Port over the existing tests and set up CI

.github/workflows/pkgdown.yaml

@@ -2,7 +2,7 @@
 # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
 on:
   push:
-    branches: [prod]
+    branches: [main, prod]
   pull_request:
     branches: [main, prod]
   release:

.github/workflows/r-cmd-check.yaml

@@ -0,0 +1,28 @@
+name: R CMD check project packages
+
+on:
+  pull_request:
+  push:
+    branches: [main, prod]
+
+jobs:
+  check-package:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v4
+    - uses: actions/checkout@v4
+    - uses: r-lib/actions/setup-r@v2
+      with:
+        r-version: "release"
+        use-public-rspm: true
+        extra-repositories: "https://mc-stan.org/r-packages/"
+    - name: "Set up dependencies for wwinference"
+      uses: r-lib/actions/setup-r-dependencies@v2
+      with:
+        needs: check
+    - name: "Install cmdstan via cmdstanr"
+      uses: epinowcast/actions/install-cmdstan@v1
+      with:
+        cmdstan-version: "latest"
+    - name: "Check wwinference package"
+      uses: r-lib/actions/check-r-package@v2

DESCRIPTION

@@ -61,7 +61,8 @@ Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.3.1
 Suggests:
     testthat (>= 3.0.0),
-    bookdown
+    bookdown,
+    knitr
 Config/testthat/edition: 3
 LazyData: true
 Imports:
@@ -78,6 +79,10 @@ Imports:
     withr,
     cmdstanr (>= 0.8.0),
     rlang,
-    scales
+    scales,
+    ggplot2,
+    posterior
 Remotes:
     stan-dev/cmdstanr
+VignetteBuilder:
+    knitr

NAMESPACE

@@ -38,13 +38,16 @@ importFrom(dplyr,as_tibble)
 importFrom(dplyr,distinct)
 importFrom(dplyr,filter)
 importFrom(dplyr,group_by)
+importFrom(dplyr,lag)
+importFrom(dplyr,lead)
 importFrom(dplyr,left_join)
 importFrom(dplyr,mutate)
 importFrom(dplyr,pull)
 importFrom(dplyr,rename)
 importFrom(dplyr,row_number)
 importFrom(dplyr,select)
 importFrom(dplyr,ungroup)
+importFrom(fs,path_package)
 importFrom(ggplot2,aes)
 importFrom(ggplot2,facet_grid)
 importFrom(ggplot2,facet_wrap)
@@ -55,13 +58,28 @@ importFrom(ggplot2,geom_point)
 importFrom(ggplot2,geom_ribbon)
 importFrom(ggplot2,geom_vline)
 importFrom(ggplot2,ggplot)
+importFrom(ggplot2,ggtitle)
 importFrom(ggplot2,labs)
 importFrom(ggplot2,scale_colour_discrete)
 importFrom(ggplot2,scale_fill_discrete)
 importFrom(ggplot2,scale_x_date)
 importFrom(ggplot2,scale_y_continuous)
 importFrom(ggplot2,theme)
 importFrom(lubridate,ymd)
+importFrom(posterior,as_draws_list)
+importFrom(posterior,subset_draws)
+importFrom(rlang,sym)
+importFrom(stats,dnbinom)
+importFrom(stats,dweibull)
+importFrom(stats,ecdf)
+importFrom(stats,plogis)
+importFrom(stats,qlogis)
+importFrom(stats,rlnorm)
+importFrom(stats,rnbinom)
+importFrom(stats,rnorm)
+importFrom(stats,rt)
+importFrom(stats,sd)
+importFrom(stats,time)
 importFrom(tidybayes,spread_draws)
 importFrom(tidybayes,stat_halfeye)
 importFrom(tidybayes,stat_slab)

R/checkers.R

@@ -22,9 +22,11 @@ check_date <- function(df, max_date, call = rlang::caller_env()) {
   invisible()
 }
 
+
 #' Check that all elements of a vector are non-negative
 #'
 #' @param x vector of arguments to check for negativity
+#' @param arg string to print the name of the element your checking
 #' @param call Calling environment to be passed to the type checker
 #'
 #' @return NULL, invisibly

