{ "_id": "6a105a50acfb0bcc41ca39de", "Package": "incidental", "Title": "Implements Empirical Bayes Incidence Curves", "Version": "0.1", "Authors@R": "c(person(\"Andrew\", \"Miller\", email = \"acmiller@apple.com\",\nrole = c(\"aut\")),\nperson(\"Lauren\", \"Hannah\", email = \"lauren_hannah@apple.com\",\nrole = c(\"aut\", \"cre\")),\nperson(\"Nicholas\", \"Foti\", email = \"nicholas_foti@apple.com\",\nrole = \"aut\"),\nperson(\"Joseph\", \"Futoma\", email = \"jfutoma@apple.com\",\nrole = \"aut\"),\nperson(\"Apple, Inc.\", email = \"hai@apple.com\", role = \"cph\"))", "Description": "Make empirical Bayes incidence curves from reported case\ndata using a specified delay distribution.", "License": "MIT + file LICENSE", "LazyData": "true", "RoxygenNote": "7.1.1", "VignetteBuilder": "knitr", "Encoding": "UTF-8", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-09 06:03:05 UTC", "User": "root" }, "Author": "Andrew Miller [aut], Lauren Hannah [aut, cre], Nicholas Foti\n[aut], Joseph Futoma [aut], Apple, Inc. [cph]", "Maintainer": "Lauren Hannah ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2020-09-16 08:50:03 UTC", "RemoteUrl": "https://github.com/cran/incidental", "RemoteRef": "HEAD", "RemoteSha": "70cac274ab5204b28b9b399a99515ce2b871ceef", "MD5sum": "4fb40d8501369ac504d825860c575167", "_user": "cran", "_type": "src", "_file": "incidental_0.1.tar.gz", "_fileid": "800b3a1555cc5f3fa4eed7514472020fec4ed5900823b24e09b19ef27fe51f3a", "_filesize": 4539247, "_sha256": "800b3a1555cc5f3fa4eed7514472020fec4ed5900823b24e09b19ef27fe51f3a", "_created": "2026-05-09T06:03:05.000Z", "_published": "2026-05-22T13:29:52.128Z", "_distro": "noble", "_jobs": [ { "job": 77389448144, "time": 139, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "NOTE", "artifact": "6893443592" }, { "job": 77389447988, "time": 133, "config": "linux-release-x86_64", "r": "4.6.0", "check": "NOTE", "artifact": "6893442151" }, { "job": 77389447668, "time": 159, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "6893426532" }, { "job": 77389447270, "time": 114, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7161697581" } ], "_buildurl": "https://github.com/r-universe/cran/actions/runs/25593484728", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/cran/incidental", "_commit": { "id": "70cac274ab5204b28b9b399a99515ce2b871ceef", "author": "Lauren Hannah ", "committer": "cran-robot ", "message": "version 0.1\n", "time": 1600246203 }, "_maintainer": { "name": "Lauren Hannah", "email": "lauren_hannah@apple.com" }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.5.0", "role": "Depends" }, { "package": "ggplot2", "role": "Imports" }, { "package": "MASS", "role": "Imports" }, { "package": "matrixStats", "role": "Imports" }, { "package": "numDeriv", "role": "Imports" }, { "package": "dlnm", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "utils", "role": "Imports" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "testthat", "role": "Suggests" } ], "_owner": "cran", "_selfowned": false, "_usedby": 0, "_updates": [], "_tags": [], "_stars": 2, "_userbio": { "uuid": 6899542, "type": "organization", "name": "cran", "description": "Unofficial read-only mirror of all CRAN R packages" }, "_downloads": { "count": 208, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/incidental" }, "_searchresults": 10, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/incidental.html", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "manual.pdf" ], "_realowner": "cran", "_cranurl": false, "_releases": [ { "version": "0.1", "date": "2020-09-16" } ], "_exports": [ "compute_expected_cases", "compute_log_incidence", "data_check", "data_processing", "fit_incidence", "front_zero_pad", "incidence_to_df", "init_params", "make_ar_extrap_samps", "make_likelihood_matrix", "make_spline_basis", "marg_loglike_poisson", "marg_loglike_poisson_fisher", "marg_loglike_poisson_grad", "poisson_objective", "poisson_objective_grad", "regfun", "regfun_grad", "regfun_hess", "sample_laplace_log_incidence_poisson", "scan_spline_dof", "scan_spline_lam", "train_and_validate", "train_val_split" ], "_datasets": [ { "name": "covid_delay_dist", "title": "Delay distribution from COVID-19 pandemic.", "object": "covid_delay_dist", "class": [ "data.frame" ], "fields": [ "days", "case", "hospitalization", "death" ], "rows": 61, "table": true, "tojson": true }, { "name": "covid_new_york_city", "title": "New York City data from the COVID-19 pandemic.", "object": "covid_new_york_city", "class": [ "data.frame" ], "fields": [ "date", "borough", "case", "hospitalization", "death" ], "rows": 615, "table": true, "tojson": true }, { "name": "spanish_flu", "title": "Daily flu mortality from 1918 flu pandemic.", "object": "spanish_flu_mortality", "class": [ "data.frame" ], "fields": [ "Date", "Indiana", "Kansas", "Philadelphia" ], "rows": 122, "table": true, "tojson": true }, { "name": "spanish_flu_delay_dist", "title": "Delay distribution from 1918 flu pandemic.", "object": "spanish_flu_delay_distribution", "class": [ "data.frame" ], "fields": [ "days", "proportion" ], "rows": 31, "table": true, "tojson": true } ], "_help": [ { "page": "compute_expected_cases", "title": "Compute expected cases", "topics": [ "compute_expected_cases" ] }, { "page": "compute_log_incidence", "title": "Compute log likelihood of incidence model", "topics": [ "compute_log_incidence" ] }, { "page": "covid_delay_dist", "title": "Delay distribution from COVID-19 pandemic.", "topics": [ "covid_delay_dist" ] }, { "page": "covid_new_york_city", "title": "New York City data from the COVID-19 pandemic.", "topics": [ "covid_new_york_city" ] }, { "page": "data_check", "title": "Input data check", "topics": [ "data_check" ] }, { "page": "data_processing", "title": "Data processing wrapper", "topics": [ "data_processing" ] }, { "page": "diff_trans", "title": "Transpose of the 1st difference operator", "topics": [ "diff_trans" ] }, { "page": "fit_incidence", "title": "Fit incidence curve to reported data", "topics": [ "fit_incidence" ] }, { "page": "front_zero_pad", "title": "Pad reported data with zeros in front", "topics": [ "front_zero_pad" ] }, { "page": "incidence_to_df", "title": "Export incidence model to data frame", "topics": [ "incidence_to_df" ] }, { "page": "init_params", "title": "Initialize spline parameters (beta)", "topics": [ "init_params" ] }, { "page": "make_ar_extrap_samps", "title": "Make AR samples for extrapolation past end point", "topics": [ "make_ar_extrap_samps" ] }, { "page": "make_likelihood_matrix", "title": "Make delay likelihood matrix", "topics": [ "make_likelihood_matrix" ] }, { "page": "make_spline_basis", "title": "Create spline basis matrix", "topics": [ "make_spline_basis" ] }, { "page": "marg_loglike_poisson", "title": "Marginal log likelihood This function computes the marginal probability of Pr(reported | beta). Note that lnPmat must be zero padded enough (or censored) to match the length of reported cases vector.", "topics": [ "marg_loglike_poisson" ] }, { "page": "marg_loglike_poisson_fisher", "title": "Marginal log likelihood Fisher information matrix", "topics": [ "marg_loglike_poisson_fisher" ] }, { "page": "marg_loglike_poisson_grad", "title": "Marginal log likelihood