NEWS.md
In slideimp: Numeric Matrices K-NN and PCA Imputation

slideimp 1.0.0

group_imp() enforces stricter data validation. The requested feature subset must be a subset the object's column names which must be a subset of the mapping data.frame. Set allow_unmapped = TRUE to bypass errors when intersections are incomplete.
group_imp() and tune_imp() now error when arguments are supplied that do not apply to the chosen imputation method, rather than silently ignoring them.
knn_imp() now uses a logical tree argument to toggle between Ball tree (TRUE) and brute force (FALSE). KD tree is no longer supported.
knn_imp() and pca_imp() gain more early errors and early exits.
pca_imp() gains the same colmax and post_imp arguments as knn_imp().
prep_groups() (formerly group_features()) is the new name for the grouping function. It now accepts a column name vector instead of a full matrix.
sample_na_loc() (formerly inject_na()) is now exported. The original remains accessible via slideimp:::inject_na() for legacy code.
sim_mat() now returns a matrix in sample-by-column format for immediate compatibility with other package functions. perc_NA is renamed to perc_total_na, and dimensions are now specified via n (rows) and p (columns).
tune_imp() gains a unified method argument that applies to both pca_imp() and knn_imp(), replacing pca_method and knn_method. The rep argument is renamed to n_reps.
tune_imp() results from v0.5.4 are no longer reproducible because internal NA generation now uses sample_na_loc().
The khanmiss1 dataset has been removed.

compute_metrics() now supports data frames with a result list column containing truth and estimate columns, similar to {yardstick}.
group_imp() and prep_groups() automatically look up Illumina manifests using the register-on-load pattern for {slideimp.extra}.
knn_imp() gains max_cache to control the internal cache size (defaults to 4GB).
sim_mat() gains a rho argument to support compound symmetry correlation structures in simulated matrices.
sim_mat() and tune_imp() gain dedicated print methods that provide concise summaries instead of dumping raw data to the console.
slide_imp() gains location, flank, and dry_run arguments for fixed-window imputation, "flank mode" for features surrounding a subset, and pre-computation inspection of window statistics.
tune_imp() gains granular control over NA injection via n_cols, n_rows, num_na, and na_col_subset. Pre-calculated locations can also be passed to na_loc to compare methods using identical NA patterns.

col_vars() and mean_imp_col() have been overhauled to use the faster {RcppArmadillo} backend and now support parallel computation with OpenMP.
Dependencies are streamlined. {tibble} and {purrr} are removed as hard dependencies, {cli} is added for more informative messaging, and {carrier} is added as an explicit dependency.
Documentation is thoroughly overhauled with numerous consistency improvements and bug fixes.
{RhpcBLASctl} is added as a suggested package to allow pinning BLAS cores and avoid thrashing during parallel runs.
group_imp() and tune_imp() prioritize process-level parallelization via {mirai}. knn_imp() supports OpenMP-controlled parallelization via the cores argument when {mirai} daemons are not active.
knn_imp() and pca_imp() use optimized internal Rcpp functions for better performance.

slideimp 0.5.4

CRAN resubmission.
group_features() is added to help with creating the group tibble needed for group_imp().
pca_imp() now allows row.w = "n_miss" to scale row weights by the number of missing values per row.