NEWS.md
In purrrlyr: Tools at the Intersection of 'purrr' and 'dplyr'

purrrlyr 0.0.8

Fixes for CRAN checks.
purrrlyr no longer depends on BH headers.

purrrlyr 0.0.7

Fixed CRAN checks with r-devel.

purrrlyr 0.0.6

Compatibility with dplyr 1.0.

purrrlyr 0.0.5

Fixed protection issues reported by rchk.

purrrlyr 0.0.4

Compatibility with dplyr 0.8.0.
Compatibility with R 3.5.

purrrlyr 0.0.3

Fixed a compilation issue with clang and libc++.

purrlyr 0.0.2

CRAN maintenance release.

purrlyr 0.0.1

All data-frame based mappers have been moved to this package. These functions are not technically deprecated (so you can move to this package as easily as possible), but these functions are unlikely to be changed in the future (i.e. there will be no bug fixes) and are likely to go away in the near future, so we highly recommend updating to new approaches.

Mapping a function to each column of a data frame should now be handled with the colwise mutating and summarising operations in dplyr instead of dmap(). These are the verbs with suffix _all(), _at() and _if(), such as mutate_all() or summarise_if(). Note that this means the output of .f should conform to the requirements of dplyr operations: same length as the input for mutating operations, and length 1 for summarising operations.
Inovking a function row by row with the columns of a data frame as arguments should be done with pmap() followed by dplyr::as_dataframe() instead of map_rows().
Mapping rowwise slices of a data frame with by_row() is deprecated in favour of a combination of tidyverse functions. First use tidyr::nest() to create a list-column containing groupwise data frames. Then use dplyr::mutate() to operate on this list-column. Typically you will want to apply a function on each element (nested data frame) of this list-column with purrr::map().