Package: UniCensor 0.1.0

Shikhar Tyagi

UniCensor: Reproducible Random Samples Under Univariate Censoring Schemes

Generates reproducible random samples from any user-specified univariate distribution under a comprehensive suite of censoring and truncation schemes. Users supply the probability density function (PDF), cumulative distribution function (CDF), survival function, support bounds, and parameters; the same seed and inputs yield identical samples across sessions. Supported schemes include right and left truncation, random, right, left, interval, and middle censoring, block random censoring, balanced joint progressive Type-II (BJPT-II), progressive first failure, joint Type-I, Type-I, Type-II, progressive Type-II, Type-II progressively hybrid, joint Type-II, hybrid, hybrid Type-I, doubly Type-II, Type-I hybrid, and hybrid Type-II censoring. Diagnostic histogram, dot plot, and autocorrelation plots are provided for each scheme to verify distributional behaviour. Methods are described in Nagar, Kumar, and Krishna (2026) <doi:10.59467/IJASS.2026.22.1>, Goel, Kumar, and Krishna (2026, "Estimation in power Lindley distributions using balanced joint progressively Type-II censored data"), Wu and Kus (2009) <doi:10.1016/j.csda.2009.03.010>, Goel and Krishna (2026) <doi:10.1007/s13198-026-03208-w>, Balakrishnan and Aggarwala (2000, ISBN:978-1-4612-1334-5), Mondal and Kundu (2020) <doi:10.1080/03610926.2018.1554128>, Ding and Gui (2023) <doi:10.3390/math11092003>, Prajapati, Mitra, and Kundu (2019) <doi:10.1007/s13571-018-0167-0>, Yadav, Jaiswal, and Yadav (2026) <doi:10.1007/s11135-026-02647-8>, Iyer, Jammalamadaka, and Kundu (2008) <doi:10.1016/j.jspi.2007.03.062>, Banerjee and Kundu (2008) <doi:10.1109/TR.2008.916890>, and Kundu and Joarder (2006) <doi:10.1016/j.csda.2005.05.002>.

Authors:Shikhar Tyagi [aut, cre]

UniCensor_0.1.0.tar.gz
UniCensor_0.1.0.tar.gz(r-4.7-any)UniCensor_0.1.0.tar.gz(r-4.6-any)
UniCensor_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
UniCensor/json (API)

# Install 'UniCensor' in R:
install.packages('UniCensor', repos = c('https://cran.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 27 exports 0 dependencies

Last updated from:5703a3f536. Checks:4 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK112
source / vignettesOK157
linux-release-x86_64OK110
wasm-releaseOK93

Exports:dist_specplot_censor_acfplot_censor_diagnosticsplot_censor_dotplot_censor_histr_bjpt2_censorr_block_random_censorr_completer_doubly_type2_censorr_hybrid_censorr_hybrid_type1_censorr_hybrid_type2_censorr_interval_censorr_joint_type1_censorr_joint_type2_censorr_left_censorr_left_truncationr_middle_censorr_progressive_first_failurer_progressive_hybrid_type2_censorr_progressive_type2_censorr_random_censorr_right_censorr_right_truncationr_type1_censorr_type1_hybrid_censorr_type2_censor

Dependencies:

Readme and manuals

Help Manual

Help pageTopics
Coerce censored sample to data frameas.data.frame.censored_sample
Extract observed values from a censored sampleas.vector.censored_sample
Define a univariate distribution for simulationdist_spec
Autocorrelation function plotplot_censor_acf
Combined diagnostic panel: histogram, dot plot, and ACFplot_censor_diagnostics
Dot plot (index plot) of sample valuesplot_censor_dot
Histogram of observed values with true density overlayplot_censor_hist
Print method for censored_sampleprint.censored_sample
Print method for dist_specprint.dist_spec
Balanced joint progressive Type-II (BJPT-II) censoringr_bjpt2_censor
Block random censoringr_block_random_censor
Generate a complete (uncensored) random sampler_complete
Doubly Type-II censoringr_doubly_type2_censor
Hybrid censoring (Type-I and Type-II constraints)r_hybrid_censor
Hybrid Type-I censoring with progressive withdrawalr_hybrid_type1_censor
Hybrid Type-II censoringr_hybrid_type2_censor
Interval censoringr_interval_censor
Joint Type-I censoring across multiple samplesr_joint_type1_censor
Joint Type-II censoring across multiple samplesr_joint_type2_censor
Left censoring at a fixed thresholdr_left_censor
Left-truncated random sampler_left_truncation
Middle censoringr_middle_censor
Progressive first-failure censoringr_progressive_first_failure
Type-II progressively hybrid censoringr_progressive_hybrid_type2_censor
Progressive Type-II censoringr_progressive_type2_censor
Random censoringr_random_censor
Right censoring at a fixed timer_right_censor
Right-truncated random sampler_right_truncation
Type-I (time-terminated) censoringr_type1_censor
Type-I hybrid censoringr_type1_hybrid_censor
Type-II (failure-terminated) censoringr_type2_censor