assigner is maturing, but in order to make the package better, changes are inevitable. Experimental functions will change, argument names will change.
Below an example of recent changes that are all documented in NEWS and changelog.
Missing data imputations: now in grur
The imputation of missing data requires special attention that fall outside the scope of assigner. Consequently, these options are no longer available. For assignment, it’s better to do no imputation then quickly do imputations with defaults.
Inside my package called grur, users can
visualize patterns of missingness associated with
different variables (lanes, chips, sequencers, populations, sample
sites, reads/samples, homozygosity, etc). Several
Map-independent imputations of missing genotypes are
available: Random Forests (on-the-fly-imputations or
predictive modeling), Extreme Gradient Tree Boosting,
Strawman imputations (~ max/mean/mode: the most frequently observed,
non-missing genotypes is used). Imputations can be conducted
overall samples or by
populations/strata/grouping.
radiator::genomic_converter is integrated with the
imputation function of grur.