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Showing content from https://github.com/ropensci/software-review/issues/642 below:

R package for interpreting paired environmental DNA and traditional surveys · Issue #642 · ropensci/software-review · GitHub

Date accepted: 2024-09-26
Submitting Author Name: Abigail Keller
Submitting Author Github Handle: @abigailkeller
Repository: https://github.com/abigailkeller/eDNAjoint
Version submitted: 0.1
Submission type: Stats
Badge grade: silver
Editor: @emitanaka
Reviewers: @chitrams, @smwindecker

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

Package: eDNAjoint
Title: Joint Modeling of Traditional and Environmental DNA Survey Data
Version: 0.1
Maintainer: Abigail G. Keller <agkeller@berkeley.edu>
Author: Abigail G. Keller
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 (see package vignette: https://bookdown.org/abigailkeller/eDNAjoint_vignette/). 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
URL: https://github.com/abigailkeller/eDNAjoint
BugReports: https://github.com/abigailkeller/eDNAjoint/issues
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), 
    rlist,
    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: 
    bayesplot,
    knitr,
    rmarkdown,
    testthat (>= 3.0.0)
VignetteBuilder: knitr
Config/testthat/edition: 3

• Please indicate which of our statistical package categories this package falls under. (Please check one appropriate box below):

  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
  • Probability Distributions

• A pre-submission inquiry has been approved in issue 628

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

The package eDNAjoint is useful for interpreting observations from paired environmental DNA (eDNA) and traditional surveys. The package runs a Bayesian model that integrates these two data streams to jointly estimate parameters like the false positive probability of eDNA detection and expected catch rate at a site. The package allows users to access pre-compiled models written in Stan. 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.

• Paste your responses to our General Standard G1.1 here, describing whether your software is:

This is the first implementation of a model/algorithm developed in Keller et al. 2022.

• (If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?
  Not applicable.

• What grade of badge are you aiming for? (bronze, silver, gold)

Silver

• If aiming for silver or gold, describe which of the four aspects listed in the Guide for Authors chapter the package fulfils (at least one aspect for silver; three for gold)

Compliance with a good number of standards beyond those identified as minimally necessary.

Confirm each of the following by checking the box.

• I have read the rOpenSci packaging guide.
• I have read the author guide and I expect to maintain this package for at least 2 years or have another maintainer identified.
• I/we have read the Statistical Software Peer Review Guide for Authors.
• I/we have run autotest checks on the package, and ensured no tests fail.
• The srr_stats_pre_submit() function confirms this package may be submitted.
• The pkgcheck() function confirms this package may be submitted - alternatively, please explain reasons for any checks which your package is unable to pass.

This package:

• does not violate the Terms of Service of any service it interacts with.
• has a CRAN and OSI accepted license.
• contains a README with instructions for installing the development version.

• Do you intend for this package to go on CRAN?
• Do you intend for this package to go on Bioconductor?

• I agree to abide by rOpenSci's Code of Conduct during the review process and in maintaining my package should it be accepted.

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