Running ‘testthat.R’ [54s/60s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > # This file is part of the standard setup for testthat.
  > # It is recommended that you do not modify it.
  > #
  > # Where should you do additional test configuration?
  > # Learn more about the roles of various files in:
  > # * https://r-pkgs.org/tests.html
  > # * https://testthat.r-lib.org/reference/test_package.html#special-files
  > 
  > library(testthat)
  > library(ern)
  
  ern version: 2.0.0 
  
  If not already installed, software JAGS is recommended. 
  (https://sourceforge.net/projects/mcmc-jags/files/)
  
  > 
  > test_check("ern")
  Compiling model graph
     Resolving undeclared variables
     Allocating nodes
  Graph information:
     Observed stochastic nodes: 8
     Unobserved stochastic nodes: 58
     Total graph size: 790
  
  Initializing model
  
  MCMC paramters:
    Number of chains   : 1
    Burn-in iterations : 5
    MCMC iterations    : 5
  Wastewater data smoothed using loess method
  iterations Richardson-Lucy deconvolution:  20
  -----
  The clinical testing data you input is not daily.
  `ern` requires daily data to compute Rt, so it will infer daily reports from your inputs.
  Inference method for daily incidence: `renewal`
  See `prm.daily` and `prm.daily.check` arguments of `estimate_R_cl()` for daily inference options.
  -----
  -----
  Assuming the first observed report (from 2020-03-14)
  is aggregated over 7 previous days
  (second observation's aggregation period).
  This can be changed in `estimate_R_cl()`, using the
  `prm.daily` argument (set a value for `first.agg.period`
  in this parameter list).
  -----
  Running MCMC model to infer daily reports from aggregated reports...
  
  Compiling model graph
     Resolving undeclared variables
     Allocating nodes
  Graph information:
     Observed stochastic nodes: 8
     Unobserved stochastic nodes: 58
     Total graph size: 790
  
  Initializing model
  
  MCMC paramters:
    Number of chains   : 1
    Burn-in iterations : 5
    MCMC iterations    : 5
  Aggregating inferred daily reports back using the original reporting schedule, and calculating relative difference with original reports...
  Filtering out any daily inferred reports associated with inferred aggregates outside of the specified tolerance of 10%...
    Before filtering : 56 daily reports
    After filtering  :  42 daily reports
  Using default config in `EpiEstim::estimate_R()`.
  Deconvolution reporting delays...
  iterations Richardson-Lucy deconvolution:  10
  Deconvolution incubation period...
  iterations Richardson-Lucy deconvolution:  10
  -----
  The clinical testing data you input is not daily.
  `ern` requires daily data to compute Rt, so it will infer daily reports from your inputs.
  Inference method for daily incidence: `renewal`
  See `prm.daily` and `prm.daily.check` arguments of `estimate_R_cl()` for daily inference options.
  -----
  -----
  Assuming the first observed report (from 2020-03-14)
  is aggregated over 7 previous days
  (second observation's aggregation period).
  This can be changed in `estimate_R_cl()`, using the
  `prm.daily` argument (set a value for `first.agg.period`
  in this parameter list).
  -----
  Running MCMC model to infer daily reports from aggregated reports...
  
  Compiling model graph
     Resolving undeclared variables
     Allocating nodes
  Graph information:
     Observed stochastic nodes: 8
     Unobserved stochastic nodes: 58
     Total graph size: 790
  
  Initializing model
  
  MCMC paramters:
    Number of chains   : 2
    Burn-in iterations : 55
    MCMC iterations    : 55
  Aggregating inferred daily reports back using the original reporting schedule, and calculating relative difference with original reports...
  Filtering out any daily inferred reports associated with inferred aggregates outside of the specified tolerance of 10%...
    Before filtering : 56 daily reports
    After filtering  :  42 daily reports
  To reduce the number of observations dropped in filtering,either:
  - adjust MCMC parameters in prm.daily (burn, iter, chains) to improve chances of MCMC convergence,
  - increase tolerance for this check (prm.daily.check$agg.reldiff.tol)
  Using default config in `EpiEstim::estimate_R()`.
  Deconvolution reporting delays...
  iterations Richardson-Lucy deconvolution:  10
  Deconvolution incubation period...
  iterations Richardson-Lucy deconvolution:  10
  Using default config in `EpiEstim::estimate_R()`.
  Deconvolution reporting delays...
  iterations Richardson-Lucy deconvolution:  10
  Deconvolution incubation period...
  iterations Richardson-Lucy deconvolution:  10
  Deconvolution reporting delays...
  iterations Richardson-Lucy deconvolution:  10
  Deconvolution incubation period...
  iterations Richardson-Lucy deconvolution:  10
  Using default config in `EpiEstim::estimate_R()`.
  Deconvolution reporting delays...
  iterations Richardson-Lucy deconvolution:  10
  Deconvolution incubation period...
  iterations Richardson-Lucy deconvolution:  10
  Deconvolution reporting delays...
  iterations Richardson-Lucy deconvolution:  10
  Deconvolution incubation period...
  iterations Richardson-Lucy deconvolution:  10
  iterations Richardson-Lucy deconvolution:  9
  Deconvolution incubation period...
  iterations Richardson-Lucy deconvolution:  9
  Wastewater data smoothed using loess method
  Wastewater data smoothed using loess method
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  iterations Richardson-Lucy deconvolution:  9
  
  ERROR: `si_distr` must be specified in `config.EpiEstim`. ABORTING!
  Wastewater data smoothed using loess method
  Wastewater data smoothed using rollmean method
  [ FAIL 1 | WARN 0 | SKIP 0 | PASS 164 ]
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Failure ('test-get_use_dates.R:64:3'): get_use_dates() works when dates.only = FALSE ──
  `df.new` (`actual`) not identical to `df.expected` (`expected`).
  
  `actual$date` is an S3 object of class <Date>, a double vector
  `expected$date` is an S3 object of class <Date>, an integer vector
  
  [ FAIL 1 | WARN 0 | SKIP 0 | PASS 164 ]
  Error: Test failures
  Execution halted