R Observatory: Metrics for PoolTestR version 0.2.0

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

PoolTestR

0.2.0Package record

Prevalence and Regression for Pool-Tested (Group-Tested) Data

0packages depend

3.4Kdownloads / year

–test coverage

0/13flavors OK

Overview

About

Maintained by Angus McLureCRAN page ↗GitHub ↗Issues ↗

An easy-to-use tool for working with presence/absence tests on 'pooled' or 'grouped' samples. The primary application is for estimating prevalence of a marker in a population based on the results of tests on pooled specimens. This sampling method is often employed in surveillance of rare conditions in humans or animals (e.g. molecular xenomonitoring). The package was initially conceived as an R-based alternative to the molecular xenomonitoring software, 'PoolScreen' https://sites.uab.edu/statgenetics/software/. However, it goes further, allowing for estimates of prevalence to be adjusted for hierarchical sampling frames, and perform flexible mixed-effect regression analyses (McLure et al. Environmental Modelling and Software. DOI:10.1016/j.envsoft.2021.105158). The package is currently in early stages, however more features are planned or in the works: e.g. adjustments for imperfect test specificity/sensitivity, functions for helping with optimal experimental design, and functions for spatial modelling.

Changes in 0.2.0

Caitlin Cherryh has joined the development team and has been working on

improving readability of outputs, documentation, and testing.

This update includes an option for PoolPrev() to skip the calculation of

Bayesian estimates. When using bayesian = FALSE, only MLE and likelihood ratio

confidence intervals will be calculated, substantially speeding up this function

(perhaps x100).

This updates also removes one source of bias from prevalence estimates returned

for any hierarchical models. This effects the results of HierPoolPrev() and

getPrevalence() applied to models with random effects. Under the update,

prevalence estimates will typically slightly increase, though the difference

will not be notable if the sample size is large and there is little clustering.

Previous estimates of prevalence did not marginalise out the random effects when

Show all changes in 0.2.0Hide the rest of the changes in 0.2.0

calculating population-level prevalence, but as of this version, random effects

are marginalised out. Due to the complexity introduced by this bias-correction

we now longer-support specifying nested surveys using ~(1|Layer1/Layer2) and

recommend using the format ~(1|Layer1) + (1|Layer2) which should be equivalent

as long as each level in Layer2 is unique --- i.e. the format already required

for HierPoolPrev().

Due to the complexity introduced by this bias-correction, the way of specifying

priors for HierPoolPrev() has been updated. Priors for HierPoolPrev() are

now directly on the real-scale (logit-transformed) parameters, rather than

prevalence directly. We have also updated the default priors for PoolRegBayes()

for regression parameters, as we believe the previous priors were too diffuse

(normal(0,100)). The defaults for the centered predictors are now

student(6,0,1.5).

HierPoolPrev() now has functionality to return estimate of intracluster

correlation coefficients (ICC) at one or more levels of clustering.

HierPoolPrev() and PoolPrev() now have custom output classes (inheriting

from tibble, the previous class for these outputs). This has allowed us

(Caitlin) to add pretty-print functions these outputs which are much more human

readable. Saving the output with write.csv() or similar will still return a

detailed, machine-readable output.

Both PoolPrev() and HierPoolPrev() have been updated to improve point

estimates. New default function values have been added for both PoolPrev() and

HierPoolPrev(). The default for the new robust parameter is robust = TRUE,

which means the point estimate of prevalence is the posterior median. In both

functions, the default value for all.negative.pools = 'zero', meaning when all

pools are negative, the point estimate and the. lower bound for the interval

will be 0.

From NEWS.md in the release

Install

R (CRAN)pak (dev)Ubuntu (r2u)Fedora (COPR)openSUSE (OBS)

install.packages("PoolTestR", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

if (!requireNamespace("pak", quietly = TRUE))
  install.packages("pak", repos = "https://cloud.r-project.org/")
pak::pak("AngusMcLure/PoolTestR")

Development version from AngusMcLure/PoolTestR. Installs pak first if it is not already available.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-pooltestr

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled. We list this channel from recorded r2u download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-PoolTestR

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-PoolTestR

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed). We list this channel from recorded autoCRAN download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

Health

CRAN checks

13NOTE

Check messages

NOTEDESCRIPTION meta-informationr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64

r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64

Missing dependency on R >= 4.1.0 because package code uses the pipe
  |> or function shorthand \(...) syntax added in R 4.1.0.
  File(s) using such syntax:
    ‘getPrevalence.R’

r-devel-windows-x86_64, r-oldrel-windows-x86_64, r-release-windows-x86_64

Missing dependency on R >= 4.1.0 because package code uses the pipe
  |> or function shorthand \(...) syntax added in R 4.1.0.
  File(s) using such syntax:
    'getPrevalence.R'

NOTEdependencies in R coder-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc

Namespaces in Imports field not imported from:
  ‘RcppParallel’ ‘rstantools’
All declared Imports should be used.

