A Batch Process for High Dimensional Imputation via Chained Random Forests
MIT License
Bot releases are visible (Hide)
Added citation onLoad message: Waggoner, P. D. (2023). A batch process for high dimensional imputation. Computational Statistics, 1-22. doi:10.1007/s00180-023-01325-9
Added mad()
function for evaluation by computing mean absolute differences between imputations and the original data
Added unit testing for basic mad()
functionality
Added column-wise and row-wise NA checks for pre- and post- imputation checking:
check_feature_na()
: find features with (specified amount of) missingnesscheck_row_na()
: find number of and which rows contain any missingnessAdded two new vignettes to work examples using the new functions:
Cleaned up DESCRIPTION
, fixed typos, and other small edits
Published by pdwaggoner over 2 years ago
The latest stable version (v0.1.1) is now available on CRAN.
Minor fixes, edits. Also, includes use of cli
for more informative printing during the hdImpute
process.