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mixgb 1.5.2

For CRAN Submission

• Significantly faster imputation by optimising data preprocessing and the use of RcppArmadillo
• Visual diagnostic functions have been move to the vismi package
• Documentation

mixgb 1.4.2

Bug fix

• Set drop.unused.levels = FALSE in fac2sparse() to prevent dropping unused levels in factor or ordinal factor.
  • Ensure the feature matrix has the same number of columns to feed in XGBoost

mixgb 1.4.1

Compatibility

• Compatible with both XGBoost >=2.0.0 or CRAN version of XGBoost (=1.7.5.1)

Bug fix

• Correct feature type for numeric and integer variable for initial imputation
• Update save_yhatobs() for Type 1 pmm.

mixgb 1.4.0

• Optimised mixgb() for large datasets:
  • Significantly faster imputation by optimising data preprocessing and the use of RcppArmadillo
  • Enhanced memory efficiency with in-place modifications using data.table
  • Bootstrapping option removed from mixgb(). Users can still use bootstrap in the archived function mixgb0().
  • PMM is now set to NULL by default.

mixgb 1.3.2

Miscellaneous

• Improves package documentation regarding the import of xgb.save() and xgb.load() from XGBoost.

mixgb 1.3.1

Compatibility

• Makes the package compatible with XGBoost 2.0.0 with GPU support:
  • Introduces a new parameter device.
  • Deprecates parameters gpu_id and predictor.
  • Sets tree_method = "hist" by default, aligning with XGBoost 2.0.0.

New Features

• Introduces support for saving imputation models to a local directory through the parameter save.models.folder in mixgb().
  • Models save in JSON format using xgb.save(), a method recommended by XGBoost for future compatibility.
  • When save.models.folder is specified, the return object of mixgb() includes the current imputed datasets, directories for imputation models, and relevant parameters. This object can save using saveRDS() as it doesn’t directly contain the models. Users can later load this object into R and employ impute_new(object, newdata, ...) for new data imputation.

mixgb 1.2.1

Updates

• Includes the URL of the published article.

mixgb 1.2.0

New Features

• Enhances mixgb(data,...) to support datasets with diverse data types:
  • numeric
  • integer
  • factor
  • logical

  Note: Users must manually convert character variables to factors.

mixgb 1.1.0

New Features

• Introduces default_params(), an auxiliary function for mixgb(), to validate the list of XGBoost hyperparameters supplied by the user. It simplifies hyperparameter modifications without requiring explicit specification of all default values.

Bug Fixes

• Addresses issues related to PMM for multiclass variables.
• Updates plot_hist() and plot_bar() to align with changes in ggplot2 3.4.0:
  • Replaces ..density.. with after_stat(density) in plot_hist().
  • Replaces ..prop.. with after_stat(prop) in plot_bar().

mixgb 1.0.2

Minor Changes

• Adjusts examples to use nthread = 2 to comply with CRAN policies.

mixgb 1.0.1

Changes in Default Settings

• Transitions from bootstrapping to subsampling. Subsampling, set at subsample = 0.7, becomes the default method due to identified biases with bootstrapping in certain scenarios.
  • Default for mixgb():
    • Subsampling: subsample = 0.7.
    • No bootstrapping: bootstrap = FALSE.

mixgb 0.1.1

Minor Bug Fixes and Updates

• Resolves a minor issue in the createNA() function.
• Modifies default settings in mixgb():
  • ordinalAsInteger: Changes from TRUE to FALSE.
  • max_depth: Changes from 6 to 3.
  • nrounds: Changes from 50 to 100.
  • bootstrap: Sets to TRUE by default.

mixgb 0.1.0

Initial Release

• First version releases on CRAN.
• Supports both single and multiple imputation.
• Offers customisable settings for bootstrapping and predictive matching.
• Provides visual diagnostics for multiply imputed data.

Notes

• Mac OSX users might face challenges with multithreading in mixgb as xgboost requires OpenMP for multi-core operations. For details, please refer to OpenMP for Mac.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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