# Numeric Matrices K-NN and PCA Imputation

## Documentation for package ‘slideimp’ version 1.2.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

col_vars | Calculate Matrix Column Variances
compute_metrics | Compute Prediction Accuracy Metrics
compute_metrics.data.frame | Compute Prediction Accuracy Metrics
compute_metrics.slideimp_tune | Compute Prediction Accuracy Metrics
group_imp | Grouped K-NN or PCA Imputation
knn_imp | K-Nearest Neighbor Imputation for Numeric Matrices
lobpcg_control | LOBPCG Eigensolver Control Options
mat_miss | Column or Row Missing Counts and Proportions
mean_imp_col | Column Mean Imputation
pca_imp | PCA Imputation for Numeric Matrices
prep_groups | Prepare Groups for Imputation
print.slideimp_results | Print a 'slideimp_results' Object
print.slideimp_sim | Print a 'slideimp_sim' Object
print.slideimp_tbl | Print a 'slideimp_tbl' Object
sample_na_loc | Sample Missing-Value Locations with Constraints
sim_mat | Simulate a Matrix with Metadata
slideimp_resolve_group | Resolve a Group Specification to a Data Frame
slideimp_resolve_group.data.frame | Resolve a Group Specification to a Data Frame
slideimp_resolve_group.default | Resolve a Group Specification to a Data Frame
slide_imp | Sliding-Window K-NN or PCA Imputation
tune_imp | Tune Imputation Method Parameters