Relaxed Candidate Set Models for Masked Data in Series Systems

Overview

This R package implements likelihood-based inference for series systems with masked failure data when traditional conditions are relaxed. It extends the standard C1-C2-C3 framework by allowing:

Informative masking (relaxed C2): Candidate set probabilities can depend on which component failed
Parameter-dependent masking (relaxed C3): Masking mechanism can depend on system parameters
Flexible candidate set models: Bernoulli, rank-based, and KL-divergence constrained models

Installation

# Install from GitHub
remotes::install_github("queelius/mdrelax")

Key Features

Maximum likelihood estimation for exponential and Weibull series systems
Fisher information matrix computation for efficiency analysis
Informative masking models (rank-based, KL-constrained)
Identifiability analysis tools
Simulation utilities for Monte Carlo studies

Quick Start

library(mdrelax)

# Generate masked data with Bernoulli candidate sets
md <- md_bernoulli_cand_C1_C2_C3(data, p = 0.3)

# Sample candidate sets
md <- md_cand_sampler(md)

# Compute MLE for exponential series system
fit <- md_mle_exp_series_C1_C2_C3(md)

# Get Fisher information matrix
fim <- md_fim_exp_series_C1_C2_C3(md, params(fit))

Background

In series systems with masked failure data: - The system fails when any component fails - The failed component is not directly observed - A candidate set of possible failed components is reported

Traditional analysis assumes: - C1: Failed component is always in the candidate set - C2: Non-informative masking (uniform probability within candidate set) - C3: Masking independent of system parameters

This package provides tools for inference when C2 and/or C3 are violated.

Documentation

See the package website for full documentation.

wei.series.md.c1.c2.c3 - Original implementation for Weibull series systems under C1-C2-C3

License

GPL (>= 3)

mdrelax