Package: hdImpute 0.2.1

hdImpute: A Batch Process for High Dimensional Imputation

A correlation-based batch process for fast, accurate imputation for high dimensional missing data problems via chained random forests. See Waggoner (2023) <doi:10.1007/s00180-023-01325-9> for more on 'hdImpute', Stekhoven and Bühlmann (2012) <doi:10.1093/bioinformatics/btr597> for more on 'missForest', and Mayer (2022) <https://github.com/mayer79/missRanger> for more on 'missRanger'.

Authors:Philip Waggoner [aut, cre]

# Install 'hdImpute' in R:

install.packages('hdImpute', repos = c('https://pdwaggoner.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Last updated 21 days agofrom:f26e60f649. Checks:OK: 1 WARNING: 6. Indexed: yes.

Help page	Topics
Find features with (specified amount of) missingness	check_feature_na
Find number of and which rows contain any missingness	check_row_na
High dimensional imputation via batch processed chained random forests Build correlation matrix	feature_cor
Flatten and arrange cor matrix to be df	flatten_mat
Complete hdImpute process: correlation matrix, flatten, rank, create batches, impute, join	hdImpute
Impute batches and return completed data frame	impute_batches
Compute variable-wise mean absolute differences (MAD) between original and imputed dataframes.	mad