Package: maskedhaz 0.1.0

maskedhaz: Masked-Cause Likelihood Models for Series Systems with Arbitrary Hazard Components

Likelihood-based inference for series systems with masked component cause of failure, using arbitrary dynamic failure rate component distributions. Computes log-likelihood, score, Hessian, and maximum likelihood estimates for masked data satisfying conditions C1, C2, C3 under general component hazard functions. Implements the 'series_md' protocol defined in the 'maskedcauses' package.

Authors:Alexander Towell [aut, cre]

maskedhaz_0.1.0.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
maskedhaz/json (API)

# Install 'maskedhaz' in R:
install.packages('maskedhaz', repos = c('https://queelius.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/queelius/maskedhaz/issues

Pkgdown/docs site:https://queelius.github.io

On CRAN:

Conda:

censored-datahazard-functionlikelihood-functionsmasked-datamlereliabilityseries-systemssurvival-analysis

4.60 score 6 scripts 328 downloads 26 exports 13 dependencies

Last updated from:bc6ef67863. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK164
source / vignettesOK209
linux-releaseOK102
macos-releaseOK116
macos-oldrelOK187
windows-develOK83
windows-releaseOK61
windows-oldrelOK94
wasm-releaseOK94

Exports:assumptionscause_probabilitycomponentcomponent_hazardconditional_cause_probabilitycum_hazdfr_dist_seriesdfr_exponentialdfr_gompertzdfr_loglogisticdfr_series_mddfr_weibullfithazardhess_loglikis_dfr_dist_seriesis_dfr_series_mdloglikncomponentsparam_layoutparamsrdatasample_componentssamplerscoresurv

Dependencies:algebraic.distalgebraic.mlebootdist.structureflexhazgenericslikelihood.modelmaskedcausesMASSmvtnormnumDerivR6serieshaz

Censoring Types and Masked Causes
Two Independent Sources of Information Loss | The omega Column | Contribution Formulas | Worked Examples | Exact and right-censored | Left-censored | Interval-censored | Mixed observation types | Cross-Validation Against a Reference Implementation | Identifiability Under Masking | Summary
Rendered fromcensoring-and-masking.Rmdusingknitr::rmarkdown

Last update: 2026-04-14
Started: 2026-04-14

Hypothesis Testing on Fitted Models
Why a Separate Vignette? | The Scenario | Question 1: Is the Exponential Family Adequate? | Question 2: Is Each Individual Shape Significantly Different from 1? | Question 3: Controlling Family-Wise Error | Question 4: Composite Hypotheses | Question 5: A Score Test Without Refitting | Question 6: Confidence Intervals by Test Inversion | Cross-Implementation Check | Summary
Rendered fromhypothesis-tests.Rmdusingknitr::rmarkdown

Last update: 2026-04-14
Started: 2026-04-14

maskedhaz: Masked-Cause Likelihood for General Series Systems
The Problem | The series_md Protocol and This Implementation | Quick Tour | Step 1: generate data | Step 2: evaluate the log-likelihood | Step 3: fit via MLE | Step 4: the fit is a distribution | Step 5: diagnostics | The Ecosystem | Protocol stack (model classes) | Component stack (what goes inside a series system) | MLE result stack | Where to Go Next | Assumptions
Rendered frommaskedhaz.Rmdusingknitr::rmarkdown

Last update: 2026-04-14
Started: 2026-04-14

Mixed-Distribution Series Systems
The Motivation | A Worked Example: Medical Device with Four Failure Modes | Simulating Field Data | Fitting | Why Identifiability Works for Mixed Families | Defining Your Own Component | Summary
Rendered fromcustom-components.Rmdusingknitr::rmarkdown

Last update: 2026-04-14
Started: 2026-04-14