{
  "_id": "6a12c648acfb0bcc41d1ff0b",
  "Package": "kofn",
  "Title": "Maximum Likelihood Estimation for k-out-of-n System Data",
  "Version": "0.4.0",
  "Authors@R": "person(\"Alexander\", \"Towell\",\nemail = \"lex@metafunctor.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-6443-9897\"))",
  "Description": "Maximum likelihood estimation of component lifetime\nparameters from system-level observations of k-out-of-n\nsystems. Supports exponential and Weibull component\ndistributions under multiple observation schemes: Scheme 0\n(system lifetime only), Scheme 1 (periodic inspection), and\nScheme 2 (complete monitoring). Provides an EM algorithm for\nWeibull parallel systems and Fisher information comparison\nacross schemes. The k-out-of-n framework unifies series (k=1)\nand parallel (k=m) systems as a censoring problem on component\nlifetimes. Conforms to the 'likelihood.model' generics and\nreturns fitted objects compatible with 'algebraic.mle'. The\ndata-generating process and topology infrastructure (system\nsurvival, density, signature, structure function, importance\nmeasures) are delegated to the 'dist.structure' package; kofn\nfocuses exclusively on inference for the k-out-of-n family.",
  "License": "MIT + file LICENSE",
  "URL": "https://github.com/queelius/kofn, https://queelius.github.io/kofn/",
  "BugReports": "https://github.com/queelius/kofn/issues",
  "Config/testthat/edition": "3",
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  "Roxygen": "list(markdown = TRUE)",
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  "VignetteBuilder": "knitr",
  "Repository": "https://queelius.r-universe.dev",
  "Date/Publication": "2026-05-23 06:18:38 UTC",
  "RemoteUrl": "https://github.com/queelius/kofn",
  "RemoteRef": "HEAD",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-24 09:31:25 UTC",
    "User": "root"
  },
  "Author": "Alexander Towell [aut, cre] (ORCID:\n<https://orcid.org/0000-0001-6443-9897>)",
  "Maintainer": "Alexander Towell <lex@metafunctor.com>",
  "MD5sum": "f3569702de5041e744440fb7805dff3d",
  "_user": "queelius",
  "_type": "src",
  "_file": "kofn_0.4.0.tar.gz",
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  "_created": "2026-05-24T09:31:25.000Z",
  "_published": "2026-05-24T09:35:04.246Z",
  "_distro": "noble",
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  "_upstream": "https://github.com/queelius/kofn",
  "_commit": {
    "id": "a84d8f62f572011075fbc534c79b900743485941",
    "author": "Alex Towell <lex@metafunctor.com>",
    "committer": "Alex Towell <lex@metafunctor.com>",
    "message": "kofn 0.4.0: CRAN-ready cleanup (vignette runtime, S3 signature, re-exports)\n\nVignette runtime now ~5 min total (was ~25 min):\n* weibull-em.Rmd: 4 unconditional fits drop from n=300 -> n=50\n* periodic-inspection.Rmd: drop n=300 -> n=100 for two-component\n  fits, n=150 -> n=50 for the four-component k-spectrum loop, and\n  n_starts=3/2 -> 1 on all unconditional chunks.\n* observation-schemes.Rmd: scheme-comparison loop drops R=5 to R=3\n  and n=200 to n=60, plus n_starts=3 -> 1.\n* exponential-parallel.Rmd: n=100 -> n=50 on unconditional demos,\n  Monte Carlo R=5 -> 3, n_starts=5/3 -> 1.\n* ecosystem.Rmd: n=300 -> n=100 for both fits, n_starts=3 -> 1.\n* dist-structure-integration.Rmd: n=500 -> n=100, n_starts=3 -> 1.\n* getting-started.Rmd: n=300/200 -> n=100, n_starts=3 -> 1.\n* The gated run_long chunks keep n=300 and n_starts=3 for the\n  scientific results; only the always-run demo chunks shrank.\n\nR CMD check fixes:\n* R/kofn.R: rename `ncomponents.kofn(model, ...)` -> `(x, ...)` to\n  match the generic signature in dist.structure. Resolves the S3\n  generic/method consistency WARNING.\n* R/reexports.R: re-export `dfr_exponential` and `dfr_weibull` from\n  flexhaz, and `ncomponents` from dist.structure. Users (and vignettes\n  and examples) can now call these unqualified after library(kofn).