added extra.R to ensure document() builds ok

.Rbuildignore

@@ -6,3 +6,4 @@
 ^pkgdown$
 ^\.github$
 ^vignettes/articles$
+^README\.Rmd$

.github/workflows/test-coverage.yaml

@@ -0,0 +1,62 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+on:
+  push:
+    branches: [main, master]
+  pull_request:
+    branches: [main, master]
+
+name: test-coverage.yaml
+
+permissions: read-all
+
+jobs:
+  test-coverage:
+    runs-on: ubuntu-latest
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::covr, any::xml2
+          needs: coverage
+
+      - name: Test coverage
+        run: |
+          cov <- covr::package_coverage(
+            quiet = FALSE,
+            clean = FALSE,
+            exclusions <- "src/stanExports_epifilter_covariates.h|src/stanExports_epifilter.h"
+            install_path = file.path(normalizePath(Sys.getenv("RUNNER_TEMP"), winslash = "/"), "package")
+          )
+          covr::to_cobertura(cov)
+        shell: Rscript {0}
+
+      - uses: codecov/codecov-action@v4
+        with:
+          fail_ci_if_error: ${{ github.event_name != 'pull_request' && true || false }}
+          file: ./cobertura.xml
+          plugin: noop
+          disable_search: true
+          token: ${{ secrets.CODECOV_TOKEN }}
+
+      - name: Show testthat output
+        if: always()
+        run: |
+          ## --------------------------------------------------------------------
+          find '${{ runner.temp }}/package' -name 'testthat.Rout*' -exec cat '{}' \; || true
+        shell: bash
+
+      - name: Upload test results
+        if: failure()
+        uses: actions/upload-artifact@v4
+        with:
+          name: coverage-test-failures
+          path: ${{ runner.temp }}/package

NAMESPACE

@@ -1,7 +1,10 @@
 # Generated by roxygen2: do not edit by hand
 
 export(fit_epifilter)
+export(fit_epifilter_covariates)
+export(simulate_renewal_epidemic)
+exportPattern("^[[:alpha:]]+")
 import(Rcpp)
 import(methods)
 importFrom(rstan,sampling)
-useDynLib(epidp, .registration = TRUE)
+useDynLib(epidp)

R/extra.R

@@ -0,0 +1,6 @@
+#' @useDynLib epidp
+#' @import Rcpp
+#' @import methods
+#' @importFrom rstan sampling
+#' @exportPattern "^[[:alpha:]]+"
+NULL

R/simulation.R

@@ -82,6 +82,7 @@ generate_vector_serial <- function(nt, mean_si, sd_si) {
 #' rt_fun <- function(t) { 1.5 * exp(-0.05 * t) }
 #' simulate_renewal_epidemic(rt_fun, 100, 5, 2, 10)
 #' }
+#' @export
 simulate_renewal_epidemic <- function(Rt_fun, nt, mean_si, sd_si, i_0, X=NULL){
 
   # Input validation
@@ -109,9 +110,9 @@ simulate_renewal_epidemic <- function(Rt_fun, nt, mean_si, sd_si, i_0, X=NULL){
   Rt <- vector(length = nt)
   for(i in seq_along(Rt)) {
     if(is.null(X))
-      Rt[i] = rt_fun(t[i])
+      Rt[i] = Rt_fun(t[i])
     else
-      Rt[i] <- rt_fun(t[i], X[i, ])
+      Rt[i] <- Rt_fun(t[i], X[i, ])
   }
 
   # Total infectiousness and incidence with initial imports

README.Rmd

@@ -0,0 +1,112 @@
+---
+output: github_document
+---
+
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  fig.path = "man/figures/README-",
+  out.width = "100%"
+)
+```
+
+# epidp
+
+<!-- badges: start -->
+[![codecov](https://codecov.io/github/ben18785/epidp/graph/badge.svg?token=STG0INT235)](https://codecov.io/github/ben18785/epidp)
+<!-- badges: end -->
+
+The goal of `epidp` is to allow covariate information to inform estimates of the time-varying reproduction number, $R_t$.
+
+## Installation
+
+You can install the development version of epidp from [GitHub](https://github.com/) with:
+
+``` r
+# install.packages("devtools")
+devtools::install_github("ben18785/epidp")
+```
+
+## Example
+### Step function in $R_t$
+We first generate case data assuming a step function for $R_t$.
+```{r}
+library(epidp)
+library(ggplot2)
+library(dplyr)
+library(magrittr)
+library(purrr)
+library(tidyr)
+
+rt_fun = function(t){
+  if(t <= 60)
+    R = 2
+  else if (t <= 90)
+    R = 0.5
+  else
+    R = 1
+  R
+}
+
+# simulation parameters
+nt <- 200
+mean_si <- 6.5
+sd_si <- 4.03
+i_0 <- 10
+
+# data frame of outputs
+epidemic_df <- simulate_renewal_epidemic(rt_fun, nt, mean_si, sd_si, i_0)
+
+# plot
+transform_factor <- 300
+epidemic_df %>%
+  select(-c(w_dist, lambda_t)) %>%
+  mutate(R_t = R_t * transform_factor) %>% 
+  pivot_longer(c(i_t, R_t)) %>%
+  ggplot(aes(x = t, y = value, colour = name)) +
+  geom_line() +
+  scale_y_continuous(
+    name = "Incidence (i_t)",
+    sec.axis = sec_axis(~ . / transform_factor, name = "Reproduction Number (R_t)")
+  ) +
+  labs(x = "Time") +
+  theme_minimal() +
+  scale_color_brewer("Series", palette = "Dark2")
+```
+
+We now use a Stan version of EpiFilter to estimate the maximum a posteriori estimates
+of $R_t$ and overlay these on top of the actual values. Note, these estimates do
+not have uncertainty associated with them but the benefit of this is that estimation
+is instantaneous. The estimates are close to the actual $R_t$ values after an initial
+period when case counts are low.
+```{r}
+# fit model
+fit <- fit_epifilter(
+  N=length(epidemic_df$i_t),
+  C=epidemic_df$i_t,
+  w=epidemic_df$w_dist,
+  is_sampling=FALSE,
+  as_vector=FALSE
+)
+
+# plot
+R <- fit$par$R
+epidemic_df %>% 
+  mutate(estimated=R) %>% 
+  rename(true=R_t) %>% 
+  select(t, estimated, true) %>% 
+  pivot_longer(c(estimated, true)) %>% 
+  ggplot(aes(x=t, y=value)) +
+  geom_line(aes(colour=name)) +
+  scale_color_brewer("R_t", palette = "Dark2") +
+  ylab("R_t") +
+  xlab("Time") +
+  theme_minimal()
+```
+
+## Contributing guidelines
+We welcome contributions from collaborators. Before doing so, we ask that you read
+our [contributing guidelines](contributing-guidelines.md) section.

