Package: MleCensoR 0.1.0

Shikhar Tyagi

MleCensoR: Maximum Likelihood Estimation under Censoring Schemes

Provides generalized functions to compute Maximum Likelihood Estimation (MLE) for any univariate distribution under various censoring and truncation schemes. Users supply the probability density function (PDF), cumulative distribution function (CDF), survival function, support bounds, and initial parameter values; the package constructs and maximizes the appropriate log-likelihood automatically. 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. Optimization methods include Newton-Raphson (NR), Broyden-Fletcher-Goldfarb-Shanno (BFGS), the BFGS algorithm implemented in R (BFGSR), Berndt-Hall-Hall-Hausman (BHHH), Simulated Annealing (SANN), Conjugate Gradients (CG), and Nelder-Mead (NM). Inference summaries provide the Akaike Information Criterion (AIC), estimated coefficients, log-likelihood, iteration count, standard errors, z-values, p-values, and the variance-covariance matrix. 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>, Kundu and Joarder (2006) <doi:10.1016/j.csda.2005.05.002>, Berndt, Hall, Hall, and Hausman (1974) "Estimation and Inference in Nonlinear Structural Models" <doi:10.3386/t0003>, Fletcher (1987, "Practical Methods of Optimization", ISBN:978-0-471-91547-8), Nelder and Mead (1965) <doi:10.1093/comjnl/7.4.308>, McKinnon (1999) "Convergence of the Nelder-Mead simplex method to a non-stationary point" <doi:10.1137/S1052623496303482>, Kirkpatrick, Gelatt, and Vecchi (1983) <doi:10.1126/science.220.4598.671>, Fletcher and Reeves (1964) <doi:10.1093/comjnl/7.2.149>, and Nocedal and Wright (2006, "Numerical Optimization", ISBN:978-0-387-30303-1).

Authors:Shikhar Tyagi [aut, cre], Vrijesh Tripathi [aut]

MleCensoR_0.1.0.tar.gz
MleCensoR_0.1.0.zip(r-4.7-any)
MleCensoR_0.1.0.tar.gz(r-4.7-any)MleCensoR_0.1.0.tar.gz(r-4.6-any)
MleCensoR_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION
card.svg |card.png
MleCensoR/json (API)

# Install 'MleCensoR' in R:
install.packages('MleCensoR', 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.

1.70 score 23 exports 0 dependencies

Last updated from:0b9e7482a9. Checks:5 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK113
source / vignettesOK193
linux-release-x86_64OK112
windows-devel-x86_64OK69
wasm-releaseOK91

Exports:mle_bjpt2mle_block_randommle_doubly_type2mle_hybridmle_hybrid_type1mle_hybrid_type2mle_intervalmle_joint_type1mle_joint_type2mle_leftmle_left_truncationmle_middlemle_progressive_first_failuremle_progressive_hybrid_type2mle_progressive_type2mle_randommle_rightmle_right_truncationmle_type1mle_type1_hybridmle_type2nIterstdEr

Dependencies: