- using R Under development (unstable) (2026-09-08 r90509)
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GNU Fortran (Debian 16.2.0-1) 16.2.0
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* current time: 2026-09-09 14:53:33 UTC
- checking for file ‘fable.bayesRecon/DESCRIPTION’ ... OK
- this is package ‘fable.bayesRecon’ version ‘0.2.0’
- package encoding: UTF-8
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- checking whether package ‘fable.bayesRecon’ can be installed ... OK
See the install log for details.
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- checking examples ... [5s/6s] ERROR
Running examples in ‘fable.bayesRecon-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: bayesRecon_MixCond
> ### Title: Probabilistic reconciliation of mixed hierarchies
> ### Aliases: bayesRecon_MixCond bayesRecon_TDcond
>
> ### ** Examples
>
> library(tsibble)
Attaching package: ‘tsibble’
The following objects are masked from ‘package:base’:
intersect, setdiff, union
> 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(fable)
Loading required package: fabletools
> library(fabletools)
> library(fable.intermittent)
>
>
> # Mixed hierarchy with integer-valued bottom and one upper aggregate, from the `auto` dataset.
> # Forecasts with EMPDISTR for bottom base and ETS for smooth base forecasts.
>
>
> # Helper: split a hierarchy into its upper (aggregated) and bottom (leaf) series so that a
> # different base model can be fitted at each level.
> hier_filter <- function(data, level = c("upper", "bottom")) {
+ level <- match.arg(level)
+ key_cols <- tsibble::key_vars(data)
+ if (level == "upper") {
+ dplyr::filter(data, dplyr::if_any(dplyr::all_of(key_cols), fabletools::is_aggregated))
+ } else {
+ dplyr::filter(data, !dplyr::if_any(dplyr::all_of(key_cols), fabletools::is_aggregated))
+ }
+ }
>
> # Build a small two-level hierarchy from a handful of auto spare-part series.
> # The four series become the bottom level; their sum is the upper aggregate.
> sel <- c("TS1461", "TS2953", "TS333", "TS2710")
> data <- auto |>
+ dplyr::filter(series_id %in% sel) |>
+ fabletools::aggregate_key(series_id, value = sum(value))
>
>
> # Hold out the last 6 months for forecasting.
> train <- data |> dplyr::filter(index < tsibble::yearmonth("2011 Jul"))
>
> # Base forecasts: a smooth (Gaussian) model on the continuous-looking upper aggregate,
> # and the EMPDISTR non-parametric count model on the intermittent bottom series.
> fit_upper <- train |>
+ hier_filter("upper") |>
+ fabletools::model(base = fable::ETS(value))
>
> fit_bottom <- train |>
+ hier_filter("bottom") |>
+ fabletools::model(base = fable.intermittent::EMPDISTR(value))
Error: 'EMPDISTR' is not an exported object from 'namespace:fable.intermittent'
Execution halted
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Running ‘testthat.R’ [74s/93s]
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- DONE
Status: 1 ERROR