In thierrygosselin/assigner: Assignment Analysis with RADseq Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

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.

Life cycle

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.