R Observatory: Metrics for thinr version 0.3.0

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

thinr

0.3.0Package record

Binary Image Thinning Algorithms

0packages depend

1.5Kdownloads / year

100.0%test coverage

13/13flavors OK

Overview

About

Maintained by Bill DenneyCRAN page ↗GitHub ↗Website ↗Issues ↗

Thinning (skeletonization) algorithms for binary raster images. Provides seven algorithms behind a single dispatching function: Zhang-Suen (Zhang and Suen 1984) doi:10.1145/357994.358023, Guo-Hall (Guo and Hall 1989) doi:10.1145/62065.62074, a 2-D adaptation of Lee (Lee, Kashyap, and Chu 1994) doi:10.1006/cgip.1994.1042, K3M (Saeed, Tabedzki, Rybnik, and Adamski 2010) doi:10.2478/v10006-010-0024-4, the parallel form commonly attributed to Hilditch (1969, in 'Machine Intelligence 4'), OPTA / SPTA (Naccache and Shinghal 1984), and Holt and colleagues (1987) doi:10.1145/12527.12531. Also provides the medial axis transform (Blum 1967) and a distance transform implementation following Felzenszwalb and Huttenlocher (2012) doi:10.4086/toc.2012.v008a019. The thin() API selects the algorithm by name, defaulting to Zhang-Suen. Complements the morphology in the 'EBImage' package, which does not provide a thinning operator.

Changes in 0.3.0

Correctness fixes from the figureextract ecosystem review (2026-07-03).

All are boundary conditions the published algorithms assume away; see the

new vignette("correctness-properties") for the guarantees they restore.

thin() now skeletonises shapes that touch the matrix border correctly. The kernels inspect an 8-neighbourhood and so never deleted pixels in the outermost row or column, leaving edge-touching shapes two to three pixels thick; thin() now pads the image with a one-pixel background margin and crops it back, so a shape thins identically whether or not it touches the frame. Applies to all seven methods (#F012).
thin(method = "opta") now keeps two-pixel-wide strokes connected. The SPTA kernel evaluated all four contour directions against the pre-cycle snapshot and deleted them in one batch, so both sides of a 2px stroke were removed together and the skeleton fragmented (a 2×9 bar collapsed to its four corner pixels). Deletion is now sequential per direction, as Naccache & Shinghal's two-scan formulation requires (#F011).

Show all changes in 0.3.0Hide the rest of the changes in 0.3.0

distance_transform(metric = "manhattan" | "chessboard") now returns Inf for a fully-foreground image (no background pixel exists), matching the "euclidean" metric and EBImage::distmap(). The two-pass metrics previously seeded foreground with a finite sentinel that leaked out as a plausible-looking but meaningless finite distance (#F015).
thin(), distance_transform(), and medial_axis() now reject NA input with a clear error at the coercion boundary instead of silently turning it into NA_integer_ (INT_MIN) in the C++ kernels (#F016).
thin(method = "hilditch") now thins junctions to the published parallel form. The look-ahead conditions 3 and 4 skip deleting a pixel only when a cardinal neighbour has crossing number A == 1 on the current image; the kernel had compared the look-ahead crossing number (computed with the centre already removed) against 1, which is strictly stronger and also spared junction neighbours where A >= 2, leaving a redundant pixel beside the junction. Skeletons are now equal to or thinner than before, never thicker (verified against a reference implementation of the published form over random images) (#F014).
Added a connectivity-preservation property test across all seven methods and tightened the Holt straight-line test (#F013).
Documented and pinned the isolated-2×2-block behaviour: the default zhang_suen erases an isolated 2×2 block entirely while guo_hall keeps one pixel. See vignette("choosing-a-method") for guidance on small-blob masks (#F017).
Removed thinImage(). Use thin() (Zhang-Suen is the default method).