R/data.R

@@ -0,0 +1,118 @@
+#' Example wastewater dataset.
+#'
+#' A dataset containing the simulated wastewater concentrations
+#' (labeled here as `genome_copies_per_ml`) by sample collection date (`date`),
+#' the site where the sample was collected (`site`) and the lab where the
+#' samples were processed (`lab`). Additional columns that are required
+#' attributes needed for the model are the limit of detection for that lab on
+#' each day (labeled here as `lod`) and the population size of the wastewater
+#' catchment area represented by the wastewater concentrations in each `site`.
+#'
+#' This data is generated via the default values in the
+#'  `generate_simulated_data()` function. They represent the bare minumum
+#'  required fields needed to pass to the model, and we recommend that users
+#'  try to format their own data to match this format.
+#'
+#' The variables are as follows:
+#'
+#' @format ## ww_data
+#' A tibble with 102 rows and 6 columns
+#' \describe{
+#'   \item{date}{Sample collection date, formatted in ISO8601 standards as
+#'   YYYY-MM-DD}
+#'   \item{site}{The wastewater treatment plant where the sample was collected}
+#'   \item{lab}{The lab where the sample was processed}
+#'   \item{genome_copies_per_ml}{The wastewater concentration measured on the
+#'   date specified, collected in the site specified, and processed in the lab
+#'   specified. The default parameters assume that this quantity is reported
+#'   as the genome copies per mL, on a natural scale.}
+#'   \item{lod}{The limit of detection in the site and lab on a particular day
+#'   of the quantification device (e.g. PCR). This is also by default reported
+#'   in terms of the genome copies per mL.}
+#'   \item{site_pop}{The population size of the wastewater catchment area
+#'   represented by the site variable}
+#'   }
+#' @source vignette_data.R
+"ww_data"
+
+
+
+
+#' Example hospital admissions dataset
+#'
+#'  A dataset containing the simulated daily hospital admissions
+#' (labeled here as `daily_hosp_admits`) by date of admission (`date`).
+#'  Additional columns that are required are the population size of the
+#'  population contributing to the hospital admissions. It is assumed that
+#'  the wastewater sites are subsets of this larger population, which
+#'  is in the package data assumed to be from a hypothetical US state.
+#'  The data generated are daily hospital admissions but they could be any other
+#'  epidemiological count dataset e.g. cases. This data should only contain
+#'  hospital admissions that would have been available as of the date that
+#'  the forecast was made. We recommend that users try to format their data
+#'  to match this format.
+#'
+#' This data is generated via the default values in the
+#'  `generate_simulated_data()` function. They represent the bare minumum
+#'  required fields needed to pass to the model, and we recommend that users
+#'  try to format their own data to match this formate.
+#'
+#' The variables are as follows:
+#' \describe{
+#'   \item{date}{Date the hospital admissions occurred, formatte din ISO8601
+#'   standatds as YYYY-MM-DD}
+#'   \item{daily_hosp_admits}{The number of individuals admitted to the
+#'   hospital on that date, available as of the forecast date}
+#'   \item{state_pop}{The number of people contributing to the daily hospital
+#'   admissions}
+#'   }
+#' @source vignette_data.R
+"hosp_data"
+
+#' Example hospital admissions dataset for evaluation
+#'
+#'  A dataset containing the simulated daily hospital admissions that the model
+#'  will be evaluated against (labeled here as `daily_hosp_admits_for_eval`)
+#'  by date of admission (`date`). This data is not needed to fit the model,
+#'  but is used in the Getting Started vignette to demonstrate the forecasted
+#'  hospital admissions compared to those later observed.
+#'
+#' This data is generated via the default values in the
+#'  `generate_simulated_data()` function.
+#'
+#' The variables are as follows:
+#' \describe{
+#'   \item{date}{Date the hospital admissions occurred, formatte din ISO8601
+#'   standatds as YYYY-MM-DD}
+#'   \item{daily_hosp_admits_for_eval}{The number of individuals admitted to the
+#'   hospital on that date, available beyond the forecast date for evaluating
+#'   the forecasted hospital admissions}
+#'   \item{state_pop}{The number of people contributing to the daily hospital
+#'   admissions}
+#'   }
+#' @source vignette_data.R
+"hosp_data_eval"
+
+#' COVID-19 post-Omicron generation interval probability mass function
+#'
+#' \describe{
+#' A vector that sums to 1, with each element representing the daily
+#' probability of secondary onward transmission occurring on that day. The
+#' first element of this vector represents the day after primary transmission
+#' occurred, it is assumed to be impossible for primary and secondary
+#' transmission to occur on the same day.
+#' }
+#' @source covid_pmfs.R
+"generation_interval"
+
+#' COVID-19 time delay distribution from infection to hospital admission
+#'
+#' \describe{
+#' A vector that sums to 1, with each element representing the daily
+#' probabilty of transitioning from infected to hospitalized, conditioned on
+#' being infected and eventually ending up hospitalized. The first element
+#' represents the probability of being infected and admitted to the hospital
+#' on the same day
+#' }
+#' @source covid_pmfs.R
+"inf_to_hosp"