DRLAP2: Dynamic Reinforcement Learning and Adaptive Progressive Censoring

Implements Maximum Likelihood Estimation (MLE) and Bayesian Markov Chain Monte Carlo (MCMC) sampling algorithms for progressive censoring models, with support for dynamic reinforcement learning environment simulation and accelerated computational routines written in C++.

Version: 0.1.1
Imports: Rcpp, stats
LinkingTo: Rcpp
Published: 2026-08-25
DOI: 10.32614/CRAN.package.DRLAP2 (may not be active yet)
Author: Okechukwu J. Obulezi [aut, cre]
Maintainer: Okechukwu J. Obulezi <oj.obulezi at unizik.edu.ng>
License: GPL (≥ 3)
NeedsCompilation: yes
CRAN checks: DRLAP2 results

Documentation:

Reference manual: DRLAP2.html , DRLAP2.pdf

Downloads:

Package source: DRLAP2_0.1.1.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available

Linking:

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