\n* R/scheme1.R: add @importFrom stats density to loglik_scheme1 so\n  the bare density() call inside the loglik closure resolves cleanly.\n  Eliminates the \"no visible global function\" NOTE.\n* DESCRIPTION: pin flexhaz (>= 0.5.2), which introduces the\n  family-specific S3 subclass on prototype constructors that\n  kofn_subclass() relies on. flexhaz 0.5.2 is currently in CRAN review.\n* .Rbuildignore: add _pkgdown.yml, cran-comments.md, CRAN-SUBMISSION,\n  .papermill.md. Resolves the \"non-standard file at top level\" NOTE.\n\nStatus: 0 errors, 0 warnings, 1 NOTE (the benign \"unable to verify\ncurrent time\" timestamp). Tests pass; on CRAN the stress tests\n(test-stress.R, test-stress2.R) skip via skip_on_",
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    "login": "queelius",
    "orcid": "0000-0001-6443-9897",
    "twitter": "@queelius",
    "description": "Alexander Towell (Alex Towell). Reliability theory, encrypted search, algebraic structures. Building personal data tools.",
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    "compare_fisher_info",
    "dfr_exponential",
    "dfr_weibull",
    "F_sys_exp",
    "fit",
    "fit_scheme1",
    "hess_loglik",
    "ie_expand",
    "is_kofn",
    "kofn",
    "loglik",
    "loglik_masked",
    "loglik_scheme1",
    "ncomponents",
    "observe_exact",
    "observe_interval_censor",
    "observe_left_censor",
    "observe_mixture",
    "observe_periodic",
    "observe_right_censor",
    "rdata",
    "rdata_masked",
    "rdata_scheme1",
    "S_sys_exp",
    "score",
    "solve_mle",
    "trunc_log_moment_vec",
    "trunc_pow_moment",
    "trunc_pow_moment_vec",
    "w_j_exact",
    "w_j_integral"
  ],
  "_help": [
    {
      "page": "assumptions.exp_kofn",
      "title": "Assumptions for exponential k-out-of-n model",
      "topics": [
        "assumptions.exp_kofn"
      ]
    },
    {
      "page": "assumptions.wei_kofn",
      "title": "Assumptions for Weibull k-out-of-n system model",
      "topics": [
        "assumptions.wei_kofn"
      ]
    },
    {
      "page": "compare_fisher_info",
      "title": "Compare Fisher information across observation schemes",
      "topics": [
        "compare_fisher_info"
      ]
    },
    {
      "page": "F_sys_exp",
      "title": "System CDF for exponential parallel systems",
      "topics": [
        "F_sys_exp"
      ]
    },
    {
      "page": "fit_scheme1",
      "title": "Fit k-out-of-n system MLE under Scheme 1 (periodic inspection)",
      "topics": [
        "fit_scheme1"
      ]
    },
    {
      "page": "fit.exp_kofn",
      "title": "MLE fitting for exponential k-out-of-n model",
      "topics": [
        "fit.exp_kofn"
      ]
    },
    {
      "page": "fit.wei_kofn",
      "title": "Fit Weibull k-out-of-n system model",
      "topics": [
        "fit.wei_kofn"
      ]
    },
    {
      "page": "hess_loglik.exp_kofn",
      "title": "Hessian of the log-likelihood for exponential k-out-of-n model",
      "topics": [
        "hess_loglik.exp_kofn"
      ]
    },
    {
      "page": "hess_loglik.wei_kofn",
      "title": "Hessian of log-likelihood for Weibull k-out-of-n system",
      "topics": [
        "hess_loglik.wei_kofn"
      ]
    },
    {
      "page": "ie_expand",
      "title": "Inclusion-exclusion expansion of a product of CDFs",
      "topics": [
        "ie_expand"
      ]
    },
    {
      "page": "is_kofn",
      "title": "Test if an object is a kofn model",
      "topics": [
        "is_kofn"
      ]
    },
    {
      "page": "kofn",
      "title": "Create a k-out-of-n system estimation model",
      "topics": [
        "kofn"
      ]
    },
    {
      "page": "loglik_masked",
      "title": "Masked k-out-of-n log-likelihood",
      "topics": [
        "loglik_masked"
      ]
