Package check result: OK

Changes to worse in reverse depends:

Package: fabletools
Check: examples
New result: ERROR
  Running examples in ‘fabletools-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: forecast.mdl_df
  > ### Title: Produce forecasts
  > ### Aliases: forecast.mdl_df forecast.mdl_ts
  > 
  > ### ** Examples
  > 
  > ## Don't show: 
  > if (requireNamespace("fable", quietly = TRUE) && requireNamespace("tsibbledata", quietly = TRUE)) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
  + ## End(Don't show)
  + library(fable)
  + library(tsibble)
  + library(tsibbledata)
  + library(dplyr)
  + library(tidyr)
  + 
  + # Forecasting with an ETS(M,Ad,A) model to Australian beer production
  + beer_fc <- aus_production %>%
  +   model(ets = ETS(log(Beer) ~ error("M") + trend("Ad") + season("A"))) %>% 
  +   forecast(h = "3 years")
  + 
  + # Compute 80% and 95% forecast intervals
  + beer_fc %>% 
  +   hilo(level = c(80, 95))
  + 
  + beer_fc %>% 
  +   autoplot(aus_production)
  + 
  + # Forecasting with a seasonal naive and linear model to the monthly 
  + # "Food retailing" turnover for each Australian state/territory.
  + library(dplyr)
  + aus_retail %>% 
  +   filter(Industry == "Food retailing") %>% 
  +   model(
  +     snaive = SNAIVE(Turnover),
  +     ets = TSLM(log(Turnover) ~ trend() + season()),
  +   ) %>% 
  +   forecast(h = "2 years 6 months") %>% 
  +   autoplot(filter(aus_retail, Month >= yearmonth("2000 Jan")), level = 90)
  +   
  + # Forecast GDP with a dynamic regression model on log(GDP) using population and
  + # an automatically chosen ARIMA error structure. Assume that population is fixed
  + # in the future.
  + aus_economy <- global_economy %>% 
  +   filter(Country == "Australia")
  + fit <- aus_economy %>% 
  +   model(lm = ARIMA(log(GDP) ~ Population))
  + 
  + future_aus <- new_data(aus_economy, n = 10) %>% 
  +   mutate(Population = last(aus_economy$Population))
  + 
  + fit %>% 
  +   forecast(new_data = future_aus) %>% 
  +   autoplot(aus_economy)
  + ## Don't show: 
  + }) # examplesIf
  > library(fable)
  > library(tsibble)
  
  Attaching package: ‘tsibble’
  
  The following objects are masked from ‘package:base’:
  
      intersect, setdiff, union
  
  > library(tsibbledata)
  > 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(tidyr)
  > beer_fc <- aus_production %>% model(ets = ETS(log(Beer) ~ error("M") + 
  +     trend("Ad") + season("A"))) %>% forecast(h = "3 years")
  > beer_fc %>% hilo(level = c(80, 95))
  # A tsibble: 12 x 6 [1Q]
  # Key:       .model [1]
     .model Quarter              Beer .mean                  `80%`
     <chr>    <qtr>            <dist> <dbl>                 <hilo>
   1 ets    2010 Q3   t(N(6, 0.0013))  407. [388.3086, 425.8337]80
   2 ets    2010 Q4 t(N(6.2, 0.0014))  483. [459.8714, 506.5468]80
   3 ets    2011 Q1   t(N(6, 0.0014))  419. [398.6325, 439.3894]80
   4 ets    2011 Q2   t(N(6, 0.0015))  384. [365.3574, 403.7254]80
   5 ets    2011 Q3   t(N(6, 0.0019))  405. [382.4128, 427.7091]80
   6 ets    2011 Q4 t(N(6.2, 0.0022))  481. [452.3059, 509.7606]80
   7 ets    2012 Q1   t(N(6, 0.0023))  417. [391.5386, 443.0498]80
   8 ets    2012 Q2 t(N(5.9, 0.0025))  383. [358.4021, 407.8355]80
   9 ets    2012 Q3   t(N(6, 0.0032))  403. [374.7091, 432.7834]80
  10 ets    2012 Q4 t(N(6.2, 0.0036))  479. [442.8408, 516.4800]80
  11 ets    2013 Q1   t(N(6, 0.0039))  416. [383.0362, 449.4648]80
  12 ets    2013 Q2 t(N(5.9, 0.0043))  382. [350.3968, 414.1880]80
  # ℹ 1 more variable: `95%` <hilo>
  > beer_fc %>% autoplot(aus_production)
  > library(dplyr)
  > aus_retail %>% filter(Industry == "Food retailing") %>% model(snaive = SNAIVE(Turnover), 
  +     ets = TSLM(log(Turnover) ~ trend() + season()), ) %>% forecast(h = "2 years 6 months") %>% 
  +     autoplot(filter(aus_retail, Month >= yearmonth("2000 Jan")), level = 90)
  > aus_economy <- global_economy %>% filter(Country == "Australia")
  > fit <- aus_economy %>% model(lm = ARIMA(log(GDP) ~ Population))
  Warning: 1 error encountered for lm
  [1] The `urca` package must be installed to use this functionality. It can be installed with install.packages("urca")
  
  > future_aus <- new_data(aus_economy, n = 10) %>% mutate(Population = last(aus_economy$Population))
  > fit %>% forecast(new_data = future_aus) %>% autoplot(aus_economy)
  Error in if (!is.null(x[["dist"]]) && !monotonic_increasing(x[["transform"]],  : 
    missing value where TRUE/FALSE needed
  Calls: <Anonymous> ... median.dist_default -> quantile -> quantile.dist_transformed
  Execution halted