% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/censoring.R
\name{censoring_mtl}
\alias{censoring_mtl}
\alias{rcensor_mtl}
\alias{mle_censor_mtl}
\title{Censoring Schemes and Estimation for the Modified Topp-Leone Distribution}
\usage{
rcensor_mtl(
  n,
  alpha,
  scheme = c("random", "type1", "type2", "progressive2"),
  T_censor = NULL,
  m = NULL,
  R = NULL,
  censor_rate = 0.5
)

mle_censor_mtl(
  x,
  status = NULL,
  scheme = c("random", "type1", "type2", "progressive2"),
  R = NULL,
  conf.level = 0.95
)
}
\arguments{
\item{n}{Total initial sample size.}

\item{alpha}{Shape parameter of the MTL distribution (\code{alpha > 0}).}

\item{scheme}{Type of censoring scheme: \code{"random"}, \code{"type1"}, \code{"type2"}, or \code{"progressive2"}.}

\item{T_censor}{Fixed censoring time for Type-I censoring (\code{T_censor > 0}).}

\item{m}{Number of observed failures for Type-II or Progressive Type-II censoring (\code{1 <= m <= n}).}

\item{R}{Vector of removal counts for Progressive Type-II censoring of length \code{m} such that \code{sum(R) == n - m}.}

\item{censor_rate}{Rate parameter for exponential censoring distribution in random censoring.}

\item{x}{Vector of observed failure/censoring times.}

\item{status}{Vector of censoring indicators (1 = observed failure, 0 = censored).}

\item{conf.level}{Confidence level for interval estimation (default: 0.95).}
}
\value{
\code{rcensor_mtl} returns a list containing observed times (\code{x}), censoring status (\code{status}),
censoring scheme info, and parameter settings.
\code{mle_censor_mtl} returns a list containing point estimate (\code{estimate}), standard error (\code{se}),
asymptotic confidence interval (\code{ci}), and log-likelihood value under censoring.
}
\description{
Functions to generate random samples and estimate parameters of the Modified Topp-Leone (MTL)
distribution under various censoring schemes: Random Right Censoring, Type-I Censoring,
Type-II Censoring, and Progressive Type-II Censoring.
}
\examples{
set.seed(123)
# 1. Random Right Censoring
rand_cens <- rcensor_mtl(n = 30, alpha = 2.0, scheme = "random", censor_rate = 0.5)
mle_censor_mtl(x = rand_cens$x, status = rand_cens$status, scheme = "random")

# 2. Type-I Censoring
t1_cens <- rcensor_mtl(n = 30, alpha = 2.0, scheme = "type1", T_censor = 1.5)
mle_censor_mtl(x = t1_cens$x, status = t1_cens$status, scheme = "type1")

# 3. Type-II Censoring
t2_cens <- rcensor_mtl(n = 30, alpha = 2.0, scheme = "type2", m = 20)
mle_censor_mtl(x = t2_cens$x, status = t2_cens$status, scheme = "type2")

# 4. Progressive Type-II Censoring
R_scheme <- c(2, 0, 1, 0, 2, 0, 1, 0, 2, 2)
prog_cens <- rcensor_mtl(n = 20, alpha = 2.0, scheme = "progressive2", m = 10, R = R_scheme)
mle_censor_mtl(x = prog_cens$x, scheme = "progressive2", R = R_scheme)

}
\references{
Singh, B., Tyagi, S., Singh, R. P., and Tyagi, A. (2025). Modified Topp-Leone
Distribution: Properties, Classical and Bayesian Estimation with Application to COVID-19
and Reliability Data. \emph{Thailand Statistician}, 23(1), 72-96.

Balakrishnan, N., and Aggarwala, R. (2000). \emph{Progressive Censoring: Theory, Methods, and Applications}.
Birkhäuser, Boston.
}
\author{
Shikhar Tyagi, Abhishek Tyagi, Arvind Pandey, Bhupendra Singh, Vrijesh Tripathi
}
