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Clinical Reference Interval Estimation with Reference Interval Network (RINet)

install.packages("rinet")
CRAN 30-day downloads, method
249+13.7% up on the 30 days before, the better direction

Predicts statistics of a reference distribution from a mixture of raw clinical measurements (healthy and pathological). Uses pretrained CNN models to estimate the mean, standard deviation, and reference fraction from 1D or 2D sample data.

Full description

Predicts statistics of a reference distribution from a mixture of raw clinical measurements (healthy and pathological). Uses pretrained CNN models to estimate the mean, standard deviation, and reference fraction from 1D or 2D sample data. Methods are described in LeBien, Velev, and Roche-Lima (2026) "RINet: synthetic data training for indirect estimation of clinical reference distributions" <doi:10.1016/j.jbi.2026.104980>.

Maintainer
Jack LeBien
Latest release
20 Oct 2026
First release
29 Jan 2026

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More details
Releases
3
Needs compilation
No
Needs R
>= 3.5.0
Config/roxygen2/version
8.1.1
Subjects (1) Method, Subjects
Cite

Package

Run in R for the authors' preferred citation:

citation("rinet")
APA
Jack LeBien. (2026). rinet: Clinical Reference Interval Estimation with Reference Interval
Network (RINet) (Version 0.2.0) [Computer software]. https://doi.org/10.32614/CRAN.package.rinet
About

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

R Observatory

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

APA
Balamuta, J. J. (2026). R Observatory: Metrics for rinet version 0.2.0 [Data set]. HJJB, LLC. Data release v2026-10-27. https://doi.org/10.5281/zenodo.21843040
About

From data release v2026-10-27, which the citation names so these numbers can be found later.

All 13 check platforms pass.

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  • 13 OK

Over time

No change since 5 Aug 2026

As a list (6)
ChangedWorst statusOKNotesWarningsErrors
5 Aug 2026OK13000
1 Aug 2026Note12100
9 Jun 2026OK13000
8 Jun 2026Warning12010
25 Apr 2026OK12000
10 Mar 2026Note11300

Since March 2026

Check results on CRAN

Total downloads in the last 30 days
254+5.4% up on the 30 days before, the better directionIt is the sum of 3 channels: CRAN, r2u and autoCRAN.

CRAN rank

17,722 of 26,529

CRAN

249 downloads in the last 30 days

Rank
17,722 of 26,529
Change
+13.7% up on the 30 days before, the better direction

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r2u

4 downloads in the last 30 days

Rank
10,337 of 30,526
Change
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1 downloads in the last 30 days

Rank
25,650 of 29,212
Change
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Over time

Compare downloads

Needs
2
Dependent packages
0
Suggested by
0
Needs (2)
Installing it adds
24 packages, 10.6 MB of source
Of those, need compilation
12
Functions
8
Public exports
3
Lines of R code
723
Median complexity
5
Functions (8)
Size and composition

Lines by kind (1,068)

KindLinesShare
R code72368%
Documentation34532%

Lines by language

LanguageLinesShare
R72368%
Rd34532%

Languages are counted from file extensions.

Files
22
R functions
8
Complexity
Median
5
Mean
6.5
Highest
15
Median lines per function
33.5
About complexity

Cyclomatic complexity counts the independent paths through a function: 1 is a straight line, higher is harder to test. R functions only.

Call graph figures
R functions
8
Calls
10
Unconnected
1
Groups
1
Growth (3 releases)
Total lines by release
1,131 on 29 Jan 20261,068 on 20 Oct 2026
Release changes

The package was built and has no tests to run.

Lines of tests
0
Exports named in tests
0%
Test suite
Has tests
No
Test frameworks
No tests in this release
Test lines per line of code
0
Exports documented
100%
Arguments documented
100%
Return value documented
100%
Help pages with examples
37.5%
Examples run in checks
0%
Other documentation
README
No
Release notes file
Yes
Help pages
8
Lines of documentation
345
  • Jack LeBien, an organisation

    Maintainer since 0.1.0 (2026), author

jackgle / rinet_r_package on github.com

Stars
1
Forks
0
Open issues
0
Last commit
20 Oct 2026
Activity
Commits
22
Open pull requests
0
Tagged releases
0
Latest tagged release
–
Issues closed in
–
Pull requests closed in
–
Pull requests merged
–
Created
2 Jan 2026
Licence file reads as
–
Development practices (2)
KindFound
Reproducibility and dev environmentdata-raw/
CRAN release processcran-comments.md

Method, repository practices

Across jackgle

Claude Codeat least 8 crediting itClaude CodeFirst seen 18 Sep 2026at least 8 crediting it
Claude Sonnet 5.5Claude Sonnet 5.520 Oct 20262 commits
Claude Opus 5.5Claude Opus 5.520 Oct 20266 commits

Tools

ToolFirst seenFoundCommits crediting it
Claude Code18 Sep 2026Named in .gitignore.claude8 at least
  • Named in .gitignore

Models

ModelCommitsFirst commitLast commitCredit lines of
Claude Sonnet 5.5220 Oct 202620 Oct 2026Claude Code
Claude Opus 5.5620 Oct 202620 Oct 2026Claude Code

Across jackgle

R releases
  • On CRAN
  • Release
VersionReleasedR
0.2.0 current20 Oct 20264.6.1
0.1.123 Feb 20264.5.2
0.1.029 Jan 20264.5.2
Release notes (0.2.0)

Backend: torch replaces Python/TensorFlow

  • The package no longer requires Python, TensorFlow, Keras, or scikit-learn. Models now run through the torch R package. After installing rinet, run torch::install_torch() once to download the backend.
  • This is an infrastructure change only: predictions from the v2.0 model are identical to rinet 0.1.x up to floating-point precision.

New default 1D model (v2.1)

  • The default 1D model is now v2.1. It has the same inputs and outputs as v2.0 but was retrained on revised simulated data: pathological component shifts and widths are drawn relative to the reference component (instead of in absolute units with clamping), and the training set is five times larger. This gives more even coverage of mixtures in which the pathological component is far from, or narrower than, the reference.
  • The model described in the published paper is still available as model_version = "v2.0" in predict_rinet() and predict_rinet_1d().
  • Each prediction now includes a model_version element.
  • The 2D model is unchanged (v2.0).
  • The updated simulation and training code is available at <https://github.com/jackgle/RINet>.

Evaluation of v2.1 against v2.0

  • RIbench benchmark (Ammer et al., 2022; 5,760 simulated test sets, 10 analytes; absolute z-score deviation, lower is better): overall score improved from 0.395 to 0.356.
  • Largest gains at high pathological fractions, e.g. normally distributed analytes improved from 0.523 to 0.379.
  • Some loss at low pathological fractions for skewed analytes: skewed 0.364 to 0.371, heavily skewed 0.249 to 0.267.
  • Liver function test dataset (10 analytes x 2 sexes, normalized error against the direct method on reference-labelled patients): mean error 0.061 (v2.0) vs 0.067 (v2.1), within the variation expected for 20 cases.