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Showing content from https://github.com/ropensci/software-review/issues/628 below:

an R package for interpreting paired environmental DNA and traditional surveys · Issue #628 · ropensci/software-review · GitHub

Submitting Author Name: Abigail Keller
Submitting Author Github Handle: @abigailkeller
Repository: https://github.com/abigailkeller/eDNAjoint
Submission type: Pre-submission
Language: en

• Paste the full DESCRIPTION file inside a code block below:

Version: 0.1
Maintainer: Abigail G. Keller <agkeller@berkeley.edu>
Authors@R: 
    c(person("Abigail G.", "Keller", role = c("aut", "cre"), email="agkeller@berkeley.edu"),
    person("Ryan P.", "Kelly", role = "ctb", email="rpkelly@uw.edu"))
Description: Models integrate environmental DNA (eDNA) detection data and traditional survey data to jointly estimate species catch rate. Models can be used with count data via traditional survey methods (i.e., trapping, electrofishing, visual) and replicated eDNA detection/nondetection data via polymerase chain reaction (i.e., PCR or qPCR) from multiple survey locations. Estimated parameters include probability of a false positive eDNA detection, a site-level covariates that scale the sensitivity of eDNA surveys relative to traditional surveys, and catchability coefficients for traditional gear types. Models are implemented with a Bayesian framework (Markov chain Monte Carlo) using the 'Stan' probabilistic programming language.
License: GPL-3
Encoding: UTF-8
Roxygen: list(markdown = TRUE, roclets = c ("namespace", "rd", "srr::srr_stats_roclet"))
RoxygenNote: 7.3.1
Biarch: true
Depends: 
    R (>= 3.4.0)
Imports: 
    bayestestR,
    dplyr,
    ggplot2,
    loo,
    magrittr,
    methods,
    Rcpp (>= 0.12.0),
    RcppParallel (>= 5.0.1),
    rstan (>= 2.26.23),
    rstantools (>= 2.3.1.1),
    tidyr
LinkingTo: 
    BH (>= 1.66.0),
    Rcpp (>= 0.12.0),
    RcppEigen (>= 0.3.3.3.0),
    RcppParallel (>= 5.0.1),
    rstan (>= 2.26.23),
    StanHeaders (>= 2.26.22)
SystemRequirements: GNU make
LazyData: true
Suggests: 
    knitr,
    rmarkdown,
    testthat (>= 3.0.0)
VignetteBuilder: knitr
Config/testthat/edition: 3

• Please indicate which category or categories from our package fit policies or statistical package categories this package falls under. (Please check an appropriate box below):

  Data Lifecycle Packages

  • data retrieval
  • data extraction
  • data munging
  • data deposition
  • data validation and testing
  • workflow automation
  • version control
  • citation management and bibliometrics
  • scientific software wrappers
  • field and lab reproducibility tools
  • database software bindings
  • geospatial data
  • text analysis

  Statistical Packages

  • Bayesian and Monte Carlo Routines
  • Dimensionality Reduction, Clustering, and Unsupervised Learning
  • Machine Learning
  • Regression and Supervised Learning
  • Exploratory Data Analysis (EDA) and Summary Statistics
  • Spatial Analyses
  • Time Series Analyses

• Explain how and why the package falls under these categories (briefly, 1-2 sentences). Please note any areas you are unsure of:

This package runs Bayesian statistical models written in Stan using data from paired environmental DNA (eDNA) and traditional surveys provided by a user. Additional functions support interpretation of model output.

• If submitting a statistical package, have you already incorporated documentation of standards into your code via the srr package?

Not yet.

• Who is the target audience and what are scientific applications of this package?

The target audience is environmental science researchers or managers who want to interpret environmental DNA data but do not have experience writing and implementing custom Bayesian models.

• Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category?

To my knowledge, no other R package implements similar types of models. Although the R package contains additional model variations, the model was originally developed in a publication: "Tracking an invasion front with environmental DNA", led by the authors of this submission.

• (If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?

Yes

• Any other questions or issues we should be aware of?:

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