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MleCensoR
Maximum Likelihood Estimation under Censoring Schemes
View on CRAN: Click
here
Download and install MleCensoR package within the R console
Install from CRAN:
install.packages("MleCensoR")
Install from Github:
library("remotes")
install_github("cran/MleCensoR") Install by package version:
library("remotes")
install_version("MleCensoR", "0.1.0") Attach the package and use:
library("MleCensoR")
Maintained by
Shikhar Tyagi
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[Scholar Profile | Author Map]
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First Published: 2026-07-23
Latest Update: 2026-07-23
Description:
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).
How to cite:
Shikhar Tyagi (2026). MleCensoR: Maximum Likelihood Estimation under Censoring Schemes. R package version 0.1.0, https://cran.r-project.org/web/packages/MleCensoR. Accessed 25 Oct. 2026.
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