gradient", "topics": [ "marg_loglike_poisson_grad" ] }, { "page": "plot.incidence_spline_model", "title": "Plot model from fit_incidence", "topics": [ "plot.incidence_spline_model" ] }, { "page": "poisson_objective", "title": "Poisson objective function", "topics": [ "poisson_objective" ] }, { "page": "poisson_objective_grad", "title": "Poisson objective function gradient", "topics": [ "poisson_objective_grad" ] }, { "page": "poisson_objective_post_cov_approx", "title": "Compute Fisher information matrix for Poisson objective", "topics": [ "poisson_objective_post_cov_approx" ] }, { "page": "regfun", "title": "Beta regularization function", "topics": [ "regfun" ] }, { "page": "regfun_grad", "title": "Beta regularization function gradient", "topics": [ "regfun_grad" ] }, { "page": "regfun_hess", "title": "Beta regularization function Hessian", "topics": [ "regfun_hess" ] }, { "page": "sample_laplace_log_incidence_poisson", "title": "Generate Laplace samples of incidence", "topics": [ "sample_laplace_log_incidence_poisson" ] }, { "page": "scan_spline_dof", "title": "Scan spline degrees of freedom", "topics": [ "scan_spline_dof" ] }, { "page": "scan_spline_lam", "title": "Scan spline regularization parameter", "topics": [ "scan_spline_lam" ] }, { "page": "spanish_flu", "title": "Daily flu mortality from 1918 flu pandemic.", "topics": [ "spanish_flu" ] }, { "page": "spanish_flu_delay_dist", "title": "Delay distribution from 1918 flu pandemic.", "topics": [ "spanish_flu_delay_dist" ] }, { "page": "train_and_validate", "title": "Train and validate model on reported data", "topics": [ "train_and_validate" ] }, { "page": "train_val_split", "title": "Split reported case data", "topics": [ "train_val_split" ] } ], "_readme": "https://github.com/cran/incidental/raw/HEAD/README.md", "_rundeps": [ "cli", "cpp11", "dlnm", "farver", "ggplot2", "glue", "gtable", "isoband", "labeling", "lattice", "lifecycle", "MASS", "Matrix", "matrixStats", "mgcv", "nlme", "numDeriv", "R6", "RColorBrewer", "rlang", "S7", "scales", "tsModel", "vctrs", "viridisLite", "withr" ], "_vignettes": [ { "source": "incidental-tutorial.Rmd", "filename": "incidental-tutorial.html", "title": "incidental-tutorial", "engine": "knitr::rmarkdown", "headings": [ "incidental", "Data Format", "Model Fitting", "Model Outputs", "Advanced Usage", "Linear Extrapolation", "Hyperparameter Tuning", "Hyperparameter Selection Methods", "Validation Likelihood Hyperparameter Selection" ], "created": "2020-09-16 08:50:03", "modified": "2020-09-16 08:50:03", "commits": 1 } ], "_score": 3, "_indexed": true, "_nocasepkg": "incidental", "_universes": [ "cran" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.1", "date": "2026-05-09T06:05:20.000Z", "distro": "noble", "commit": "70cac274ab5204b28b9b399a99515ce2b871ceef", "fileid": "36ea41a53cbeb345000e9f08f8c80f57de84dd7cbff83de20ce625e07ada4782", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/cran/actions/runs/25593484728" }, { "r": "4.6.0", "os": "linux", "version": "0.1", "date": "2026-05-09T06:05:10.000Z", "distro": "noble", "commit": "70cac274ab5204b28b9b399a99515ce2b871ceef", "fileid": "0f34820a86208e67f489b567503e77f969ed6f461a436660cd8b040fef2eb480", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/cran/actions/runs/25593484728" }, { "r": "4.6.0", "os": "wasm", "version": "0.1", "date": "2026-05-22T13:29:36.000Z", "commit": "70cac274ab5204b28b9b399a99515ce2b871ceef", "fileid": "12660a3e016b9f9da4c31eb8142592f33f4ba054450c2844fa136fd87b9255ee", "status": "success", "buildurl": "https://github.com/r-universe/cran/actions/runs/25593484728" } ] }