cv_generator: generate folds for nested cv
dfm_gen: added optional lowercasing parameter
estimator: estimate model and performance based on parameters
feat_select: select features based on textstat_keyness
metric_gen: convert output from estimator to model performance metrics
modelizer: updated for new pipeline
modelizer_old: old model pipeline
out_parser: now correctly exported
dfm_gen: added old junk codes for recoding, and removed deprecated ngrams parameter from dfm function
class_update: removed dfm_words parameter, which is replaced by the force = T parameter in predict(), training/model idf is now applied to unseen data
DESCRIPTION: added quanteda.textmodels as new dependency, since these have been separated from base quanteda 2.0.0 onwards
modelizer: Minor update to feature keyness, using absolute values now to determine the most informative features for a class (so features that are either strongly postively or negatively related to the class)
bulk_writer: Added the 'ver' parameter to include a short version string with each update. Mostly to deal with updates that do not complete successfully on all data
Your Name
Erik de Vries