cat2cat_ml_run function to check the ml models performance before
cat2cat with ml option is run. Now, the ml models are more transparent.
ropensci standards, like CONTRIBUTING file and
testthat version 3.
cat2cat_agg has updated
cat_var argument to two new ones,
freqs_df argument in the
cat2cat function is moved from data to mappings part, it is backward compatible. Now it is consistent with the python cat2cat implementation.
pkgcheck related fixes, like 80 chars per line.
library calls style.
dummy_c2c to be backward compatible.
cat_apply_freq function performance.
ml argument in the
dummy_c2c function is redefined, shorter names for a simpler usage.
cat2cat ml part is using direct
cat_var for target (for an update) dataset now, not the one from the
ml argument list.
cat2cat validation, if the
trans table covers all needed levels.
data argument in the
cat2cat::cat2cat function, two additional arguments each.
prune_c2c scales the weights now, so still sum to one for each subject.
dummy_c2c to add a default
cat2cat columns to a
occup_small datasets have 4 periods now.
cat2cat function, the ml part is assuming that categorical variable is always named “code”.
randomForest packages to Suggests, they are delayed loaded now.
occup_small dataset to pass checks in terms of computation time of examples.