v0.5

• rMIDAS now includes an automatic setup that prompts the user on whether to automatically set up a Python environment and its dependencies
• Addressed dependency issues and deprecation warnings
• An additional .Rmd example that showcases rMIDAS core functions
• Added a new vignette for running rMIDAS in headless mode, along with updates to the existing vignettes
• Updated the accompanying YAML environment file that works on all major operating systems (including macOS running Apple silicon hardware), enabling easier installation of conda environments (if setup mannually)

Huge thanks to @edvinskis for leading on this update!

v0.4.1

• Disabled Tensorflow deprecation warnings as default (as Python rather than R warning)
• Updated accompanying YAML for easier Conda setup
• Added no-binary pip install to YAML to resolve BLAS issues on Macs

v0.4.0

• python argument in set_python_env renamed to x for clarity
• Minor fixes including remedying bug in complete() function
• Improved documentation

v0.3.0

rMIDAS 0.3

• Minor updates to underlying Python code to mirror MIDASpy v1.2.1
• Added NULL defaults to cat_cols and bin_cols parameters within rMIDAS::convert()
• Overimputation legend now plotted in bottom-right corner of figure
• Minor changes to README

rMIDAS 0.2

• rMIDAS now fully supports both Tensorflow 1.X and 2.X
• Added two vignettes for demonstrating imputation workflow and configuring Python installs/environments
• Streamlined handling of Python configuration and interface with reticulate
• Added a fast parameter to the complete() function, giving users more flexibility on how to handle predicted probabilities for categorical and binary variables
• Added function add_missingness() to spike-in missingness for examples
• Minor changes to README
• Minor changes to DESCRIPTION including title and description fields
• Replaced all instances of cat() with message() for better logging
• Bug fixes related to GitHub issues

rMIDAS 0.1

• First release including all core functionality
• VAE and overimputation diagnostic tests included
• Easy to use pre/post-processing of data
• Multiple imputation wrapper of `glm()' for in-built analysis of completed data