Package: CompRiskRel 0.1.0

CompRiskRel: Reliability and Competing Risks Analysis under Hybrid Censoring

Generalized computational algorithms for competing risks analysis, stress-strength reliability modeling, optimal designs, and reliability acceptance sampling plans under various hybrid censoring schemes. Includes data generation routines, Maximum Likelihood Estimation (MLE) with seven optimization algorithms ('Newton-Raphson', 'BFGS', 'BFGSR', 'BHHH', 'SANN', 'CG', and 'Nelder-Mead'), Bayesian inference via Gibbs sampling and Metropolis-Hastings MCMC, Importance Sampling, and 'Lindley' asymptotic approximation. Visualization functions generate histograms, dot plots, and autocorrelation plots for model validation. Methodology and design principles are based on 'Balakrishnan', 'Cramer', and 'Kundu' (2023, "Hybrid Censoring Know-How: Designs and Implementations", Academic Press, ISBN:978-0-12-398387-9).

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

CompRiskRel_0.1.0.tar.gz
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CompRiskRel_0.1.0.tgz(r-4.6-any)CompRiskRel_0.1.0.tgz(r-4.5-any)
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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
CompRiskRel/json (API)

# Install 'CompRiskRel' in R:
install.packages('CompRiskRel', repos = c('https://shikhartyagi.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 247 downloads 19 exports 0 dependencies

Last updated from:9c0f4beec1. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK91
source / vignettesOK158
linux-releaseOK90
macos-releaseOK96
macos-oldrelOK84
windows-develOK56
windows-releaseOK70
windows-oldrelOK79
wasm-releaseOK66

Exports:bayes_gibbs_censoredbayes_mh_censoredbayesian_sampling_plan_expgen_competing_risksgen_gen_prog_hybridgen_stress_strengthgen_type1_hybridgen_type2_hybridimportance_sampling_censoredlindley_approx_censoredmle_competing_risksmle_gen_prog_hybridmle_type1_hybridmle_type2_hybridoptimal_design_hybridplot_diagnosticssampling_plan_exponentialsampling_plan_weibullstress_strength_rel

Dependencies:

Readme and manuals

Help Manual

Help pageTopics
Bayesian Inference via Gibbs Sampling for Censored Data Modelsbayes_gibbs_censored
Bayesian Inference via Metropolis-Hastings MCMC Algorithmbayes_mh_censored
Bayesian Reliability Acceptance Sampling Plan for Exponential lifetimes (10.5.3)bayesian_sampling_plan_exp
Generate Data for Competing Risks Analysisgen_competing_risks
Generate Data under Generalized Progressive Hybrid Censoring Schemegen_gen_prog_hybrid
Generate Data for Stress-Strength Reliability Modelsgen_stress_strength
Generate Data under Type-I Hybrid Censoring Schemegen_type1_hybrid
Generate Data under Type-II Hybrid Censoring Schemegen_type2_hybrid
Importance Sampling for Posterior Estimationimportance_sampling_censored
Lindley Asymptotic Approximation for Posterior Expectationslindley_approx_censored
Maximum Likelihood Estimation for Competing Risks Analysismle_competing_risks
Maximum Likelihood Estimation under Generalized Progressive Hybrid Censoringmle_gen_prog_hybrid
Maximum Likelihood Estimation under Type-I Hybrid Censoringmle_type1_hybrid
Maximum Likelihood Estimation under Type-II Hybrid Censoringmle_type2_hybrid
Optimal Design Selection for Hybrid Censoring Schemesoptimal_design_hybrid
Diagnostic Plots for Censored Data Schemesplot_diagnostics
Plot Method for Objects of Class comp_risk_rel_dataplot.comp_risk_rel_data
Print Method for Bayesian Fit Objectsprint.bayes_fit
Print Method for MLE Fit Objectsprint.mle_fit
Print Method for Sampling Plan Objectsprint.sampling_plan
Reliability Acceptance Sampling Plan for Exponential Distribution (10.5.1)sampling_plan_exponential
Reliability Acceptance Sampling Plan for Weibull Distribution (10.5.2)sampling_plan_weibull
Stress-Strength Reliability Estimation R = P(X < Y)stress_strength_rel
Summary Method for Bayesian Fit Objectssummary.bayes_fit
Summary Method for MLE Fit Objectssummary.mle_fit