README.md

@@ -1,25 +1,57 @@
+
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+
 # epidp
 
-The goal of `epidp` is to allow covariate information to inform estimates of the time-varying reproduction number, $R_t$.
+<!-- badges: start -->
+
+[![codecov](https://codecov.io/github/ben18785/epidp/graph/badge.svg?token=STG0INT235)](https://codecov.io/github/ben18785/epidp)
+<!-- badges: end -->
+
+The goal of `epidp` is to allow covariate information to inform
+estimates of the time-varying reproduction number, $R_t$.
 
 ## Installation
 
-You can install the released version of `epidp` with:
+You can install the development version of epidp from
+[GitHub](https://github.com/) with:
 
 ``` r
-devtools::install_github(ben18785/epidp)
+# install.packages("devtools")
+devtools::install_github("ben18785/epidp")
 ```
 
 ## Example
+
 ### Step function in $R_t$
+
 We first generate case data assuming a step function for $R_t$.
-```{r}
+
+``` r
 library(epidp)
 library(ggplot2)
 library(dplyr)
+#> 
+#> Attaching package: 'dplyr'
+#> The following objects are masked from 'package:stats':
+#> 
+#>     filter, lag
+#> The following objects are masked from 'package:base':
+#> 
+#>     intersect, setdiff, setequal, union
 library(magrittr)
 library(purrr)
+#> 
+#> Attaching package: 'purrr'
+#> The following object is masked from 'package:magrittr':
+#> 
+#>     set_names
 library(tidyr)
+#> 
+#> Attaching package: 'tidyr'
+#> The following object is masked from 'package:magrittr':
+#> 
+#>     extract
 
 rt_fun = function(t){
   if(t <= 60)
@@ -41,25 +73,32 @@ i_0 <- 10
 epidemic_df <- simulate_renewal_epidemic(rt_fun, nt, mean_si, sd_si, i_0)
 
 # plot
+transform_factor <- 300
 epidemic_df %>%
   select(-c(w_dist, lambda_t)) %>%
+  mutate(R_t = R_t * transform_factor) %>% 
   pivot_longer(c(i_t, R_t)) %>%
   ggplot(aes(x = t, y = value, colour = name)) +
   geom_line() +
   scale_y_continuous(
     name = "Incidence (i_t)",
-    sec.axis = sec_axis(~ . / 1000, name = "Reproduction Number (R_t)")
+    sec.axis = sec_axis(~ . / transform_factor, name = "Reproduction Number (R_t)")
   ) +
-  labs(x = "Time", colour = "Variable") +
-  theme_minimal()
+  labs(x = "Time") +
+  theme_minimal() +
+  scale_color_brewer("Series", palette = "Dark2")
 ```
 
-We now use a Stan version of EpiFilter to estimate the maximum a posteriori estimates
-of $R_t$ and overlay these on top of the actual values. Note, these estimates do
-not have uncertainty associated with them but the benefit of this is that estimation
-is instantaneous. The estimates are close to the actual $R_t$ values after an initial
-period when case counts are low.
-```{r}
+<img src="man/figures/README-unnamed-chunk-2-1.png" width="100%" />
+
+We now use a Stan version of EpiFilter to estimate the maximum a
+posteriori estimates of $R_t$ and overlay these on top of the actual
+values. Note, these estimates do not have uncertainty associated with
+them but the benefit of this is that estimation is instantaneous. The
+estimates are close to the actual $R_t$ values after an initial period
+when case counts are low.
+
+``` r
 # fit model
 fit <- fit_epifilter(
   N=length(epidemic_df$i_t),
@@ -79,9 +118,15 @@ epidemic_df %>%
   ggplot(aes(x=t, y=value)) +
   geom_line(aes(colour=name)) +
   scale_color_brewer("R_t", palette = "Dark2") +
-  ylab("R_t")
+  ylab("R_t") +
+  xlab("Time") +
+  theme_minimal()
 ```
 
+<img src="man/figures/README-unnamed-chunk-3-1.png" width="100%" />
+
 ## Contributing guidelines
-We welcome contributions from collaborators. Before doing so, we ask that you read
-our [contributing guidelines](contributing-guidelines.md) section.
+
+We welcome contributions from collaborators. Before doing so, we ask
+that you read our [contributing guidelines](contributing-guidelines.md)
+section.