From NEWS.md in the release

Install

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

install.packages("thinr", 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("humanpred/thinr")

Development version from humanpred/thinr. 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-thinr

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-thinr

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-thinr

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

13OK

CRAN check page ↗

Slowest check: 3.4 min · r-devel-windows-x86_64

Status changes

OK13 OK2026-08-04
NOTE12 OK, 1 NOTE2026-08-01
OK7 OK2026-05-28

Advisories

none known

How this is measured·How advisories are found

Documentation

In release 0.3.0

READMEYes · 520 wordsVignettesYes · 2 · dynamicNEWSYes · 100% structured · mentions 0.3.0Citation fileNoWebsiteYes · pkgdown

Help pages: 4 (1 internal)includes a package overview page
Examples that run: 100%3 run in a plain check
Exported functions with examples: 100%
Documented parameters: 100%
Return-value docs: 100%
Help pages citing references: 50%

Vignettes (2)

How these are measured

Downloads

1.5K

CRAN downloads in the past year

Rank #17,474 · ~4/day · ~124/mo

Releases R versions

Daily download trend is not available in this view yet.

20630 days

73790 days

1.5K1 year

Compare downloads with other packages →

Also on83 r2u2 autocran

How downloads are counted

Repository

humanpred/thinr ↗Organization: humanpred

0Stars

0Forks

0Open issues

0Open PRs

1Releases

61Commits

1Commit identities

61 commits · Last activity 2026-08-22 · Default branch at 0.3.0.9000, ahead of CRAN 0.3.0 (checked 2026-10-21)

Responsiveness

How these are sampled

PRs merged (all time): 93%
Median days to close, 50 most recently updated closed issues: 0
Median days to close, 50 most recently updated closed PRs: 0

Releases over time

2026-07-07 · 02026-07-26 · 1

How these are measured

PracticesBeta

8 development-tooling and community-health practices detected across 4 families in the upstream repository

Checks run against github.com/humanpred/thinr on 2026-10-21.

Continuous integration (2)

GitHub ActionsR CMD check workflow

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

Docs source (3)

pkgdown

GitHub Pages site last deployed 2026-08-22 · The repository has a site deploy workflow

pkgdown site last built 2026-08-22

Coverage (1)

Coverage reporting

Lint, format, editor (2)

lintrLint workflow

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

AI coding toolsBeta

This repository shows signs of AI coding tools.

Claude Codefirst seen on or before 2026-05-16
Seen in the repository: CLAUDE.md
Named in .Rbuildignore: CLAUDE.md
Named in .gitignore: .claude
Commits: at least 43 crediting it in the message

Models named in its commits

Claude Opus 4.819 commits · Jun to Jul 2026
Claude Opus 4.715 commits · May 2026
Claude Fable 57 commits · Jul 2026
Claude Opus 52 commits · Jul to Aug 2026

Counts are commits, not lines of code. Commits by a tool's accounts count once across all of its accounts. Commits crediting it are the largest count among its credit lines, a minimum.How this is detected·Detection ruleset v4 (updated 2026-09-27)

Dependencies

Declared dependencies

9 external dependencies (excludes base and recommended)

Installing thinr adds 1 package (1 with compiled code, 1.8 MB of source tarballs). Heaviest: Rcpp 1.8 MB.

Depends (1)

R >= 4.2

Imports (1)

Rcpp

LinkingTo (1)

Rcpp

Suggests (7)

bench covr knitr lintr rmarkdown styler testthat (>= 3.0.0)

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

How these are counted

Code & Tests

People & History

People (1)

Bill Denney

Author, Maintainer since 0.2.0 (2026)

Human Predictions, LLC

Releases (2)

Pick two releases to compare their code metrics.

0.3.0Latest
record
2026-07-26 · current release · diff ↗ ·
removed 1 export
thinImage
0.2.0
record
2026-05-27
R
R 4.6.0 released · 2026-04-24

Package metadata

First published: 2026-05-27
Total releases: 2 / 1 yrs
License: LGPL-3 OSI
Minimum R: ≥ 4.2
Download size: 62 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("thinr")

Format

Denney, B. (2026). thinr: Binary Image Thinning Algorithms (Version 0.3.0) [Computer software]. https://doi.org/10.32614/CRAN.package.thinr

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

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 thinr version 0.3.0 [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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