Numeric Matrices K-NN and PCA Imputation

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Documentation for package ‘slideimp’ version 1.1.0

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

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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