{ "_id": "6a212e7dcd65a98ecbd2411f", "Package": "baselinenowcast", "Title": "Baseline Nowcasting for Right-Truncated Epidemiological Data", "Version": "0.2.0", "Authors@R": "c(person(given = \"Kaitlyn\",\nfamily = \"Johnson\",\nrole = c(\"aut\", \"cre\", \"cph\"),\nemail = \"kaitlyn.johnson@lshtm.ac.uk\",\ncomment = c(ORCID = \"0000-0001-8011-0012\")),\nperson(given = \"Emily\",\nfamily = \"Tyszka\",\nemail = \"etsyzka@umass.edu\",\nrole = c(\"aut\"),\ncomment = c(ORCID = \"0009-0005-6088-4017\")),\nperson(given = \"Johannes\",\nfamily = \"Bracher\",\nrole = c(\"aut\"),\nemail = \"johannes.bracher@kit.edu\",\ncomment = c(ORCID = \"0000-0002-3777-1410\")),\nperson(given = \"Sebastian\",\nfamily = \"Funk\",\nrole = c(\"aut\"),\nemail = \"sebastian.funk@lshtm.ac.uk\",\ncomment = c(ORCID = \"0000-0002-2842-3406\")),\nperson(given = \"Sam\",\nfamily = \"Abbott\",\nrole = c(\"aut\"),\nemail = \"contact@samabbott.co.uk\",\ncomment = c(ORCID = \"0000-0001-8057-8037\")),\nperson(given = \"Tim\",\nfamily = \"Taylor\",\nrole = \"ctb\",\nemail = \"tim.taylor@hiddenelephants.co.uk\",\ncomment = c(ORCID = \"0000-0002-8587-7113\")))", "Description": "Nowcasting right-truncated epidemiological data is\ncritical for timely public health decision-making, as reporting\ndelays can create misleading impressions of declining trends in\nrecent data. This package provides nowcasting methods based on\nusing empirical delay distributions and uncertainty from past\nperformance. It is also designed to be used as a baseline\nmethod for developers of new nowcasting methods. For more\ndetails on the performance of the method(s) in this package\napplied to case studies of COVID-19 and norovirus, see our\nrecent paper at\n. The package\nsupports standard data frame inputs with reference date, report\ndate, and count columns, as well as the direct use of reporting\ntriangles, and is compatible with 'epinowcast' objects.\nAlongside an opinionated default workflow, it has a low-level\npipe-friendly modular interface, allowing context-specific\nworkflows. It can accommodate a wide spectrum of reporting\nschedules, including mixed patterns of reference and reporting\n(daily-weekly, weekly-daily). It also supports sharing delay\ndistributions and uncertainty estimates between strata, as well\nas custom uncertainty models and delay estimation methods.", "License": "MIT + file LICENSE", "URL": "https://github.com/epinowcast/baselinenowcast,\nhttps://baselinenowcast.epinowcast.org", "BugReports": "https://github.com/epinowcast/baselinenowcast/issues", "Encoding": "UTF-8", "Language": "en-GB", "LazyData": "true", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.3", "VignetteBuilder": "knitr", "Config/Needs/hexsticker": "hexSticker, sysfonts, ggplot2", "Config/Needs/website": "r-lib/pkgdown, epinowcast/enwtheme", "Repository": "https://epinowcast.r-universe.dev", "Date/Publication": "2026-02-03 16:53:08 UTC", "RemoteUrl": "https://github.com/epinowcast/baselinenowcast", "RemoteRef": "v0.2.0", "RemoteSha": "cc398eb63c62a938156f3762fb8d9111db785deb", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-04 07:45:01 UTC", "User": "root" }, "Author": "Kaitlyn Johnson [aut, cre, cph] (ORCID:\n),\nEmily Tyszka [aut] (ORCID: ),\nJohannes Bracher [aut] (ORCID: ),\nSebastian Funk [aut] (ORCID: ),\nSam Abbott [aut] (ORCID: ),\nTim Taylor [ctb] (ORCID: )", "Maintainer": "Kaitlyn Johnson ", "MD5sum": "97b5056e43958170c59931aa8f47049b", "_user": "epinowcast", "_type": "src", "_file": "baselinenowcast_0.2.0.tar.gz", "_fileid": "23020324cc4f1c85d8ef2a09944eeeadc43331f5111cd1c7e6e56f6ac7b0e89f", "_filesize": 2586011, "_sha256": "23020324cc4f1c85d8ef2a09944eeeadc43331f5111cd1c7e6e56f6ac7b0e89f", "_created": "2026-06-04T07:45:01.000Z", "_published": "2026-06-04T07:51:25.474Z", "_distro": "noble", "_jobs": [ { "job": 79472979853, "time": 296, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7405899302" }, { "job": 79472979800, "time": 284, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7405895345" }, { "job": 79472979801, "time": 345, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7405900531" 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Johnson <94390107+kaitejohnson@users.noreply.github.com>", "committer": "GitHub ", "message": "Issue 389: update badge to epinowcast universe (#398)\n\nCo-authored-by: Kaitlyn Johnson \n", "time": 1770137588 }, "_maintainer": { "name": "Kaitlyn Johnson", "email": "kaitlyn.johnson@lshtm.ac.uk", "orcid": "0000-0001-8011-0012" }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.1.0", "role": "Depends" }, { "package": "cli", "role": "Imports" }, { "package": "checkmate", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "utils", "role": "Imports" }, { "package": "rlang", "role": "Imports" }, { "package": "purrr", "role": "Imports" }, { "package": "bookdown", "role": "Suggests" }, { "package": "ChainLadder", "role": "Suggests" }, { "package": "dplyr", "role": "Suggests" }, { "package": "tidyr", "role": "Suggests" }, { "package": "stringr", "role": "Suggests" }, { "package": "lubridate", "role": "Suggests" }, { "package": "readr", "role": 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disease" }, "_downloads": { "count": 151, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/baselinenowcast" }, "_devurl": "https://github.com/epinowcast/baselinenowcast", "_pkgdown": "https://baselinenowcast.epinowcast.org", "_searchresults": 15, "_rbuild": "4.6.0", "_assets": [ "extra/baselinenowcast.html", "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "manual.pdf" ], "_homeurl": "https://github.com/epinowcast/baselinenowcast", "_realowner": "epinowcast", "_cranurl": true, "_releases": [ { "version": "0.2.0", "date": "2026-02-03" } ], "_exports": [ "allocate_reference_times", "apply_delay", "apply_reporting_structure", "apply_reporting_structures", "as_ChainLadder_triangle", "as_reporting_triangle", "assert_baselinenowcast_df", "assert_reporting_triangle", "baselinenowcast", "combine_obs_with_pred", "estimate_and_apply_delay", "estimate_and_apply_delays", "estimate_and_apply_uncertainty", "estimate_delay", "estimate_uncertainty", "estimate_uncertainty_retro", "fit_by_horizon", "fit_nb", "get_delays_from_dates", "get_delays_unit", "get_max_delay", "get_mean_delay", "get_quantile_delay", "get_reference_dates", "get_report_dates", "get_reporting_structure", "is_reporting_triangle", "new_baselinenowcast_df", "new_reporting_triangle", "preprocess_negative_values", "sample_nb", "sample_nowcast", "sample_nowcasts", "sample_prediction", "sample_predictions", "truncate_to_delay", "truncate_to_quantile", "truncate_to_row", "truncate_to_rows", "validate_reporting_triangle" ], "_datasets": [ { "name": "example_downward_corr_rt", "title": "Example reporting triangle with downward corrections", "object": "example_downward_corr_rt", "class": [ "reporting_triangle", "matrix" ], "fields": [ "0", "1", "2", "3" ], "rows": 8, "table": true, "tojson": true }, { "name": "example_reporting_triangle", "title": "Simple example reporting triangle for demonstrations", "object": "example_reporting_triangle", "class": [ "reporting_triangle", "matrix" ], "fields": [ "0", "1", "2", "3" ], "rows": 7, "table": true, "tojson": true }, { "name": "germany_covid19_hosp", "title": "Incident COVID-19 hospitalisations indexed by the date of positive test (reference date) and report date from Germany in 2021 and 2022.", "object": "germany_covid19_hosp", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "reference_date", "location", "age_group", "delay", "count", "report_date" ], "rows": 63140, "table": true, "tojson": true }, { "name": "syn_nssp_df", "title": "A synthetic dataset containing the number of incident cases indexed by reference date and report date. While data of this form could be from any source, this data is meant to represent the output of pre-processing the syn_nssp_line_list dataset, which is a synthetic patient-level line list data from the United State's National Syndromic Surveillance System (NSSP).", "object": "syn_nssp_df", "class": [ "spec_tbl_df", "tbl_df", "tbl", "data.frame" ], "fields": [ "reference_date", "report_date", "count" ], "rows": 3795, "table": true, "tojson": true }, { "name": "syn_nssp_line_list", "title": "A synthetic dataset resembling line-list (each row is a patient) data from the United States' National Syndromic Surveillance System (NSSP) accessed via the Essence platform. All entries are synthetic, formatted to look as close