    },
    {
      "page": "loglik_scheme1",
      "title": "Log-likelihood for Scheme 1 (periodic inspection) parallel system",
      "topics": [
        "loglik_scheme1"
      ]
    },
    {
      "page": "loglik.exp_kofn",
      "title": "Log-likelihood for exponential k-out-of-n model",
      "topics": [
        "loglik.exp_kofn"
      ]
    },
    {
      "page": "loglik.wei_kofn",
      "title": "Log-likelihood for Weibull k-out-of-n system",
      "topics": [
        "loglik.wei_kofn"
      ]
    },
    {
      "page": "ncomponents.kofn",
      "title": "Number of components in a kofn model",
      "topics": [
        "ncomponents.kofn"
      ]
    },
    {
      "page": "observe_exact",
      "title": "Exact observation scheme (no censoring)",
      "topics": [
        "observe_exact"
      ]
    },
    {
      "page": "observe_interval_censor",
      "title": "Interval-censoring observation scheme",
      "topics": [
        "observe_interval_censor"
      ]
    },
    {
      "page": "observe_left_censor",
      "title": "Left-censoring observation scheme",
      "topics": [
        "observe_left_censor"
      ]
    },
    {
      "page": "observe_mixture",
      "title": "Mixture of observation schemes",
      "topics": [
        "observe_mixture"
      ]
    },
    {
      "page": "observe_periodic",
      "title": "Periodic inspection observation scheme",
      "topics": [
        "observe_periodic"
      ]
    },
    {
      "page": "observe_right_censor",
      "title": "Right-censoring observation scheme",
      "topics": [
        "observe_right_censor"
      ]
    },
    {
      "page": "print.kofn",
      "title": "Print method for kofn models",
      "topics": [
        "print.kofn"
      ]
    },
    {
      "page": "rdata_masked",
      "title": "Generate masked k-out-of-n data",
      "topics": [
        "rdata_masked"
      ]
    },
    {
      "page": "rdata_scheme1",
      "title": "Generate Scheme 1 (periodic inspection) data",
      "topics": [
        "rdata_scheme1"
      ]
    },
    {
      "page": "rdata.exp_kofn",
      "title": "Random data generation for exponential k-out-of-n model",
      "topics": [
        "rdata.exp_kofn"
      ]
    },
    {
      "page": "rdata.wei_kofn",
      "title": "Generate random data from a Weibull k-out-of-n system",
      "topics": [
        "rdata.wei_kofn"
      ]
    },
    {
      "page": "S_sys_exp",
      "title": "System survival function for exponential parallel systems",
      "topics": [
        "S_sys_exp"
      ]
    },
    {
      "page": "score.exp_kofn",
      "title": "Score function for exponential k-out-of-n model",
      "topics": [
        "score.exp_kofn"
      ]
    },
    {
      "page": "score.wei_kofn",
      "title": "Score function for Weibull k-out-of-n system",
      "topics": [
        "score.wei_kofn"
      ]
    },
    {
      "page": "solve_mle",
      "title": "Default MLE solver for positive parameters",
      "topics": [
        "solve_mle"
      ]
    },
    {
      "page": "trunc_log_moment_vec",
      "title": "Vectorized truncated log-moment of the Weibull distribution",
      "topics": [
        "trunc_log_moment_vec"
      ]
    },
    {
      "page": "trunc_pow_moment",
      "title": "Scalar truncated power moment of the Weibull distribution",
      "topics": [
        "trunc_pow_moment"
      ]
    },
    {
      "page": "trunc_pow_moment_vec",
      "title": "Vectorized truncated power moment of the Weibull distribution",
      "topics": [
        "trunc_pow_moment_vec"
      ]
    },
    {
      "page": "w_j_exact",
      "title": "Compute w_j(t) = f_j(t) * prod_i != j F_i(t) for exponential components",
      "topics": [
        "w_j_exact"
      ]
    },
    {
      "page": "w_j_integral",
      "title": "Closed-form integral of w_j(t) over an interval",
      "topics": [
