Package 'pcsclr'

Fit PCS-CLR Parameter Estimation Models

Description

Fit PCS-CLR Parameter Estimation Models

Usage

fit_pcsclr(
  time,
  delta,
  removal,
  method = c("RK4", "Bayes_Kernel"),
  control = list(),
  is_hybrid = FALSE,
  t_max = 3,
  r_star = 0,
  ...
)

Arguments

time	Vector of observed lifetime intervals.
delta	Attrition risk indicator matrix.
removal	Active dynamic progressive censorship counts.
method	Selection between "RK4" paths or "Bayes_Kernel" sampler loops.
control	Optional configuration list parameters.
is_hybrid	Boolean indicator for hybrid censoring regimes.
t_max	Truncation time milestone boundary constraint.
r_star	Escaped survival components unfailed beyond cutoff point.
...	Additional structural arguments passed to internal optimization engines.

Value

An object of S3 class "pcsclr_fit" consisting of a list with the following elements:

estimates	A numeric vector containing the estimated Weibull shape ($\alpha$) and scale ($\beta$) parameters.
method	A character string indicating the algorithm used ("RK4" or "Bayes_Kernel").
iterations	Number of iterations to reach convergence (returned only when method = "RK4").
chains	A list of posterior MCMC samples for alpha and beta parameters (returned only when method = "Bayes_Kernel").

Examples

# 1. Generate small sample mock variables
set.seed(123)
n <- 30
mock_time <- rweibull(n, shape = 1.5, scale = 2.0)
mock_delta <- sample(c(0, 1), n, replace = TRUE, prob = c(0.2, 0.8))
mock_removal <- rbinom(n, 5, 0.1)

# 2. Fit using 4th-order Runge-Kutta Optimization
res_rk4 <- fit_pcsclr(time = mock_time, delta = mock_delta,
                      removal = mock_removal, method = "RK4")
print(res_rk4$estimates)

# 3. Fit using the C++ Bayesian MCMC Sampler
res_bayes <- fit_pcsclr(time = mock_time, delta = mock_delta,
                         removal = mock_removal, method = "Bayes_Kernel",
                         control = list(M = 1000, burn_in = 200))
print(res_bayes$estimates)

---

Simulate Datasets Under PCS-CLR Configurations

Description

Simulate Datasets Under PCS-CLR Configurations

Usage

sim_pcsclr(
  n,
  target,
  alpha = 1.8,
  beta = 2.5,
  p = 0.15,
  scheme = c("baseline", "hybrid"),
  cs_layout = c("CS_I", "CS_II"),
  T_max = 3
)

Arguments

n	Total initial sample population count.
target	Scheduled tracking truncation parameter target threshold.
alpha	Shape vector coefficient.
beta	Scale distribution tracker.
p	Attrition or competitive latent risk probability.
scheme	Setting options: "baseline" or "hybrid".
cs_layout	Distribution setups: "CS_I" or "CS_II".
T_max	Explicit global temporal limitation window boundary.

Value

An object of S3 class "pcsclr_data" consisting of a list with the following elements:

T_obs	A numeric vector of observed failure times up to the target or truncation cutoff.
delta	A numeric vector of risk indicators corresponding to each observed time.
R_vector	A numeric vector of progressive censoring removal counts at each failure point.
scheme	A character string indicating the active censoring scheme ("baseline" or "hybrid").
is_truncated	A logical flag indicating whether time truncation (T_max) occurred during simulation.
T_max	The temporal limit applied (returned only when is_truncated = TRUE).
R_star	The count of remaining survival components unfailed beyond the truncation cutoff (returned only when is_truncated = TRUE).

---