CRAN check page ↗

Slowest check: 14.6 min · r-oldrel-windows-x86_64

Status changes

NOTE13 NOTE2026-09-09
ERROR12 NOTE, 1 ERROR2026-09-04
NOTE13 NOTE2026-08-06
ERROR12 NOTE, 1 ERROR2026-07-16
NOTE13 NOTE2026-07-08

Show 6 earlier changesHide 6 earlier changes

ERROR12 NOTE, 1 ERROR2026-07-07
NOTE13 NOTE2026-06-09
ERROR12 NOTE, 1 ERROR2026-06-08
NOTE14 NOTE2026-04-22
ERROR13 NOTE, 1 ERROR2026-04-18
NOTE14 NOTE2026-03-10

Advisories

none known

How this is measured·How advisories are found

Documentation

In release 0.2.0

READMEYes · 615 wordsVignettesNoNEWSYes · 100% structured · mentions 0.2.0Citation fileYes · cites a publicationWebsitenot declared in DESCRIPTION

Help pages: 10 (1 internal)includes a package overview page
Examples that run: 83%5 run in a plain check · 1 only when donttest examples are enabled (R CMD check --as-cran)
Exported functions with examples: 100%
Documented parameters: 91%
Return-value docs: 100%
Help pages citing references: 20%

How these are measured

Downloads

3.4K

CRAN downloads in the past year

Rank #12,198 · ~9/day · ~280/mo

Releases R versions

Daily download trend is not available in this view yet.

32930 days

91290 days

3.4K1 year

Compare downloads with other packages →

Also on367 r2u3 autocran

How downloads are counted

Repository

angusmclure/pooltestr ↗Account: AngusMcLure

7Stars

3Forks

13Open issues

0Open PRs

4Releases

272Commits

4Commit identities

272 commits · Last activity 2025-11-25 · 0% stars, 30d · Default branch at 0.2.1, ahead of CRAN 0.2.0 (checked 2026-10-21)

Responsiveness

How these are sampled

PRs merged (all time): 91%
Median days to close, 50 most recently updated closed issues: 34
Median days to close, 50 most recently updated closed PRs: 0

Stars over time

2025-05-02 · 62026-07-07 · 7

How these are measured

PracticesBeta

10 development-tooling and community-health practices detected across 7 families in the upstream repository

Checks run against github.com/angusmclure/pooltestr on 2026-10-21.

Continuous integration (3)

GitHub ActionsR CMD check workflowTravis CI (config file)

The R CMD check workflow runs on Linux, macOS and Windows, and on R-devel

Docs source (1)

pkgdown

Coverage (1)

Coverage reporting (Codecov)

Reproducibility and dev environment (1)

data-raw/

Show all practicesHide 3 practices

CRAN release process (2)

cran-comments.mdCRAN-SUBMISSION

Lint, format, editor (1)

RStudio project

Git structural (1)

.gitattributes

How this is detected·Detection ruleset v4 (2026-10-20)

Dependencies

Declared dependencies

18 external dependencies (excludes base and recommended)

Installing PoolTestR adds 82 packages (39 with compiled code, 83 MB of source tarballs). Heaviest: BH 14 MB, stringi 11 MB, ggplot2 6.0 MB.

Depends (1)

R >= 3.4.0

Imports (13)

brms dplyr lme4methodsprogress Rcpp (>= 0.12.0)RcppParallel rlang rstan rstantoolsstatsstringr tibble

LinkingTo (6)

BH Rcpp RcppEigen RcppParallel rstan StanHeaders (>= 2.26.0)

Suggests (1)

covr

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

How these are counted

Code & Tests

Datasets

People & History

People (2)

Angus McLure

Author, Maintainer since 0.1.0 (2021)

Caitlin Cherryh

Contributor · added in 0.2.0

Releases (5)

Pick two releases to compare their code metrics.

R
R 4.6.0 released · 2026-04-24
R
R 4.5.0 released · 2025-04-11
0.2.0Latest
record
2024-12-05 · current release · diff ↗
R
R 4.4.0 released · 2024-04-24
R
R 4.3.0 released · 2023-04-21
0.1.3
record
2022-07-01 · diff ↗
R
R 4.2.0 released · 2022-04-22
0.1.2
record
2022-01-19 · diff ↗
R
R 4.1.0 released · 2021-05-18
0.1.1
record
2021-02-15 · diff ↗
0.1.0
record
2021-02-08
R
R 4.0.0 released · 2020-04-24

Package metadata

First published: 2021-02-08
Total releases: 5 / 5 yrs
License: GPL (>= 3) OSI
Minimum R: ≥ 3.4.0
Bundled data: 18 KB / 3 files
Download size: 80 KB

Cite

Cite this package

Run in R for the authors' entry:

citation("PoolTestR")

The package provides a citation file, so citation() returns the authors' own entry.

It points to 10.1016/j.envsoft.2021.105158.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

Format

APA

Balamuta, J. J. (2026). R Observatory: Metrics for PoolTestR version 0.2.0 [Data set]. HJJB, LLC. Data release v2026-10-24. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-24, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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