Package: rinet 0.1.1

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>.

Authors:Jack LeBien [aut, cre]

rinet_0.1.1.tar.gz
rinet_0.1.1.zip(r-4.7)rinet_0.1.1.zip(r-4.6)rinet_0.1.1.zip(r-4.5)
rinet_0.1.1.tgz(r-4.6-any)rinet_0.1.1.tgz(r-4.5-any)
rinet_0.1.1.tar.gz(r-4.7-any)rinet_0.1.1.tar.gz(r-4.6-any)
rinet_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
rinet/json (API)

# Install 'rinet' in R:
install.packages('rinet', repos = c('https://jackgle.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.30 score 3 scripts 159 downloads 3 exports 12 dependencies

Last updated from:560bed5b9c. Checks:6 OK, 3 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK704
source / vignettesOK256
linux-release-x86_64OK130
macos-release-arm64FAIL61
macos-oldrel-arm64FAIL143
windows-develOK112
windows-releaseFAIL50
windows-oldrelOK104
wasm-releaseOK100

Exports:predict_rinetpredict_rinet_1dpredict_rinet_2d

Dependencies:herejsonlitelatticeMatrixpngrappdirsRcppRcppTOMLreticulaterlangrprojrootwithr