Package: pcsclr 0.1.1

pcsclr: Progressive Censoring Schemes with Competitive Latent-Risk

Implements simulation, numerical maximum likelihood estimation via fourth-order Runge-Kutta path optimization, and high-speed Bayesian Markov Chain Monte Carlo (MCMC) samplers for Weibull lifetimes under progressive censoring setups with competitive latent risks. Both point estimation and interval estimation are provided for the model parameters.

Authors:Okechukwu J. Obulezi [aut, cre]

pcsclr_0.1.1.tar.gz
pcsclr_0.1.1.zip(r-4.7-x86_64)pcsclr_0.1.1.zip(r-4.7-arm64)pcsclr_0.1.1.zip(r-4.6-x86_64)pcsclr_0.1.1.zip(r-4.6-arm64)pcsclr_0.1.1.zip(r-4.5-x86_64)
pcsclr_0.1.1.tgz(r-4.6-x86_64)pcsclr_0.1.1.tgz(r-4.6-arm64)pcsclr_0.1.1.tgz(r-4.5-x86_64)pcsclr_0.1.1.tgz(r-4.5-arm64)
pcsclr_0.1.1.tar.gz(r-4.7-arm64)pcsclr_0.1.1.tar.gz(r-4.7-x86_64)pcsclr_0.1.1.tar.gz(r-4.6-arm64)pcsclr_0.1.1.tar.gz(r-4.6-x86_64)
pcsclr_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
pcsclr/json (API)

# Install 'pcsclr' in R:

install.packages('pcsclr', repos = c('https://obulezi-oj.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

c++– GNU Standard C++ Library v3

On CRAN:

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

cpp

1.70 score 198 downloads 2 exports 2 dependencies

Last updated from:742ce4912c. Checks:15 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	121
linux-devel-x86_64	OK	102
source / vignettes	OK	177
linux-release-arm64	OK	72
linux-release-x86_64	OK	97
macos-release-arm64	OK	114
macos-release-x86_64	OK	206
macos-oldrel-arm64	OK	138
macos-oldrel-x86_64	OK	341
windows-devel-arm64	OK	312
windows-devel-x86_64	OK	132
windows-release-arm64	OK	318
windows-release-x86_64	OK	82
windows-oldrel-x86_64	OK	69
wasm-release	OK	106