build vignettes
#> creating vignettes ... ✔ creating vignettes (1m 34.3s)
#> ─ cleaning src
#> ─ checking for LF line-endings in source and make files and shell scripts
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#> Load libraries
library(phenomix)
library(ggplot2)
#> Warning: package 'ggplot2' was built under R version 4.3.2The package pheomix provides a suite of curve fitting to describe data that may be generated from a process when distributions in time might be concentrated (from fisheries, this occurs with counts over time of salmon returning from the ocean to spawn or juvenile fish emigrating from streams to the ocean).
In a given year, the curve might be described by a symmetric or asymmetric Gaussian or Student-t distribution (shown here in log-scale on the y-axis). Questions of interest might be - are the means (x-axis) shifting through time? - are the variances shifting through time? - does the model support a symmetric or asymmetric distribution?
The main functions are create_data() and fit(). See ?create_data
and ?fit for additional details and examples. A vignette includes
additional detail, and examples of several models as well as function
arguments available https://nwfsc-cb.github.io/phenomix/.
For description of fisheries applications of asymmetric models:
Methot, R.D. 2000. Technical description of the stock synthesis assessment program. U.S. Dept. Commer., NOAA Tech. Memo. NMFS-NWFSC-43, 46 p. link
For statistical background of asymmetric models:
Rubio, F.J. and Steel, M.F.J. 2020. The family of two-piece distributions. Significance, 17(1) 120–13. link
Wallis, K.F. 2014. The two-piece normal, binormal, or double Gaussian distribution: Its origin and rediscoveries. Statistical Science, 29(1), 106–112. link
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U.S. Department of Commerce | National Oceanographic and Atmospheric Administration | NOAA Fisheries