rinet: Clinical Reference Interval Estimation with Reference Interval Network (RINet)

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>.

Version:	0.1.0
Depends:	R (≥ 3.5.0)
Imports:	reticulate
Published:	2026-01-29
DOI:	10.32614/CRAN.package.rinet (may not be active yet)
Author:	Jack LeBien [aut, cre]
Maintainer:	Jack LeBien <jackgl4124 at gmail.com>
License:	MIT + file LICENSE
NeedsCompilation:	no
SystemRequirements:	Python (>= 3.8), TensorFlow (>= 2.16), Keras (>= 3.0), scikit-learn
CRAN checks:	rinet results

Documentation:

Reference manual:

rinet.html , rinet.pdf

Downloads:

Package source:	rinet_0.1.0.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available

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