sdc_* functions gain the new argument
fill_id_var. This makes output control easier in specific
cases where you need to check an identifier with many missing values.
See this
discussion for details.options(sdc.info_level = 2), the information on
dominance now prints the dominance, similar to the number of distinct
identifiers.sdc_*() functions now contain
options and settings in a much better structure. Before, it was a bunch
of strings which could be pasted together. Now, it’s a list holding only
the relevant values. The print output remained almost identical (minor
improvements) due to new print methods.as.data.table() in sdc_*() functions is
now conditional so that it’s only called when data is not a
data.table yet. This can save memory and improve
performance.sdc_model() gained the new argument local
(like in source()), which allows new use cases. Thanks to
Pantelis Karapanagiotis for the PR!sdc_model() no longer returns wrong results for
felm models when the id_var is used for
clustering.by argument in sdc_descriptives() and
sdc_extreme() now only accepts character input. This makes
the code more robust and easier to maintain.sdc_model is
simplified (terms instead of dummies and
interactions)sdc_extreme() now return the number of distinct ID’s
(instead of number of observations) used to calculate the extreme
valuessdc_model()sdc_model()sdc_model() now checks if data was
actually used to create model (this only works if
model has a suitable S3 method for
model.frame())id_var now takes the default value of
getOption("sdc.id_var") in all functions, which makes it
possible to use options(sdc.id_var = "id") at the top of a
script and save some typingcheck_dominance() (and
therefore in sdc_descriptives() and
sdc_extreme())check_dominance() now handles negative values correctly
(as abs())NEWS.md file to track changes to the
package.
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