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maskedhaz

Masked-Cause Likelihood Models for DFR Series Systems

maskedhaz provides likelihood-based inference for series systems with masked component cause of failure. A series system fails when any component fails, but the causing component may be unknown (masked). Given candidate sets satisfying conditions C1-C2-C3, this package computes log-likelihood, score, Hessian, and MLE for the component parameters.

Unlike closed-form implementations, this package works with arbitrary component hazard functions from flexhaz — Weibull, Gompertz, log-logistic, or any custom dfr_dist.

Installation

Install from r-universe:

install.packages("maskedhaz", repos = "https://queelius.r-universe.dev")

Quick Start

library(maskedhaz)

# Three-component exponential series system
model <- dfr_series_md(components = list(
    dfr_exponential(0.1),
    dfr_exponential(0.2),
    dfr_exponential(0.3)
))

# Generate masked data with right-censoring
rdata_fn <- rdata(model)
set.seed(42)
df <- rdata_fn(theta = c(0.1, 0.2, 0.3), n = 500, tau = 10, p = 0.5)

# Evaluate log-likelihood
ll_fn <- loglik(model)
ll_fn(df, par = c(0.1, 0.2, 0.3))

# Fit via MLE
solver <- fit(model)
result <- solver(df, par = c(0.5, 0.5, 0.5))
coef(result)    # parameter estimates
vcov(result)    # variance-covariance matrix
confint(result) # confidence intervals

Mixed Component Types

The package supports arbitrary dfr_dist component distributions:

# Weibull wear-out + exponential random failure
model <- dfr_series_md(components = list(
    dfr_weibull(shape = 2, scale = 100),
    dfr_exponential(0.05)
))

rdata_fn <- rdata(model)
set.seed(42)
df <- rdata_fn(theta = c(2, 100, 0.05), n = 300, tau = Inf, p = 0.3)

solver <- fit(model)
result <- solver(df, par = c(1.5, 120, 0.03))
coef(result)

Observation Types

The data frame uses an omega column to indicate observation type:

Candidate sets are Boolean columns x1, x2, ..., xm.

Key Features

• General hazards: Works with any dfr_dist component — no closed-form assumptions
• Full censoring support: Exact, right, left, and interval censoring
• Masked cause: Candidate sets satisfying C1-C2-C3 masking conditions
• MLE fitting: fit() returns fisher_mle with coef(), vcov(), confint()
• Data generation: rdata() simulates masked series system data
• Diagnostics: Conditional and marginal cause-of-failure probabilities
• Cross-validated: Matches closed-form results from maskedcauses

Ecosystem

maskedhaz builds on:

• serieshaz — Series system distributions
• flexhaz — Dynamic failure rate distributions
• likelihood.model — Likelihood model interface
• algebraic.dist — Distribution generics

Cross-validated against:

• maskedcauses — Closed-form exponential/Weibull series likelihoods

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.