fabletools 0.8.0

New features

• Model combination now uses a joint N-way convolution that correctly accounts
  for the full residual covariance structure across all component models.
• Added sum(), mean(), and weighted.mean() S3 methods for mdl_ts and
  mdl_lst objects, providing explicit N-way model combination with equal,
  uniform, or custom weights respectively.
• All model arithmetic operators (+, -, *, /) now produce a single
  model_combination object with correctly implied weights. Arbitrary nested
  expressions such as ((m1 + m2)/2 + m3)/2 automatically flatten into a
  single joint combination.

Bug fixes

• combination_ensemble() now computes inverse-variance weights using
  response residuals (type = "response") rather than innovation residuals.
  This is particularly important for models where the innovation residual
  variance is not comparable to the variance on the response scale, e.g. for
  multiplicative ETS models (#320).
• Replaced unintuitive behaviour of previous pairwise iterative approach for
  model combination (e.g. (A + B + C)/3 as ((A + B) + C)/3) which used
  pairwise covariances that yield different results for different orders of
  models (#321).
• Fixed computing training accuracy() with global models.

Improvements

• Added with_bottom argument to coherent_smat() for inclusion of identity
  matrix entries for bottom level time series. Setting this to FALSE produces
  the A matrix (aggregated series only).
• Added "bottom" attribute for coherent_smat() for safely identifying the
  index positions of bottom level series via graph coherency structures.

Breaking changes

• The mdl_df class for mable data frames will be renamed to mbl_df (a
  mable data frame). This is to avoid a naming inconsistency with the mdl_ts
  and mdl_lst (for local and global models respectively), and free the class
  name for an eventual mdl_df multi-model class. The mbl_df class has been
  added, and mdl_df will be removed in the next minor release (v0.9.0).