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
broomExtrafrom suggested packages, because it was archived on CRAN.
crossmap 0.3.2
New features
- The
map_vec()family of functions gain a.classargument, 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 usegenericsinstead ofbroomExtra.-
broomandbroomExtraare 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 usebroomExtrainstead ofbroomto 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 ignoreNULLinputs. -
future_*()functions now prompt the user to select afutureplan if R is not set up for parallelization.
crossmap 0.2.0
CRAN release: 2020-09-24
New features
- Added
weightsargument 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
NULLorNAfor an unweighted model.
