Package: TieFreeCensor 0.1.0

TieFreeCensor: Algorithm for Generating Tie-Free Progressive Type-II Censored Samples

Generates tie-free progressive Type-II censored samples from discrete distributions and user-specified discrete probability mass functions (PMF) or cumulative distribution functions (CDF). Provides maximum likelihood estimation (MLE), Bayesian estimation via Markov chain Monte Carlo (MCMC) Metropolis-within-Gibbs sampling, likelihood-based parametric bootstrap goodness-of-fit (GOF) tests, profile log-likelihood diagnostics, and discrete survival and probability calculations. Methods are based on Ahmad and Mansour (2026) <doi:10.1155/jom/3657078>, Balakrishnan and Dembinska (2008) <doi:10.1016/j.jspi.2007.02.006>, Joe and Zhu (2005) <doi:10.1002/bimj.200410102>, and Balakrishnan and Aggarwala (2000, ISBN:978-1-4612-1334-5).

Authors:Shikhar Tyagi [aut, cre], Arvind Pandey [aut], Bhupendra Singh [aut], Vrijesh Tripathi [aut]

TieFreeCensor_0.1.0.tar.gz
TieFreeCensor_0.1.0.zip(r-4.7-any)TieFreeCensor_0.1.0.zip(r-4.6-any)TieFreeCensor_0.1.0.zip(r-4.5-any)
TieFreeCensor_0.1.0.tgz(r-4.6-any)TieFreeCensor_0.1.0.tgz(r-4.5-any)
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TieFreeCensor_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
TieFreeCensor/json (API)

# Install 'TieFreeCensor' in R:

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

Datasets:

covid_deaths - COVID-19 Daily Death Counts Dataset
spinal_tumors - Spinal Tumors Count Dataset in Neurofibromatosis Type 2 (NF2) Patients

On CRAN:

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

1.00 score 206 downloads 9 exports 0 dependencies

Last updated from:7d1ff536b0. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	125
source / vignettes	OK	159
linux-release	OK	140
macos-release	OK	77
macos-oldrel	OK	105
windows-devel	OK	65
windows-release	OK	70
windows-oldrel	OK	87
wasm-release	OK	90

Exports:fit_gpd_bayes fit_gpd_mle generate_tiefree_p2c gof_p2c_bootstrap gpd_cdf gpd_pmf gpd_random plot_profile_loglik prob_gpd

Dependencies:

Help page	Topics
Coef Method for GPD MLE	coef.gpd_mle
COVID-19 Daily Death Counts Dataset	covid_deaths
Bayesian Estimation for GPD Under Progressive Type-II Censoring	fit_gpd_bayes
Maximum Likelihood Estimation for GPD Under Progressive Type-II Censoring	fit_gpd_mle
Generate Tie-Free Progressive Type-II Censored Samples	generate_tiefree_p2c
Likelihood-Based Parametric Bootstrap Goodness-of-Fit Test	gof_p2c_bootstrap
Generalized Poisson Distribution Cumulative Distribution Function	gpd_cdf
Generalized Poisson Distribution (GPD) Probability Mass Function	gpd_pmf
Random Generation for Generalized Poisson Distribution	gpd_random
LogLik Method for GPD MLE	logLik.gpd_mle
Plot Profile Log-Likelihood Curves for GPD Parameters	plot_profile_loglik
Plot Method for GPD Bayesian MCMC Diagnostics	plot.gpd_bayes
Print Method for GOF Bootstrap Test Result	print.gof_bootstrap
Print Method for GPD Bayesian MCMC Estimation	print.gpd_bayes
Print Method for GPD MLE	print.gpd_mle
Print Method for Tie-Free Progressive Type-II Sample	print.tiefree_p2c
Compute Discrete Probabilities and Event Metrics for GPD	prob_gpd
Spinal Tumors Count Dataset in Neurofibromatosis Type 2 (NF2) Patients	spinal_tumors
Summary Method for GPD Bayesian MCMC Estimation	summary.gpd_bayes
Summary Method for GPD MLE	summary.gpd_mle
Vcov Method for GPD MLE	vcov.gpd_mle

Package: TieFreeCensor 0.1.0

TieFreeCensor: Algorithm for Generating Tie-Free Progressive Type-II Censored Samples

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Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)