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
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:
maskedhaz_0.1.0.tar.gz
maskedhaz_0.1.0.zip(r-4.7-any)maskedhaz_0.1.0.zip(r-4.6-any)maskedhaz_0.1.0.zip(r-4.5-any)
maskedhaz_0.1.0.tgz(r-4.6-any)maskedhaz_0.1.0.tgz(r-4.5-any)
maskedhaz_0.1.0.tar.gz(r-4.7-any)maskedhaz_0.1.0.tar.gz(r-4.6-any)
maskedhaz_0.1.0.tgz(r-4.6-emscripten)
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
censored-datahazard-functionlikelihood-functionsmasked-datamlereliabilityseries-systemssurvival-analysis
Last updated from:bc6ef67863. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 140 | ||
| source / vignettes | OK | 247 | ||
| linux-release-x86_64 | OK | 131 | ||
| macos-release-arm64 | OK | 94 | ||
| macos-oldrel-arm64 | OK | 89 | ||
| windows-devel-x86_64 | OK | 78 | ||
| windows-release-x86_64 | OK | 86 | ||
| windows-oldrel-x86_64 | OK | 105 | ||
| wasm-release | OK | 111 |
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
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
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
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
Last update: 2026-04-14
Started: 2026-04-14
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Assumptions for masked-cause DFR series systems | assumptions.dfr_series_md |
| Marginal cause-of-failure probability for DFR series systems | cause_probability.dfr_series_md |
| Component hazard for a masked-cause DFR series system | component_hazard.dfr_series_md |
| Conditional cause-of-failure probability for DFR series systems | conditional_cause_probability.dfr_series_md |
| Masked-Cause Likelihood Model for DFR Series Systems | dfr_series_md |
| MLE fitting for masked-cause DFR series systems | fit.dfr_series_md |
| Hessian of log-likelihood for masked-cause DFR series systems | hess_loglik.dfr_series_md |
| Test whether an object is a dfr_series_md | is_dfr_series_md |
| Log-likelihood for masked-cause DFR series systems | loglik.dfr_series_md |
| Number of components in a masked-cause DFR series system | ncomponents.dfr_series_md |
| Print method for dfr_series_md | print.dfr_series_md |
| Random data generation for masked-cause DFR series systems | rdata.dfr_series_md |
| Score function for masked-cause DFR series systems | score.dfr_series_md |
