Package: PINNProgCens 0.1.0

PINNProgCens: Physics-Informed Neural Networks for Progressive Censoring

Implementation of Physics-Informed Neural Networks ('PINN') for lifetime estimation under progressive Type-II censoring schemes. Combines parametric baseline hazards with physical differential degradation models.

Authors:Okechukwu J. Obulezi [aut, cre]

PINNProgCens_0.1.0.tar.gz
PINNProgCens_0.1.0.zip(r-4.7-any)PINNProgCens_0.1.0.zip(r-4.6-any)PINNProgCens_0.1.0.zip(r-4.5-any)
PINNProgCens_0.1.0.tgz(r-4.6-any)PINNProgCens_0.1.0.tgz(r-4.5-any)
PINNProgCens_0.1.0.tar.gz(r-4.7-any)PINNProgCens_0.1.0.tar.gz(r-4.6-any)
PINNProgCens_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
PINNProgCens/json (API)

# Install 'PINNProgCens' in R:
install.packages('PINNProgCens', repos = c('https://obulezi-oj.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.

1.00 score 190 downloads 8 exports 1 dependencies

Last updated from:bbd5d7cf4e. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK88
source / vignettesOK150
linux-releaseOK84
macos-releaseOK92
macos-oldrelOK79
windows-develOK64
windows-releaseOK58
windows-oldrelOK71
wasm-releaseOK86

Exports:compute_ode_residualsdefault_ode_systemfit_pinn_progcens_mleinterpolate_statepinn_hazardpinn_progcens_losssoftplussolve_ode_trajectory

Dependencies:deSolve