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Merge pull request #427 from cmu-delphi/main-to-dev
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.github/workflows/R-CMD-check.yaml

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GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
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R_KEEP_PKG_SOURCE: yes
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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- uses: r-lib/actions/setup-r@v2
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with:

.github/workflows/pkgdown.yaml

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needs: website
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- name: Build site
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# - target_ref gets the ref from a different variable, depending on the event
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# - override allows us to set the pkgdown mode and version_label
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# - mode: release is the standard build mode, devel places the site in /dev
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# - version_label: 'light' and 'success' are CSS labels for Bootswatch: Cosmo
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# https://bootswatch.com/cosmo/
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# - we use pkgdown:::build_github_pages to build the site because of an issue in pkgdown
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# https://github.com/r-lib/pkgdown/issues/2257
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run: |
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target_ref <- "${{ github.event_name == 'pull_request' && github.base_ref || github.ref }}"
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override <- if (target_ref == "main" || target_ref == "refs/heads/main") {
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list(development = list(mode = "release", version_label = "light"))
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} else if (target_ref == "dev" || target_ref == "refs/heads/dev") {
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list(development = list(mode = "devel", version_label = "success"))
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} else {
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stop("Unexpected target_ref: ", target_ref)
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}
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pkg <- pkgdown::as_pkgdown(".", override = override)
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cli::cli_rule("Cleaning files from old site...")
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pkgdown::clean_site(pkg)
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pkgdown::build_site(pkg, preview = FALSE, install = FALSE, new_process = FALSE)
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pkgdown:::build_github_pages(pkg)
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run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE)
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shell: Rscript {0}
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- name: Deploy to GitHub pages 🚀
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if: github.event_name != 'pull_request'
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uses: JamesIves/github-pages-deploy-action@v4.4.1
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uses: JamesIves/github-pages-deploy-action@v4.5.0
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with:
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clean: false
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branch: gh-pages

.github/workflows/styler.yml

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GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
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steps:
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- name: Checkout repo
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uses: actions/checkout@v3
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uses: actions/checkout@v4
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with:
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fetch-depth: 0
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.gitignore

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/Meta/
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.Rprofile
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renv.lock
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renv/
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renv/

DESCRIPTION

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Package: epipredict
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Title: Basic epidemiology forecasting methods
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Version: 0.1.4
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Version: 0.1.5
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Authors@R: c(
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person("Daniel J.", "McDonald", , "[email protected]", role = c("aut", "cre")),
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person("Ryan", "Tibshirani", , "[email protected]", role = "aut"),
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person("Dmitry", "Shemetov", email = "[email protected]", role = "aut"),
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person("David", "Weber", email = "[email protected]", role = "aut"),
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person("CMU's Delphi Research Group", role = c("cph", "fnd")),
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person("Dmitry", "Shemetov", , "[email protected]", role = "aut"),
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person("David", "Weber", , "[email protected]", role = "aut"),
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person("Delphi Research Group", role = c("cph", "fnd")),
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person("Logan", "Brooks", role = "aut"),
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person("Rachel", "Lobay", role = "aut"),
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person("Maggie", "Liu", role = "ctb"),
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https://cmu-delphi.github.io/epipredict
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BugReports: https://github.com/cmu-delphi/epipredict/issues/
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Depends:
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epiprocess (>= 0.9.0),
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epidatasets,
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epiprocess (>= 0.9.0),
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parsnip (>= 1.0.0),
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R (>= 3.5.0)
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Imports:
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VignetteBuilder:
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knitr
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Remotes:
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cmu-delphi/delphidocs,
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cmu-delphi/epidatasets,
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cmu-delphi/epidatr,
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cmu-delphi/epiprocess,
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cmu-delphi/epidatasets,
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dajmcdon/smoothqr
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Config/Needs/website: cmu-delphi/delphidocs
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Config/testthat/edition: 3
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Encoding: UTF-8
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LazyData: true