        "w_j_integral"
      ]
    }
  ],
  "_readme": "https://github.com/queelius/kofn/raw/HEAD/README.md",
  "_rundeps": [
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    "algebraic.mle",
    "boot",
    "compositional.mle",
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    "flexhaz",
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    "R6"
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      "filename": "dist-structure-integration.html",
      "title": "dist.structure integration",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Two packages, two missions",
        "What kofn delegates to dist.structure",
        "When you might invoke dist.structure directly",
        "Convention conversion",
        "Migration notes: v0.2.0 -> v0.3.0",
        "Why this architecture"
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      "modified": "2026-05-23 06:18:38",
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        "Inclusion-Exclusion Expansion",
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        "What kofn does",
        "Your first fit",
        "Convention",
        "Observation schemes",
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        "Periodic inspection (Scheme 1)",
        "Masked cause-of-failure",
        "Fisher information comparison",
        "What's next"
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      "filename": "ecosystem.html",
      "title": "Inside kofn: Building on the rlang MLE Stack",
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        "The design in one paragraph",
        "The stack",
        "A running example",
        "Layer 1: the likelihood calculus (likelihood.model)",
        "Layer 2: the optimizer (compositional.mle)",
        "Layer 3: the MLE object (algebraic.mle)",
        "Layer 4: the distribution bridge (algebraic.dist)",
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      "filename": "observation-schemes.html",
      "title": "Observation Scheme Composability",
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      "headings": [
        "The Observe Functor API",
        "Composing Schemes with rdata",
        "Effect on Estimation",
        "Mixed Observation Environments"
      ],
      "created": "2026-03-24 22:12:33",
      "modified": "2026-05-23 06:18:38",
      "commits": 5
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      "filename": "periodic-inspection.html",
      "title": "Observation Schemes: Resolving Information Asymmetry via Periodic Inspection",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "1. The observation scheme hierarchy",
        "2. The information asymmetry problem",
        "3. Scheme 1: Periodic inspection",
        "The likelihood",
        "4. Scheme 0 vs Scheme 1 comparison",
        "Monte Carlo comparison",
        "5. Sensitivity to inspection interval width",
        "6. Information-theoretic interpretation",
        "7. Fisher information comparison",
        "8. Scheme 1 across the k-spectrum",
        "9. Practical guidelines"
      ],
      "created": "2026-03-14 02:22:15",
      "modified": "2026-05-23 06:18:38",
      "commits": 9
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      "filename": "weibull-em.html",
      "title": "Weibull Parallel Systems: EM Algorithm for Shape-Scale Estimation",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Why Weibull?",
        "What breaks: from exponential to Weibull",
        "Direct MLE struggles",
        "The EM algorithm",
        "E-step: classification and truncated moments",
        "M-step: profile optimization",
        "EM vs direct MLE comparison",
        "Shape effect on estimation difficulty",
        "Mixed shapes: DFR + IFR systems",
        "Base R stats integration",
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