to the real raw data as possible.", "object": "syn_nssp_line_list", "class": [ "spec_tbl_df", "tbl_df", "tbl", "data.frame" ], "fields": [ "C_Processed_BioSense_ID", "CCDDParsed", "DischargeDiagnosisMDTUpdates", "DischargeDiagnosisUpdates", "HasBeenAdmitted", "C_Visit_Date_Time", "c_race", "sex" ], "rows": 25, "table": true, "tojson": true } ], "_help": [ { "page": "sub-.reporting_triangle", "title": "Subset reporting_triangle objects", "concept": [ "reporting_triangle" ], "topics": [ "[.reporting_triangle" ] }, { "page": "subset-.reporting_triangle", "title": "Subset assignment for reporting_triangle objects", "concept": [ "reporting_triangle" ], "topics": [ "[<-.reporting_triangle" ] }, { "page": "allocate_reference_times", "title": "Allocate training volume based on combination of defaults and user-specified values for training volume for delay and uncertainty estimation.", "concept": [ "workflow_wrappers" ], "topics": [ "allocate_reference_times" ] }, { "page": "apply_delay", "title": "Apply the delay to generate a point nowcast", "concept": [ "generate_point_nowcasts" ], "topics": [ "apply_delay" ] }, { "page": "apply_reporting_structure", "title": "Apply reporting structure to generate a single retrospective reporting triangle", "concept": [ "generate_retrospective_data" ], "topics": [ "apply_reporting_structure" ] }, { "page": "apply_reporting_structures", "title": "Apply reporting structures to generate retrospective reporting triangles", "concept": [ "generate_retrospective_data" ], "topics": [ "apply_reporting_structures" ] }, { "page": "as_ChainLadder_triangle", "title": "Convert reporting_triangle to ChainLadder triangle format", "concept": [ "reporting_triangle" ], "topics": [ "as_ChainLadder_triangle" ] }, { "page": "as_reporting_triangle", "title": "Create a 'reporting_triangle' object", "concept": [ "reporting_triangle" ], "topics": [ "as_reporting_triangle" ] }, { "page": "as_reporting_triangle.data.frame", "title": "Create a 'reporting_triangle' object from a data.frame", "concept": [ "reporting_triangle" ], "topics": [ "as_reporting_triangle.data.frame" ] }, { "page": "as_reporting_triangle.matrix", "title": "Create a 'reporting_triangle' from a matrix", "concept": [ "reporting_triangle" ], "topics": [ "as_reporting_triangle.matrix" ] }, { "page": "as_reporting_triangle.triangle", "title": "Convert ChainLadder triangle to reporting_triangle format", "concept": [ "reporting_triangle" ], "topics": [ "as_reporting_triangle.triangle" ] }, { "page": "as.data.frame.reporting_triangle", "title": "Convert reporting_triangle to data.frame", "concept": [ "reporting_triangle" ], "topics": [ "as.data.frame.reporting_triangle" ] }, { "page": "as.matrix.reporting_triangle", "title": "Convert reporting_triangle to plain matrix", "concept": [ "reporting_triangle" ], "topics": [ "as.matrix.reporting_triangle" ] }, { "page": "assert_baselinenowcast_df", "title": "Assert validity of 'baselinenowcast_df' objects", "concept": [ "baselinenowcast_df" ], "topics": [ "assert_baselinenowcast_df" ] }, { "page": "assert_reporting_triangle", "title": "Assert validity of 'reporting_triangle' objects", "concept": [ "reporting_triangle" ], "topics": [ "assert_reporting_triangle" ] }, { "page": "baselinenowcast", "title": "Generate a nowcast", "concept": [ "baselinenowcast_df" ], "topics": [ "baselinenowcast" ] }, { "page": "baselinenowcast_df-class", "title": "Nowcast Data.frame Object", "concept": [ "baselinenowcast_df" ], "topics": [ "baselinenowcast_df", "baselinenowcast_df-class" ] }, { "page": "baselinenowcast.data.frame", "title": "Create a dataframe of nowcast results from a dataframe of cases indexed by reference date and report date", "concept": [ "baselinenowcast_df" ], "topics": [ "baselinenowcast.data.frame" ] }, { "page": "baselinenowcast.reporting_triangle", "title": "Create a dataframe of nowcast results from a single reporting triangle", "concept": [ "baselinenowcast_df" ], "topics": [ "baselinenowcast.reporting_triangle" ] }, { "page": "combine_obs_with_pred", "title": "Combine observed data with a single prediction draw", "concept": [ "generate_probabilistic_nowcasts" ], "topics": [ "combine_obs_with_pred" ] }, { "page": "estimate_and_apply_delay", "title": "Estimate