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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, cat_var_old
and cat_var_new
.master
to main
branch.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.data
and library
calls style.cat2cat
function.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.ml
and 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 data.frame
.occup
and occup_small
datasets have 4 periods now.pkgdown
reference.pkgdown
website.deparse
instead of deparse1
.tinyverse
world, even less dependencies.cat2cat
function, the ml part is assuming that categorical variable is always named “code”.cat2cat
function.randomForest
packages to Suggests, they are delayed loaded now.occup_small
dataset to pass checks in terms of computation time of examples.cat2cat
function - “knn”, “rf”, “lda”.prune_c2c
and cross_c2c
to improve processing of results.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.