CRAN v0.2.0

New features

• Distributions are now provided by the distributional package, which is more
  space efficient and allows calculation of distributional statistics including
  the mean(), median(), variance(), quantile(), cdf() and density().
• autoplot.fbl_ts() and autolayer.fbl_ts() now accept the point_forecast
  argument, which is a named list of functions that describe the method used to
  obtain the point forecasts. If multiple are specified, each method will be
  identified using the linetype.
• Added accuracy measures: RMSSE(), pinball_loss(), scaled_pinball_loss().
• Added accessor functions for column names (or metadata) of interest. This
  includes models in a mable (mable_vars()), response variables
  (response_vars()) and distribution variables (distribution_var()).
• Added support for combinations of non-normal forecasts, which produces mean
  point forecasts only.
• Added support for reconciling non-normal forecasts, which produces reconciled
  point forecasts only.

Improvements

• Improved dplyr support. You can now use bind_*() and *_join() operations
  on mables, dables, and fables. More verbs are supported by these extension
  data classes, and so behaviour should work closer to what is expected.
• Progress reporting is now handled by the progressr package. This allows you to
  decide if, when, and how progress is reported. To show progress, wrap your
  code in the progressr::with_progress() function. Progress will no longer be
  displayed automatically during lengthy calculations.
• Improved support for streaming data to models with transformed response
  variables.
• hilo.fbl_ts() now keeps existing columns of a fable.
• forecast() will now return an empty fable instead of erroring when no
  forecasts are requested.
• is_aggregated() now works for non-aggregated data types.
• Documentation improvements.

Breaking changes

• The fable returned by forecast() now stores the distribution in the column
  named the response variable (previously, this was the point forecast). Point
  forecasts are now stored in the .mean column, which can be customised using
  the point_forecast argument.
• The bias_adjust option for forecast() is replaced by point_forecast,
  allowing you to specify which point forecast measures to display (fable/#226).
  This has been done to reduce confusion around the argument's usage,
  disambiguate the returned point forecast's meaning, and also allow users
  to specify which (if any) point forecasts to provide.
• The data coercion functions as_mable, as_dable, and as_fable have been
  changed to accept character vectors for specifying common attributes (such as
  response variables, and distributions).
• The models argument for mable and as_mable has been replaced with model
  for consistency with the lack of plural in key.
• Intervals from multivariate distributions are now returned as data frames of
  hilo intervals. The columns are the response variables. Similar structures
  are returned when computing other distributional statistics like the mean.
• hilo intervals can no longer be unnested as they are now stored more
  efficiently as a vctrs record type. The unpack_hilo() function will continue
  to function as expected, and you can now obtain the components of the interval
  with x$lower, x$upper, and x$level,
• rbind() methods are deprecated in favour of bind_rows()
• The row order of wide to long mable operations (such as accuracy()) has
  changed (due to shift to pivot_longer() from gather()). Model column name
  values are now nested within key values, rather than key values nested in
  model name values.

Bug fixes

• Fixed show_gap option not working when more than one forecast is plotted.
• Fixed autolayer() plotting issues due to inherited aesthetics.
• aggregate_key() no longer drops keys, instead they are kept as .
• Forecast reconciliation now works with historical data that is not temporally
  aligned.
• Fixed forecast() producing forecasts via h when new_data does not
  include a given series (#202).