README.md

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You should be able to do a reasonably limited amount of
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customization on them. For the basic forecasters, we currently
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provide:
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- Baseline flatline forecaster
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- Autoregressive forecaster
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- Autoregressive classifier
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- CDC FluSight flatline forecaster
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- Baseline flatline forecaster
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- Autoregressive forecaster
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- Autoregressive classifier
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- CDC FluSight flatline forecaster
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2. A framework for creating custom forecasters out of modular
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components. There are four types of components:
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- Preprocessor: do things to the data before model training
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- Trainer: train a model on data, resulting in a fitted model
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object
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- Predictor: make predictions, using a fitted model object
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- Postprocessor: do things to the predictions before returning
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- Preprocessor: do things to the data before model training
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- Trainer: train a model on data, resulting in a fitted model object
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- Predictor: make predictions, using a fitted model object
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- Postprocessor: do things to the predictions before returning
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**Target audiences:**
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- Basic. Has data, calls forecaster with default arguments.
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- Intermediate. Wants to examine changes to the arguments, take
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advantage of built in flexibility.
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- Advanced. Wants to write their own forecasters. Maybe willing to
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build up from some components.
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- Basic. Has data, calls forecaster with default arguments.
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- Intermediate. Wants to examine changes to the arguments, take
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advantage of built in flexibility.
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- Advanced. Wants to write their own forecasters. Maybe willing to build
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up from some components.
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The Advanced user should find their task to be relatively easy. Examples
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of these tasks are illustrated in the [vignettes and
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but up-to-date versions of this could be downloaded with the
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[`{epidatr}` package](https://cmu-delphi.github.io/epidatr/) and
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processed using
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[`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/).\[1\]
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[`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/).[^1]
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``` r
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library(epipredict)
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two_week_ahead
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#> ══ A basic forecaster of type ARX Forecaster ═══════════════════════════════
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#>
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#> This forecaster was fit on 2024-01-29 15:10:01.
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#> This forecaster was fit on 2024-11-11 11:38:31.
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#>
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#> Training data was an <epi_df> with:
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#> • Geography: state,
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the death rate per 100K inhabitants is available for every state
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(`geo_value`) along with a 90% predictive interval.
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1. Other epidemiological signals for non-Covid related illnesses are
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[^1]: Other epidemiological signals for non-Covid related illnesses are
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also available with
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[`{epidatr}`](https://github.com/cmu-delphi/epidatr) which
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interfaces directly to Delphi’s [Epidata

_pkgdown.yml

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url: https://cmu-delphi.github.io/epipredict/
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# This is to give a default value to the `mode` parameter in the
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# `pkgdown::build_site()` function. This is useful when building the site locally,
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# as it will default to `devel` mode. In practice, this should all be handled
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# dynamically by the CI/CD pipeline.
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development:
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mode: devel
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version_label: success
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mode: auto
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template:
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bootstrap: 5
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bootswatch: cosmo
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bslib:
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font_scale: 1.0
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primary: "#C41230"
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success: "#B4D43C"
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link-color: "#C41230"
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navbar:
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bg: primary
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type: light
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package: delphidocs
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articles:
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- title: Get started
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- backtesting
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- arx-classifier
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- update
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- title: Advanced methods
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contents:
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- articles/sliding
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- articles/smooth-qr
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- articles/symptom-surveys
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- panel-data
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repo:
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url:
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home: https://github.com/cmu-delphi/epipredict/tree/main/
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source: https://github.com/cmu-delphi/epipredict/blob/main/
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home: https://github.com/cmu-delphi/epipredict/
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source: https://github.com/cmu-delphi/epipredict/
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issue: https://github.com/cmu-delphi/epipredict/issues
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user: https://github.com/
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home:
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links:
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- text: Introduction to Delphi's Tooling Work
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href: https://cmu-delphi.github.io/delphi-tooling-book/
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- text: The epiprocess R package
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- text: The epiprocess package
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href: https://cmu-delphi.github.io/epiprocess/
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- text: The epidatr R package
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href: https://github.com/cmu-delphi/epidatr/
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- text: The epidatasets R package
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href: https://cmu-delphi.github.io/epidatasets/
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- text: The covidcast R package
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href: https://cmu-delphi.github.io/covidcast/covidcastR/
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sidebar:
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structure: [links, license, community, citation, authors, dev, related]
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components:
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related:
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title: Delphi packages and resources
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text: |
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* [The epidatr package](https://github.com/cmu-delphi/epidatr/)
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* [The epiprocess package](https://cmu-delphi.github.io/epiprocess/)
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* [The epidatasets package](https://cmu-delphi.github.io/epidatasets/)
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* [Introduction to Delphi's Tooling Work](https://cmu-delphi.github.io/delphi-tooling-book/)
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reference:
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- title: Simple forecasters
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desc: Complete forecasters that produce reasonable baselines
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contents:
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- contains("forecaster")
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- contains("classifier")
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- title: Forecaster modifications
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desc: Constructors to modify forecaster arguments and utilities to produce `epi_workflow` objects
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contents:
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- title: Helper functions for Hub submission
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- flusight_hub_formatter
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- title: Parsnip engines
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desc: Prediction methods not available elsewhere
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contents:
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- quantile_reg
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- smooth_quantile_reg
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- grf_quantiles
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- title: Custom panel data forecasting workflows
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contents:
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- epi_recipe
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contents:
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- starts_with("step_")
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- contains("bake")
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- title: Epi recipe verification checks
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contents:
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- check_enough_train_data
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- title: Forecast postprocessing
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desc: Create a series of postprocessing operations
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contents:
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- frosting
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- ends_with("_frosting")
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- get_test_data
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- tidy.frosting
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- title: Frosting layers
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contents:
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- contains("layer")
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- contains("slather")
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- title: Automatic forecast visualization
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contents:
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- autoplot.epi_workflow
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- autoplot.canned_epipred
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- title: Utilities for quantile distribution processing
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contents:
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- dist_quantiles
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- extrapolate_quantiles
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- nested_quantiles
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- weighted_interval_score
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- starts_with("pivot_quantiles")
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- title: Other utilities
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contents:
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- clean_f_name

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