Package {DRLAP2}

Type: Package
Title: Dynamic Reinforcement Learning and Adaptive Progressive Censoring
Version: 0.1.1
Description: 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++.
License: GPL (≥ 3)
Encoding: UTF-8
Imports: Rcpp, stats
LinkingTo: Rcpp
NeedsCompilation: yes
Config/roxygen2/version: 8.1.0
Packaged: 2026-08-20 18:23:38 UTC; Dr. O. J. Obulezi
Author: Okechukwu J. Obulezi [aut, cre]
Maintainer: Okechukwu J. Obulezi <oj.obulezi@unizik.edu.ng>
Repository: CRAN
Date/Publication: 2026-08-25 15:10:16 UTC

DRLAP2: Dynamic Reinforcement Learning and Adaptive Progressive Censoring

Description

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++.

Details

Statistical inference for progressive censoring models.

Author(s)

Maintainer: Okechukwu J. Obulezi oj.obulezi@unizik.edu.ng

Authors:

Calculate Reward Signal

Description

Calculate Reward Signal

Usage

calc_reward(t_i, r_i, lambda = 0.5)

Arguments

t_i

Observed failure time

r_i

Chosen removal action

lambda

Cost penalty scaling factor

Value

A numeric scalar representing the computed net reward signal (information gain minus action removal cost).

Compute DRL Sums (C++)

Description

Computes internal sum terms S0, S1, and S2 used in DRL parameter estimation.

Usage

compute_drl_sums_cpp(t, R, beta)

Arguments

t

Numeric vector of positive time or survival values.

R

Numeric vector of response or censoring counts.

beta

Double specifying the beta shape parameter.

Value

A named list containing S0, S1, and S2.

Examples

t_vals <- c(1.2, 2.5, 3.1)
R_vals <- c(0, 1, 0)
compute_drl_sums_cpp(t = t_vals, R = R_vals, beta = 1.5)

Compute Log Target Density for Beta (C++)

Description

Evaluates the conditional log-target distribution for the beta parameter in DRL models.

Usage

drl_log_target_beta_cpp(beta, theta, t, R, a1, b1)

Arguments

beta

Double specifying the current beta parameter (must be positive).

theta

Double specifying the current theta scale parameter.

t

Numeric vector of positive time values.

R

Numeric vector of response counts.

a1

Double specifying shape hyperparameter a1.

b1

Double specifying rate hyperparameter b1.

Value

Double representing the evaluated log-target value.

Examples

t_vals <- c(1.2, 2.5, 3.1)
R_vals <- c(0, 1, 0)
drl_log_target_beta_cpp(beta = 1.5, theta = 0.8, t = t_vals, R = R_vals, a1 = 1.0, b1 = 1.0)

Bayesian MCMC Sampler for DRL-AP2 Weibull Model

Description

Bayesian MCMC Sampler for DRL-AP2 Weibull Model

Usage

fit_drl_bayes(
  t,
  R,
  priors = list(a1 = 0.1, b1 = 0.1, a2 = 0.1, b2 = 0.1),
  N_MC = 10000,
  N_burn = 2000,
  proposal_sd = 0.05
)

Arguments

t

Numeric vector of failure times

R

Numeric vector of removal actions

priors

List containing prior hyperparameters a1, b1, a2, b2

N_MC

Total MCMC draws

N_burn

Burn-in draws to discard

proposal_sd

Standard deviation for Gaussian candidate proposal on beta

Value

An object of S3 class "drl_bayes", which is a list containing:

bayes_estimates

A named numeric vector of posterior mean point estimates for parameters alpha and beta.

hpd_alpha

A named numeric vector of length 2 giving the 95% equal-tailed credible interval limits for alpha.

hpd_beta

A named numeric vector of length 2 giving the 95% equal-tailed credible interval limits for beta.

chains

A data.frame containing post-burn-in MCMC draws for alpha and beta.

Maximum Likelihood Estimation for DRL-AP2 Weibull Model (C++ Accelerated)

Description

Maximum Likelihood Estimation for DRL-AP2 Weibull Model (C++ Accelerated)

Usage

fit_drl_mle(t, R, alpha = 0.05, max_iter = 100, tol = 1e-07)

Arguments

t

Numeric vector of failure times

R

Numeric vector of progressive removal actions

alpha

Significance level (default = 0.05)

max_iter

Maximum Newton-Raphson iterations

tol

Convergence threshold

Value

A list containing point estimates and asymptotic confidence intervals

Initialize MDP State Observer for Dynamic Censoring

Description

Initialize MDP State Observer for Dynamic Censoring

Usage

init_drl_env(n, m, budget)

Arguments

n

Total initial units at start of experiment

m

Required total number of failure observations

budget

Initial operational budget

Value

An object of class DRLState