miceFast 0.9.1

Bug fixes

• PMM returned predicted values instead of observed values (C++): The pmm model returned predicted \(\hat{y}\) for missing rows instead of the nearest observed \(y\) values. Now it follows Little and Rubin (2002).
• PMM with character/factor variables (R): fill_NA_N() with model = "pmm" and a character dependent variable failed because it attempted as.numeric() on non-numeric strings, producing all NAs.
• Character dependent variable with lm models: fill_NA() and fill_NA_N() with model = "lm_pred", "lm_bayes", or "lm_noise" silently returned all NAs when the dependent variable was character with non-numeric labels (e.g., "apple", "banana").

Documentation

• README: added sequential-chain MI examples (dplyr and data.table) showing how to impute multiple variables and pool with Rubin’s rules.
• Introduction vignette: added full imputation workflow with sequential ordering (impute variables whose predictors are complete first), FCS (chained equations) section with data.table example, and PMM note for the OOP interface.
• MI vignette: expanded Rubin’s rules derivations, added PMM MI example using the OOP interface, expanded “Important caveat” section with OOP and data.table FCS code snippets for non-monotone patterns.
• Documented PMM as a proper MI method throughout vignettes and README.
• Improved prose throughout vignettes and README.

Tests

• Added 20 PMM-specific tests (test-pmm.R): observed-value returns, factor/character support, weighted PMM, grouped data.table, reproducibility, stochasticity.
• Added 31 FCS tests (test-fcs.R): data.table, data.frame, and OOP FCS helpers; joint-missingness handling; MI+pool workflow; comparison with mice (pooled estimates and imputed means).
• Added tests for character dependent variables with non-numeric labels across all models and data types.
• Test suite expanded from 243 to 311 tests.

miceFast 0.9.0

Kota Hattori, thank you for your feedback and for motivating me for this deep update.

New features

• pool() function for combining results from multiply imputed datasets (Rubin’s rules, Barnard-Rubin df adjustment). Works with lm, glm, and other models that support coef() and vcov(). Validated against mice.
• print and summary methods for pooled results.

Bug fixes

• fixed residual variance estimator in lm_noise and lm_bayes stochastic models: divisor changed from n-p-1 to n-p, where p already counts the intercept column supplied by the user. The previous formula over-corrected by one degree of freedom.

Documentation and internals

• new vignette on missing data mechanisms (MCAR/MAR/MNAR) and MI workflows.
• refactored introduction vignette with pool() examples.
• improved README with MI section and benchmark table.
• test suite for pool(), including comparison against mice::pool().
• new weighted regression validation test against lm.wfit().
• refactored C++ source code for clarity.
• fixed typos in error messages and documentation.
• regenerated performance benchmarks on R 4.4.3, macOS M3 Pro.

miceFast 0.8.5

• cran related update, OMP_THREAD_LIMIT.

miceFast 0.8.4

• fixed CRAN Notes.
• style the cpp code.
• VIF() should be more stable.

miceFast 0.8.2

• simplified naive_fill_NA, It is a regular sampling imputation now.
• Fixed dontrun examples.
• replace ggplot2::aes_string with ggplot2::aes, as the former is depreciated.
• regenerate performance benchmarks on R 4.2.1.
• styler over the code.
• improve documentation.

miceFast 0.8.1

• tinyverse world, less dependencies.
• fixed imputations for character variables under linear models.
• speed up the pmm model.
• more tests, higher covr.
• rerun performance tests.

miceFast 0.7.1

• update URL inside README.

miceFast 0.7.0

• improve coverage.
• use drop = FALSE when subsetting the data.frame
• healthy DESCRIPTION file, fix spaces.
• more input validation.

miceFast 0.6.8

• update broken vignette links

miceFast 0.6.6

• solve broken UpSetR::upset reference links

miceFast 0.6.5

• upset_NA based on UpSetR::upset plot function
• compare_imp plot function
• new logo
• remove times argument

miceFast 0.6.2

• R CRAN r-oldrel-windows-ix86+x86_64 problems

miceFast 0.6.1

• lifecycle problems

miceFast 0.6.0

• fill_NA_N has a new model which is pmm - predictive mean matching
• fast PMM - presorting and binary search
• naive_fill_NA - auto function for data.frames - bayes mean and lda
• ridge argument for lm models - adding small disturbance to diag of X’X
• lm_bayes provide more disturbance
• new tests
• codecov

miceFast 0.5.1

• remove old urls form vignettes

miceFast 0.5.0

• providing a more comfortable environment for data.table/dplyr users
• expand vignette and documentation
• updated performance benchmarks
• fix a glitch - e.g. lack of correct warning for a lda model with zero variance variables

miceFast 0.2.1-3

• data.table problem - jump to R 3.5.0
• valgrind - a lot of optimizations - problem with arma::exp and arma::randn
• optimize a lot of code
• methods/functions resistant to glitches

miceFast 0.2.0

• fix imputations with a grouping variable - error if there is precisly one NA at any group
• add data.table to benchmarks - model with a grouping variable
• add R functions (fill_NA_N,fill_NA,VIF) which could be used by a data.table user

miceFast 0.1.0

• add impute_N method - optimized multiple imputations
• add vif method - Variance inflation factors

miceFast 0.0.3

• vignette,readme,description,todo

miceFast 0.0.2

• adjust to solaris
• reference - set a grouping variable by a reference but as a numeric vector - integer vector do not work (randomly lost pointer)