crossmap 0.4.0
CRAN release: 2023-01-12
- Add
xpluck()
function.-
xpluck()
works likepurrr::pluck()
, but allows you to specify multiple indices at each step, e.g.xpluck(x, 1:2, c("a", "b"))
.
-
- Deprecate
xmap_raw()
andfuture_xmap_raw()
functions.-
purrr::map_raw()
and other*_raw()
functions are deprecated in purrr 1.0.0.
-
crossmap 0.3.3
CRAN release: 2022-08-12
- Update roxygen version to avoid CRAN NOTE.
- Remove
broomExtra
from suggested packages, because it was archived on CRAN.
crossmap 0.3.2
New features
- The
map_vec()
family of functions gain a.class
argument, which coerces each element of the output to the given class.
Enhancements
- The
map_vec()
family of functions can now return vectors with S3 classes in addition to base classes. -
tidy_glance()
(and functions that call it) now usegenerics
instead ofbroomExtra
.-
broom
andbroomExtra
are now Suggested packages.
-
crossmap 0.3.1
New features
-
cross_fit()
gains the argumentclusters
, allowing mapping along cluster specifications for functions that support it, likeestimatr::lm_robust()
. -
cross_fit_robust()
is a wrapper forcross_fit(fn = estimatr::lm_robust)
.
Enhancements
-
tidy_glance()
(and functions that call it) now usebroomExtra
instead ofbroom
to support more model types. - Functions now use
rlang::check_installed()
for suggested packages, giving the user the option to install the package interactively.
crossmap 0.3.0
CRAN release: 2021-04-02
New features
Added
cross_fit_glm()
, which works likecross_fit()
but allows you to also specify a crossing ofglm()
model families.-
Added
tidy_glance()
, which returns a tibble with information from bothbroom::tidy()
andbroom::glance()
.-
tidy_glance()
is now the default tidier incross_fit()
.
-
Added
future_xmap_raw()
andfuture_xwalk()
.
Patches
-
cross_join()
,cross_list()
,cross_tbl()
andcross_df()
now silently ignoreNULL
inputs. -
future_*()
functions now prompt the user to select afuture
plan if R is not set up for parallelization.
crossmap 0.2.0
CRAN release: 2020-09-24
New features
- Added
weights
argument tocross_fit()
.- You can now cross model specifications in three dimensions: formulas, subsets, and weights.
- Weights are specified as a list of column names, or
NULL
orNA
for an unweighted model.