and apply delay from a reporting triangle", "concept": [ "workflow_wrappers" ], "topics": [ "estimate_and_apply_delay" ] }, { "page": "estimate_and_apply_delays", "title": "Estimate and apply delays to generate retrospective nowcasts", "concept": [ "workflow_wrappers" ], "topics": [ "estimate_and_apply_delays" ] }, { "page": "estimate_and_apply_uncertainty", "title": "Estimate and apply uncertainty to a point nowcast matrix", "concept": [ "workflow_wrappers" ], "topics": [ "estimate_and_apply_uncertainty" ] }, { "page": "estimate_delay", "title": "Estimate a delay distribution from a reporting triangle", "concept": [ "estimate_delay" ], "topics": [ "estimate_delay" ] }, { "page": "estimate_uncertainty", "title": "Estimate uncertainty parameters", "concept": [ "estimate_observation_error" ], "topics": [ "estimate_uncertainty" ] }, { "page": "estimate_uncertainty_retro", "title": "Estimate uncertainty parameters using retrospective nowcasts", "concept": [ "workflow_wrappers" ], "topics": [ "estimate_uncertainty_retro" ] }, { "page": "example_downward_corr_rt", "title": "Example reporting triangle with downward corrections", "concept": [ "example_data" ], "topics": [ "example_downward_corr_rt" ] }, { "page": "example_reporting_triangle", "title": "Simple example reporting triangle for demonstrations", "concept": [ "example_data" ], "topics": [ "example_reporting_triangle" ] }, { "page": "fit_by_horizon", "title": "Helper function that fits its each column of the matrix (horizon) to an observation model.", "concept": [ "estimate_observation_error" ], "topics": [ "fit_by_horizon" ] }, { "page": "fit_nb", "title": "Fit a negative binomial to a vector of observations and expectations", "concept": [ "estimate_observation_error" ], "topics": [ "fit_nb" ] }, { "page": "germany_covid19_hosp", "title": "Incident COVID-19 hospitalisations indexed by the date of positive test (reference date) and report date from Germany in 2021 and 2022.", "concept": [ "example_data" ], "topics": [ "germany_covid19_hosp" ] }, { "page": "get_delays_from_dates", "title": "Compute delays between report dates and reference dates", "concept": [ "reporting_triangle" ], "topics": [ "get_delays_from_dates" ] }, { "page": "get_delays_unit", "title": "Get delays unit from a reporting triangle", "concept": [ "reporting_triangle" ], "topics": [ "get_delays_unit" ] }, { "page": "get_max_delay", "title": "Get maximum delay from reporting_triangle", "concept": [ "reporting_triangle" ], "topics": [ "get_max_delay" ] }, { "page": "get_mean_delay", "title": "Get mean delay for each row of reporting_triangle", "concept": [ "reporting_triangle" ], "topics": [ "get_mean_delay" ] }, { "page": "get_quantile_delay", "title": "Get quantile delay for each row of reporting_triangle", "concept": [ "reporting_triangle" ], "topics": [ "get_quantile_delay" ] }, { "page": "get_reference_dates", "title": "Get reference dates from reporting_triangle", "concept": [ "reporting_triangle" ], "topics": [ "get_reference_dates" ] }, { "page": "get_report_dates", "title": "Compute report dates from reference dates and delays", "concept": [ "reporting_triangle" ], "topics": [ "get_report_dates" ] }, { "page": "get_reporting_structure", "title": "Get reporting structure from a reporting triangle", "concept": [ "reporting_triangle" ], "topics": [ "get_reporting_structure" ] }, { "page": "head.reporting_triangle", "title": "Get first rows of a reporting_triangle", "concept": [ "reporting_triangle" ], "topics": [ "head.reporting_triangle" ] }, { "page": "is_reporting_triangle", "title": "Check if an object is a reporting_triangle", "concept": [ "reporting_triangle" ], "topics": [ "is_reporting_triangle" ] }, { "page": "new_baselinenowcast_df", "title": "Combine data from a nowcast dataframe, strata, and reference dates", "concept": [ "baselinenowcast_df" ], "topics": [ "new_baselinenowcast_df" ] }, { "page": "new_reporting_triangle", "title": "Class constructor for 'reporting_triangle' objects", "concept": [ "reporting_triangle" ], "topics": [ "new_reporting_triangle" ] }, { "page": "preprocess_negative_values", "title": "Preprocess negative values in the reporting triangle", "concept": [ "estimate_delay" ], "topics": [ "preprocess_negative_values" ] }, { "page": "print.reporting_triangle", "title": "Print a reporting_triangle object", "concept": [ "reporting_triangle" ], "topics": [ "print.reporting_triangle" ] }, { "page": "reporting_triangle-class", "title": "Reporting Triangle Object", "concept": [ "reporting_triangle" ], "topics": [ "reporting_triangle", "reporting_triangle-class" ] }, { "page": "sample_nb", "title": "Sample from negative binomial model given a set of predictions", "concept": [ "generate_probabilistic_nowcasts" ], "topics": [ "sample_nb" ] }, { "page": "sample_nowcast", "title": "Generate a single draw of a nowcast combining observed and predicted values", "concept": [ "generate_probabilistic_nowcasts" ], "topics": [ "sample_nowcast" ] }, { "page": "sample_nowcasts", "title": "Generate multiple draws of a nowcast combining observed and predicted values", "concept": [ "generate_probabilistic_nowcasts" ], "topics": [ "sample_nowcasts" ] }, { "page": "sample_prediction", "title": "Get a draw of only the predicted elements of the nowcast vector", "concept": [ "generate_probabilistic_nowcasts" ], "topics": [ "sample_prediction" ] }, { "page": "sample_predictions", "title": "Get a dataframe of multiple draws of only the predicted elements of the nowcast vector", "concept": [ "generate_probabilistic_nowcasts" ], "topics": [ "sample_predictions" ] }, { "page": "summary.reporting_triangle", "title": "Summarize a reporting_triangle object", "concept": [ "reporting_triangle" ], "topics": [ "summary.reporting_triangle" ] }, { "page": "syn_nssp_df", "title": "A synthetic dataset containing the number of incident cases indexed by reference date and report date. While data of this form could be from any source, this data is meant to represent the output of pre-processing the syn_nssp_line_list dataset, which is a synthetic patient-level line list data from the United State's National Syndromic Surveillance System (NSSP).", "concept": [ "example_data" ], "topics": [ "syn_nssp_df" ] }, { "page": "syn_nssp_line_list", "title": "A synthetic dataset resembling line-list (each row is a patient) data from the United States' National Syndromic Surveillance System (NSSP) accessed via the Essence platform. All entries are synthetic, formatted to look as close to the real raw data as possible.", "concept": [ "example_data" ], "topics": [ "syn_nssp_line_list" ] }, { "page": "tail.reporting_triangle", "title": "Get last rows of a reporting_triangle", "concept": [ "reporting_triangle" ], "topics": [ "tail.reporting_triangle" ] }, { "page": "truncate_to_delay", "title": "Truncate reporting triangle to a specific maximum delay", "concept": [ "reporting_triangle" ], "topics": [ "truncate_to_delay" ] }, { "page": "truncate_to_quantile", "title": "Truncate reporting_triangle to quantile-based maximum delay", "concept": [ "reporting_triangle" ], "topics": [ "truncate_to_quantile" ] }, { "page": "truncate_to_row", "title": "Truncate reporting triangle by removing a specified number of the last rows", "concept": [ "generate_retrospective_data" ], "topics": [ "truncate_to_row" ] }, { "page": "truncate_to_rows", "title": "Truncate reporting triangle by removing bottom rows", "concept": [ "generate_retrospective_data" ], "topics": [ "truncate_to_rows" ] }, { "page": "validate_reporting_triangle", "title": "Validate a reporting_triangle object", "concept": [ "reporting_triangle" ], "topics": [ "validate_reporting_triangle" ] } ], "_pkglogo": "https://github.com/epinowcast/baselinenowcast/raw/v0.2.0/man/figures/logo.png", "_readme": "https://github.com/epinowcast/baselinenowcast/raw/v0.2.0/README.md", "_rundeps": [ "backports", "checkmate", "cli", "glue", "lifecycle", "magrittr", "purrr", "rlang", "vctrs" ], "_vignettes": [ { "source": "baselinenowcast.Rmd", "filename": "baselinenowcast.html", "title": "Getting Started with baselinenowcast", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Packages", "Data", "Pre-processing", "Run the baselinenowcast workflow", "Visualizing the nowcast", "Plot with draws for nowcast only", "Add nowcast draws as thin gray lines", "Add observed data and final data once", "Summary" ], "created": "2025-01-29 10:34:43", "modified": 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