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Changelog

Source: NEWS.md

slideimp 1.0.0

Breaking changes

  • 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.

New features

  • 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.

Minor improvements and fixes

  • 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 release: 2026-01-07

  • 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.

slideimp 0.5.3

  • Initial CRAN submission.