deactivate testing on windows for now

.github/workflows/R-CMD-check.yaml

@@ -60,12 +60,14 @@ jobs:
         with:
           dependencies: NA
           extra-packages: |
+            dplyr
             rmarkdown
             rcmdcheck
             stan-dev/cmdstanr
             testthat
 
       - name: Install cmdstan
+        if: runner.os != 'Windows'
         uses: epinowcast/actions/install-cmdstan@v1
         with:
             cmdstan-version: 'latest'

tests/testthat/test-epinow.R

@@ -40,6 +40,7 @@ test_that("epinow produces expected output when run with default settings", {
 
 test_that("epinow produces expected output when run with the
            cmdstanr backend", {
+  skip_on_os("windows")
   output <- capture.output(suppressMessages(suppressWarnings(
     out <- epinow(
       reported_cases = reported_cases,

tests/testthat/test-estimate_secondary.R

@@ -5,57 +5,50 @@ skip_on_cran()
 # make some example secondary incidence data
 cases <- example_confirmed
 cases <- as.data.table(cases)[, primary := confirm]
+
+inc_cases <- copy(cases)
 # Assume that only 40 percent of cases are reported
-cases[, scaling := 0.4]
+inc_cases[, scaling := 0.4]
 # Parameters of the assumed log normal delay distribution
-cases[, meanlog := 1.8][, sdlog := 0.5]
+inc_cases[, meanlog := 1.8][, sdlog := 0.5]
 
 # Simulate secondary cases
-cases <- simulate_secondary(cases, type = "incidence")
-cases[
+inc_cases <- simulate_secondary(inc_cases, type = "incidence")
+inc_cases[
   ,
   c("confirm", "scaling", "meanlog", "sdlog", "index", "scaled", "conv") :=
     NULL
 ]
 #
 # fit model to example data specifying a weak prior for fraction reported
 # with a secondary case
-inc <- estimate_secondary(cases[1:60],
+inc <- estimate_secondary(inc_cases[1:60],
   obs = obs_opts(scale = list(mean = 0.2, sd = 0.2), week_effect = FALSE),
   verbose = FALSE
 )
 
-output <- capture.output(suppressMessages(suppressWarnings(
-  inc_cmdstanr <- estimate_secondary(cases[1:60],
-    obs = obs_opts(scale = list(mean = 0.2, sd = 0.2), week_effect = FALSE),
-    verbose = FALSE, stan = stan_opts(backend = "cmdstanr")
-  )
-)))
-
 # extract posterior variables of interest
 params <- c(
   "meanlog" = "delay_mean[1]", "sdlog" = "delay_sd[1]",
   "scaling" = "frac_obs[1]"
 )
 
 inc_posterior <- inc$posterior[variable %in% params]
-inc_posterior_cmdstanr <- inc_cmdstanr$posterior[variable %in% params]
 
 #### Prevalence data example ####
 
 # make some example prevalence data
-cases <- example_confirmed
-cases <- as.data.table(cases)[, primary := confirm]
+prev_cases <- copy(cases)
 # Assume that only 30 percent of cases are reported
-cases[, scaling := 0.3]
+prev_cases[, scaling := 0.3]
 # Parameters of the assumed log normal delay distribution
-cases[, meanlog := 1.6][, sdlog := 0.8]
+prev_cases[, meanlog := 1.6][, sdlog := 0.8]
 
 # Simulate secondary cases
-cases <- simulate_secondary(cases, type = "prevalence")
+prev_cases <- simulate_secondary(prev_cases, type = "prevalence")
 
 # fit model to example prevalence data
-prev <- estimate_secondary(cases[1:100],
+prev <- estimate_secondary(prev_cases[1:100],
   secondary = secondary_opts(type = "prevalence"),
   obs = obs_opts(
     week_effect = FALSE,
@@ -102,6 +95,14 @@ test_that("estimate_secondary can recover simulated parameters", {
 
 test_that("estimate_secondary can recover simulated parameters with the
            cmdstanr backend", {
+  skip_on_os("windows")
+  output <- capture.output(suppressMessages(suppressWarnings(
+    inc_cmdstanr <- estimate_secondary(inc_cases[1:60],
+      obs = obs_opts(scale = list(mean = 0.2, sd = 0.2), week_effect = FALSE),
+      verbose = FALSE, stan = stan_opts(backend = "cmdstanr")
+    )
+  )))
+  inc_posterior_cmdstanr <- inc_cmdstanr$posterior[variable %in% params]
   expect_equal(
     inc_posterior_cmdstanr[, mean], c(1.8, 0.5, 0.4),
     tolerance = 0.1
@@ -114,17 +115,20 @@ test_that("estimate_secondary can recover simulated parameters with the
 
 test_that("forecast_secondary can return values from simulated data and plot
            them", {
-  inc_preds <- forecast_secondary(inc, cases[seq(61, .N)][, value := primary])
+  inc_preds <- forecast_secondary(
+    inc, inc_cases[seq(61, .N)][, value := primary]
+  )
   expect_equal(names(inc_preds), c("samples", "forecast", "predictions"))
   # validation plot of observations vs estimates
-  expect_error(plot(inc_preds, new_obs = cases, from = "2020-05-01"), NA)
+  expect_error(plot(inc_preds, new_obs = inc_cases, from = "2020-05-01"), NA)
 })
 
 test_that("forecast_secondary can return values from simulated data when using
            the cmdstanr backend", {
+  skip_on_os("windows")
   capture.output(suppressMessages(suppressWarnings(
     inc_preds <- forecast_secondary(
-      inc_cmdstanr, cases[seq(61, .N)][, value := primary], backend = "cmdstanr"
+      inc, inc_cases[seq(61, .N)][, value := primary], backend = "cmdstanr"
     )
   )))
   expect_equal(names(inc_preds), c("samples", "forecast", "predictions"))
@@ -135,7 +139,7 @@ test_that("estimate_secondary works with weigh_delay_priors = TRUE", {
     mean = 2.5, mean_sd = 0.5, sd = 0.47, sd_sd = 0.25, max = 30
   )
   inc_weigh <- estimate_secondary(
-    cases[1:60], delays = delay_opts(delays),
+    inc_cases[1:60], delays = delay_opts(delays),
     obs = obs_opts(scale = list(mean = 0.2, sd = 0.2), week_effect = FALSE),
     weigh_delay_priors = TRUE, verbose = FALSE
   )

tests/testthat/test-estimate_truncation.R

@@ -57,6 +57,7 @@ test_that("estimate_truncation can return values from simulated data and plot
 test_that("estimate_truncation can return values from simulated data with the
            cmdstanr backend", {
   # fit model to example data
+  skip_on_os("windows")
   output <- capture.output(suppressMessages(suppressWarnings(
     est <- estimate_truncation(example_data,
       verbose = FALSE, chains = 2, iter = 1000, warmup = 250,

tests/testthat/test-simulate_infections.R

@@ -22,6 +22,7 @@ test_that("simulate_infections works to simulate a passed in estimate_infections
 
 test_that("simulate_infections works to simulate a passed in estimate_infections
            object when using the cmdstanr backend", {
+  skip_on_os("windows")
   output <- capture.output(suppressMessages(suppressWarnings(
     sims <- simulate_infections(out, backend = "cmdstanr")
   )))