slider 0.2.0

• New family of very fast specialized sliding functions:

  • slide_sum(), slide_index_sum(): for rolling sums

  • slide_mean(), slide_index_mean(): for rolling averages

  • slide_prod(), slide_index_prod(): for rolling products

  • slide_min(), slide_index_min(): for rolling minimums

  • slide_max(), slide_index_max(): for rolling maximums

  • slide_any(), slide_index_any(): for rolling any

  • slide_all(), slide_index_all(): for rolling all

• The slide_index_*() family now allows .before and .after to be
  functions of 1 argument (the index) that compute the boundaries of the
  sliding window. This can be extremely useful when the default, which computes
  .i - .before and .i + .after, is not applicable or correct for your needs.
  One use case is to set .before = ~.x %m-% months(1) rather than
  .before = months(1) to perform a 1 month rolling window in a way that won't
  generate NA values on invalid dates (like 1 month before 2019-03-31) (#139).

• The slide_index_*() family has undergone some internal changes to make it
  more compatible with custom vctrs classes that could be provided as the
  index (.i), such as the date-time classes in the clock package (#133, #130).

• For the slide_index_*() family, it is now required that .i - .before and
  .i + .after be castable to .i by vctrs::vec_cast(). Similarly, for
  the hop_index_*() family, .starts and .stops must both be castable to
  .i (#132).

• New vignette, vignette("tsibble"), explaining how to transition from tsibble
  to slider (#128).

• vignette("rowwise") has been updated to use cur_data() from dplyr 1.0.0,
  which makes it significantly easier to do rolling operations on data frames
  (like rolling regressions) using slider in a dplyr pipeline.